@article {141, title = {Association between Smoking History and Tumor Mutation Burden in Advanced Non-Small Cell Lung Cancer.}, journal = {Cancer Res}, volume = {81}, year = {2021}, month = {2021 May 01}, pages = {2566-2573}, abstract = {

Lung carcinogenesis is a complex and stepwise process involving accumulation of genetic mutations in signaling and oncogenic pathways via interactions with environmental factors and host susceptibility. Tobacco exposure is the leading cause of lung cancer, but its relationship to clinically relevant mutations and the composite tumor mutation burden (TMB) has not been fully elucidated. In this study, we investigated the dose-response relationship in a retrospective observational study of 931 patients treated for advanced-stage non-small cell lung cancer (NSCLC) between April 2013 and February 2020 at the Dana Farber Cancer Institute and Brigham and Women{\textquoteright}s Hospital. Doubling smoking pack-years was associated with increased and less frequent and mutations, whereas doubling smoking-free months was associated with more frequent . In advanced lung adenocarcinoma, doubling smoking pack-years was associated with an increase in TMB, whereas doubling smoking-free months was associated with a decrease in TMB, after controlling for age, gender, and stage. There is a significant dose-response association of smoking history with genetic alterations in cancer-related pathways and TMB in advanced lung adenocarcinoma. SIGNIFICANCE: This study clarifies the relationship between smoking history and clinically relevant mutations in non-small cell lung cancer, revealing the potential of smoking history as a surrogate for tumor mutation burden.

}, issn = {1538-7445}, doi = {10.1158/0008-5472.CAN-20-3991}, author = {Wang, Xinan and Ricciuti, Biagio and Nguyen, Tom and Li, Xihao and Rabin, Michael S and Awad, Mark M and Lin, Xihong and Johnson, Bruce E and Christiani, David C} } @article {138, title = {Comprehensive cell type decomposition of circulating cell-free DNA with CelFiE.}, journal = {Nat Commun}, volume = {12}, year = {2021}, month = {2021 05 11}, pages = {2717}, abstract = {

Circulating cell-free DNA (cfDNA) in the bloodstream originates from dying cells and is a promising noninvasive biomarker for cell death. Here, we propose an algorithm, CelFiE, to accurately estimate the relative abundances of cell types and tissues contributing to cfDNA from epigenetic cfDNA sequencing. In contrast to previous work, CelFiE accommodates low coverage data, does not require CpG site curation, and estimates contributions from multiple unknown cell types that are not available in external reference data. In simulations, CelFiE accurately estimates known and unknown cell type proportions from low coverage and noisy cfDNA mixtures, including from cell types composing less than 1\% of the total mixture. When used in two clinically-relevant situations, CelFiE correctly estimates a large placenta component in pregnant women, and an elevated skeletal muscle component in amyotrophic lateral sclerosis (ALS) patients, consistent with the occurrence of muscle wasting typical in these patients. Together, these results show how CelFiE could be a useful tool for biomarker discovery and monitoring the progression of degenerative disease.

}, keywords = {Adult, Algorithms, Amyotrophic Lateral Sclerosis, B-Lymphocytes, Biomarkers, Case-Control Studies, Cell-Free Nucleic Acids, DNA Methylation, Epigenesis, Genetic, Female, Humans, Macrophages, Male, Monocytes, Muscle, Skeletal, Neutrophils, Organ Specificity, Pregnancy, Pregnancy Trimesters, T-Lymphocytes}, issn = {2041-1723}, doi = {10.1038/s41467-021-22901-x}, author = {Caggiano, Christa and Celona, Barbara and Garton, Fleur and Mefford, Joel and Black, Brian L and Henderson, Robert and Lomen-Hoerth, Catherine and Dahl, Andrew and Zaitlen, Noah} } @article {144, title = {A cross-population atlas of genetic associations for 220 human phenotypes.}, journal = {Nat Genet}, volume = {53}, year = {2021}, month = {2021 10}, pages = {1415-1424}, abstract = {

Current genome-wide association studies do not yet capture sufficient diversity in populations and scope of phenotypes. To expand an atlas of genetic associations in non-European populations, we conducted 220 deep-phenotype genome-wide association studies (diseases, biomarkers and medication usage) in BioBank Japan (n = 179,000), by incorporating past medical history and text-mining of electronic medical records. Meta-analyses with the UK Biobank and FinnGen (n = 628,000) identified ~5,000 new loci, which improved the resolution of the genomic map of human traits. This atlas elucidated the landscape of pleiotropy as represented by the major histocompatibility complex locus, where we conducted HLA fine-mapping. Finally, we performed statistical decomposition of matrices of phenome-wide summary statistics, and identified latent genetic components, which pinpointed responsible variants and biological mechanisms underlying current disease classifications across populations. The decomposed components enabled genetically informed subtyping of similar diseases (for example, allergic diseases). Our study suggests a potential avenue for hypothesis-free re-investigation of human diseases through genetics.

}, keywords = {ABO Blood-Group System, Biological Specimen Banks, Genetic Association Studies, Genetic Loci, Genetic Pleiotropy, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Major Histocompatibility Complex, Meta-Analysis as Topic, Mutation, Phenotype}, issn = {1546-1718}, doi = {10.1038/s41588-021-00931-x}, author = {Sakaue, Saori and Kanai, Masahiro and Tanigawa, Yosuke and Karjalainen, Juha and Kurki, Mitja and Koshiba, Seizo and Narita, Akira and Konuma, Takahiro and Yamamoto, Kenichi and Akiyama, Masato and Ishigaki, Kazuyoshi and Suzuki, Akari and Suzuki, Ken and Obara, Wataru and Yamaji, Ken and Takahashi, Kazuhisa and Asai, Satoshi and Takahashi, Yasuo and Suzuki, Takao and Shinozaki, Nobuaki and Yamaguchi, Hiroki and Minami, Shiro and Murayama, Shigeo and Yoshimori, Kozo and Nagayama, Satoshi and Obata, Daisuke and Higashiyama, Masahiko and Masumoto, Akihide and Koretsune, Yukihiro and Ito, Kaoru and Terao, Chikashi and Yamauchi, Toshimasa and Komuro, Issei and Kadowaki, Takashi and Tamiya, Gen and Yamamoto, Masayuki and Nakamura, Yusuke and Kubo, Michiaki and Murakami, Yoshinori and Yamamoto, Kazuhiko and Kamatani, Yoichiro and Palotie, Aarno and Rivas, Manuel A and Daly, Mark J and Matsuda, Koichi and Okada, Yukinori} } @article {117, title = {A deoxyribonuclease 1-like 3 genetic variant associates with asthma exacerbations.}, journal = {J Allergy Clin Immunol}, volume = {147}, year = {2021}, month = {2021 Mar}, pages = {1095-1097.e10}, issn = {1097-6825}, doi = {10.1016/j.jaci.2020.09.027}, author = {Herrera-Luis, Esther and Lorenzo-Diaz, Fabian and Samedy-Bates, Lesly-Anne and Eng, Celeste and Villar, Jes{\'u}s and Rodriguez-Santana, Jose R and Burchard, Esteban G and Pino-Yanes, Maria} } @article {132, title = {Disentangling selection on genetically correlated polygenic traits via whole-genome genealogies.}, journal = {Am J Hum Genet}, volume = {108}, year = {2021}, month = {2021 02 04}, pages = {219-239}, abstract = {

We present a full-likelihood method to infer polygenic adaptation from DNA sequence variation and GWAS summary statistics to quantify recent transient directional selection acting on a complex trait. Through simulations of polygenic trait architecture evolution and GWASs, we show the method substantially improves power over current methods. We examine the robustness of the method under stratification, uncertainty and bias in marginal effects, uncertainty in the causal SNPs, allelic heterogeneity, negative selection, and low GWAS sample size. The method can quantify selection acting on correlated traits, controlling for pleiotropy even among traits with strong genetic correlation (|r|=80\%) while retaining high power to attribute selection to the causal trait. When the causal trait is excluded from analysis, selection is attributed to its closest proxy. We discuss limitations of the method, cautioning against strongly causal interpretations of the results, and the possibility of undetectable gene-by-environment (GxE) interactions. We apply the method to 56 human polygenic traits, revealing signals of directional selection on pigmentation, life history, glycated hemoglobin (HbA1c), and other traits. We also conduct joint testing of 137 pairs of genetically correlated traits, revealing widespread correlated response acting on these traits (2.6-fold enrichment, p = 1.5~{\texttimes} 10). Signs of selection on some traits previously reported as adaptive (e.g., educational attainment and hair color) are largely attributable to correlated response (p = 2.9~{\texttimes} 10 and 1.7~{\texttimes} 10, respectively). Lastly, our joint test shows antagonistic selection has increased type 2 diabetes risk and decrease HbA1c (p = 1.5~{\texttimes} 10).

}, keywords = {Computer Simulation, Diabetes Mellitus, Type 2, Evolution, Molecular, Gene-Environment Interaction, Genetic Heterogeneity, Genetic Pleiotropy, Genome, Human, Genome-Wide Association Study, Glycated Hemoglobin A, Humans, Models, Genetic, Multifactorial Inheritance, Phenotype, Polymorphism, Single Nucleotide, Sample Size, Selection, Genetic}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2020.12.005}, author = {Stern, Aaron J and Speidel, Leo and Zaitlen, Noah A and Nielsen, Rasmus} } @article {130, title = {Elevated Polygenic Burden for Autism Spectrum Disorder Is Associated With the Broad Autism Phenotype in Mothers of Individuals With Autism Spectrum Disorder.}, journal = {Biol Psychiatry}, volume = {89}, year = {2021}, month = {2021 03 01}, pages = {476-485}, abstract = {

BACKGROUND: Autism spectrum disorder (ASD) is a multifactorial neurodevelopmental disorder that encompasses a complex and heterogeneous set of traits. Subclinical traits that mirror the core features of ASD, referred to as the broad autism phenotype (BAP), have been documented repeatedly in unaffected relatives and are believed to reflect underlying genetic liability to ASD. The BAP may help inform the etiology of ASD by allowing the stratification of families into more phenotypically and etiologically homogeneous subgroups. This study explores polygenic scores related to the BAP.

METHODS: Phenotypic and genotypic information were obtained from 2614 trios from the Simons Simplex Collection. Polygenic scores of ASD (ASD-PGSs) were generated across the sample to determine the shared genetic overlap between the BAP and ASD. Maternal and paternal ASD-PGSs were explored in relation to BAP traits and their child{\textquoteright}s ASD symptomatology.

RESULTS: Maternal pragmatic language was related to child{\textquoteright}s social communicative atypicalities. In fathers, rigid personality was related to increased repetitive behaviors in children. Maternal (but not paternal) ASD-PGSs were related to the pragmatic language and rigid BAP domains.

CONCLUSIONS: Associations emerged between parent and child phenotypes, with more associations emerging in mothers than in fathers. ASD-PGS associations emerged with BAP in mothers only, highlighting the potential for a female protective factor, and implicating the polygenic etiology of ASD-related phenotypes in the BAP.

}, keywords = {Autism Spectrum Disorder, Autistic Disorder, Child, Fathers, Female, Humans, Male, Mothers, Phenotype}, issn = {1873-2402}, doi = {10.1016/j.biopsych.2020.08.029}, author = {Nayar, Kritika and Sealock, Julia M and Maltman, Nell and Bush, Lauren and Cook, Edwin H and Davis, Lea K and Losh, Molly} } @article {114, title = {Genetics of 35 blood and urine biomarkers in the UK Biobank.}, journal = {Nat Genet}, volume = {53}, year = {2021}, month = {2021 02}, pages = {185-194}, abstract = {

Clinical laboratory tests are a critical component of the continuum of care. We evaluate the genetic basis of 35 blood and urine laboratory measurements in the UK Biobank (n = 363,228 individuals). We identify 1,857 loci associated with at least one trait, containing 3,374 fine-mapped associations and additional sets of large-effect (>0.1 s.d.) protein-altering, human leukocyte antigen (HLA) and copy number variant (CNV) associations. Through Mendelian randomization (MR) analysis, we discover 51 causal relationships, including previously known agonistic effects of urate on gout and cystatin C on stroke. Finally, we develop polygenic risk scores (PRSs) for each biomarker and build {\textquoteright}multi-PRS{\textquoteright} models for diseases using 35 PRSs simultaneously, which improved chronic kidney disease, type 2 diabetes, gout and alcoholic cirrhosis genetic risk stratification in an independent dataset (FinnGen; n = 135,500) relative to single-disease PRSs. Together, our results delineate the genetic basis of biomarkers and their causal influences on diseases and improve genetic risk stratification for common diseases.

}, keywords = {Biological Specimen Banks, Biomarkers, Cardiovascular Diseases, Diabetes Mellitus, Type 2, DNA Copy Number Variations, Genetic Pleiotropy, HLA Antigens, Humans, Linkage Disequilibrium, Liver-Specific Organic Anion Transporter 1, Mendelian Randomization Analysis, Polymorphism, Single Nucleotide, Proteins, Renal Insufficiency, Chronic, Serine Endopeptidases, United Kingdom}, issn = {1546-1718}, doi = {10.1038/s41588-020-00757-z}, author = {Sinnott-Armstrong, Nasa and Tanigawa, Yosuke and Amar, David and Mars, Nina and Benner, Christian and Aguirre, Matthew and Venkataraman, Guhan Ram and Wainberg, Michael and Ollila, Hanna M and Kiiskinen, Tuomo and Havulinna, Aki S and Pirruccello, James P and Qian, Junyang and Shcherbina, Anna and Rodriguez, Fatima and Assimes, Themistocles L and Agarwala, Vineeta and Tibshirani, Robert and Hastie, Trevor and Ripatti, Samuli and Pritchard, Jonathan K and Daly, Mark J and Rivas, Manuel A} } @article {98, title = {Genome-wide association study reveals a novel locus for asthma with severe exacerbations in diverse populations.}, journal = {Pediatr Allergy Immunol}, volume = {32}, year = {2021}, month = {2021 Jan}, pages = {106-115}, abstract = {

BACKGROUND: Severe asthma exacerbations are a major cause of asthma morbidity and increased healthcare costs. Several studies have shown racial and ethnic differences in asthma exacerbation rates. We aimed to identify genetic variants associated with severe exacerbations in two high-risk populations for asthma.

METHODS: A genome-wide association study of asthma in children and youth with severe exacerbations was performed in 1283 exacerbators and 2027 controls without asthma of Latino ancestry. Independent suggestive variants (P <= 5 {\texttimes} 10 ) were selected for replication in 448 African Americans exacerbators and 595 controls. Case-only analyses were performed comparing the exacerbators with additional 898 Latinos and 524 African Americans asthma patients without exacerbations, while adjusting by treatment category as a proxy of asthma severity. We analyzed the functionality of associated variants with in silico methods and by correlating genotypes with methylation levels in whole blood in a subset of 473 Latinos.

RESULTS: We identified two genome-wide significant associations for susceptibility to asthma with severe exacerbations, including a novel locus located at chromosome 2p21 (rs4952375, odds ratio = 1.39, P = 3.8 {\texttimes} 10 ), which was also associated with asthma exacerbations in a case-only analysis (odds ratio = 1.25, P = 1.95 {\texttimes} 10 ). This polymorphism is an expression quantitative trait locus of the long intergenic non-protein coding RNA 1913 (LINC01913) in lung tissues (P = 1.3 {\texttimes} 10 ) and influences methylation levels of the protein kinase domain-containing cytoplasmic (PKDCC) gene in whole-blood cells (P = 9.8 {\texttimes} 10 ).

CONCLUSION: We identified a novel susceptibility locus for severe asthma exacerbations in Hispanic/Latino and African American youths with functional effects in gene expression and methylation status of neighboring genes.

}, issn = {1399-3038}, doi = {10.1111/pai.13337}, author = {Herrera-Luis, Esther and Espuela-Ortiz, Antonio and Lorenzo-Diaz, Fabian and Keys, Kevin L and Mak, Angel C Y and Eng, Celeste and Huntsman, Scott and Villar, Jes{\'u}s and Rodriguez-Santana, Jose R and Burchard, Esteban G and Pino-Yanes, Maria} } @article {131, title = {Graphical analysis for phenome-wide causal discovery in genotyped population-scale biobanks.}, journal = {Nat Commun}, volume = {12}, year = {2021}, month = {2021 01 13}, pages = {350}, abstract = {

Causal inference via Mendelian randomization requires making strong assumptions about horizontal pleiotropy, where genetic instruments are connected to the outcome not only through the exposure. Here, we present causal Graphical Analysis Using Genetics (cGAUGE), a pipeline that overcomes these limitations using instrument filters with provable properties. This is achievable by identifying conditional independencies while examining multiple traits. cGAUGE also uses ExSep (Exposure-based Separation), a novel test for the existence of causal pathways that does not require selecting instruments. In simulated data we illustrate how cGAUGE can reduce the empirical false discovery rate by up to 30\%, while retaining the majority of true discoveries. On 96 complex traits from 337,198 subjects from the UK Biobank, our results cover expected causal links and many new ones that were previously suggested by correlation-based observational studies. Notably, we identify multiple risk factors for cardiovascular disease, including red blood cell distribution width.

}, keywords = {Biological Specimen Banks, Cardiovascular Diseases, Causality, Computer Simulation, Gene Regulatory Networks, Genetic Pleiotropy, Genetic Variation, Genome-Wide Association Study, Genotype, Humans, Mendelian Randomization Analysis, Models, Theoretical, Multifactorial Inheritance, Phenotype, Risk Factors}, issn = {2041-1723}, doi = {10.1038/s41467-020-20516-2}, author = {Amar, David and Sinnott-Armstrong, Nasa and Ashley, Euan A and Rivas, Manuel A} } @article {139, title = {GWAS of three molecular traits highlights core genes and pathways alongside a highly polygenic background.}, journal = {Elife}, volume = {10}, year = {2021}, month = {2021 Feb 15}, abstract = {

Genome-wide association studies (GWAS) have been used to study the genetic basis of a wide variety of complex diseases and other traits. We describe UK Biobank GWAS results for three molecular traits-urate, IGF-1, and testosterone-with better-understood biology than most other complex traits. We find that many of the most significant hits are readily interpretable. We observe huge enrichment of associations near genes involved in the relevant biosynthesis, transport, or signaling pathways. We show how GWAS data illuminate the biology of each trait, including differences in testosterone regulation between females and males. At the same time, even these molecular traits are highly polygenic, with many thousands of variants spread across the genome contributing to trait variance. In summary, for these three molecular traits we identify strong enrichment of signal in putative core gene sets, even while most of the SNP-based heritability is driven by a massively polygenic background.

}, issn = {2050-084X}, doi = {10.7554/eLife.58615}, author = {Sinnott-Armstrong, Nasa and Naqvi, Sahin and Rivas, Manuel and Pritchard, Jonathan K} } @article {142, title = {Identification of rare and common regulatory variants in pluripotent cells using population-scale transcriptomics.}, journal = {Nat Genet}, volume = {53}, year = {2021}, month = {2021 03}, pages = {313-321}, abstract = {

Induced pluripotent stem cells (iPSCs) are an established cellular system to study the impact of genetic variants in derived cell types and developmental contexts. However, in their pluripotent state, the disease impact of genetic variants is less well known. Here, we integrate data from 1,367 human iPSC lines to comprehensively map common and rare regulatory variants in human pluripotent cells. Using this population-scale resource, we report hundreds of new colocalization events for human traits specific to iPSCs, and find increased power to identify rare regulatory variants compared with somatic tissues. Finally, we demonstrate how iPSCs enable the identification of causal genes for rare diseases.

}, keywords = {Bardet-Biedl Syndrome, Calcium Channels, Cell Line, Cerebellar Ataxia, DNA Methylation, Gene Expression, Genetic Variation, Humans, Induced Pluripotent Stem Cells, Polymorphism, Single Nucleotide, Proteins, Quantitative Trait Loci, Rare Diseases, Regulatory Sequences, Nucleic Acid, Sequence Analysis, RNA, Whole Genome Sequencing}, issn = {1546-1718}, doi = {10.1038/s41588-021-00800-7}, author = {Bonder, Marc Jan and Smail, Craig and Gloudemans, Michael J and Fr{\'e}sard, Laure and Jakubosky, David and D{\textquoteright}Antonio, Matteo and Li, Xin and Ferraro, Nicole M and Carcamo-Orive, Ivan and Mirauta, Bogdan and Seaton, Daniel D and Cai, Na and Vakili, Dara and Horta, Danilo and Zhao, Chunli and Zastrow, Diane B and Bonner, Devon E and Wheeler, Matthew T and Kilpinen, Helena and Knowles, Joshua W and Smith, Erin N and Frazer, Kelly A and Montgomery, Stephen B and Stegle, Oliver} } @article {136, title = {Incorporating European GWAS findings improve polygenic risk prediction accuracy of breast cancer among East Asians.}, journal = {Genet Epidemiol}, year = {2021}, month = {2021 Mar 19}, abstract = {

Previous genome-wide association studies (GWASs) have been largely focused on European (EUR) populations. However, polygenic risk scores (PRSs) derived from EUR have been shown to perform worse in non-EURs compared with EURs. In this study, we aim to improve PRS prediction in East Asians (EASs). We introduce a rescaled meta-analysis framework to combine both EUR (N = 122,175) and EAS (N = 30,801) GWAS summary statistics. To improve PRS prediction in EASs, we use a scaling factor to up-weight the EAS data, such that the resulting effect size estimates are more relevant to EASs. We then derive PRSs for EAS from the rescaled meta-analysis results of EAS and EUR data. Evaluated in an independent EAS validation data set, this approach increases the prediction liability-adjusted Nagelkerke{\textquoteright}s pseudo R by 40\%, 41\%, and 5\%, respectively, compared with PRSs derived from an EAS GWAS only, EUR GWAS only, and conventional fixed-effects meta-analysis of EAS and EUR data. The PRS derived from the rescaled meta-analysis approach achieved an area under the receiver operating characteristic curve (AUC)~of 0.6059, higher than AUC = 0.5782, 0.5809, 0.6008 for EAS, EUR, and conventional meta-analysis of EAS and EUR. We further compare PRSs constructed by single-nucleotide polymorphisms that have different linkage disequilibrium (LD) scores and minor allele frequencies (MAFs) between EUR and EAS, and observe that lower LD scores or MAF in EAS correspond to poorer PRS performance (AUC = 0.5677,~0.5530, respectively) than higher LD scores or MAF (AUC = 0.589, 0.5993, respectively). We finally build a PRS stratified by LD score differences in EUR and EAS using rescaled meta-analysis, and obtain an AUC of 0.6096, with improvement over other strategies investigated.

}, issn = {1098-2272}, doi = {10.1002/gepi.22382}, author = {Ji, Ying and Long, Jirong and Kweon, Sun-Seog and Kang, Daehee and Kubo, Michiaki and Park, Boyoung and Shu, Xiao-Ou and Zheng, Wei and Tao, Ran and Li, Bingshan} } @article {129, title = {Integration of multiomic annotation data to prioritize and characterize inflammation and immune-related risk variants in squamous cell lung cancer.}, journal = {Genet Epidemiol}, volume = {45}, year = {2021}, month = {2021 Feb}, pages = {99-114}, abstract = {

Clinical trial results have recently demonstrated that inhibiting inflammation by targeting the interleukin-1β pathway can offer a significant reduction in lung cancer incidence and mortality, highlighting a pressing and unmet need to understand the benefits of inflammation-focused lung cancer therapies at the genetic level. While numerous genome-wide association studies (GWAS) have explored the genetic etiology of lung cancer, there remains a large gap between the type of information that may be gleaned from an association study and the depth of understanding necessary to explain and drive translational findings. Thus, in this study we jointly model and integrate extensive multiomics data sources, utilizing a total of 40 genome-wide functional annotations that augment previously published results from the International Lung Cancer Consortium (ILCCO) GWAS, to prioritize and characterize single nucleotide polymorphisms (SNPs) that increase risk of squamous cell lung cancer through the inflammatory and immune responses. Our work bridges the gap between correlative analysis and translational follow-up research, refining GWAS association measures in an interpretable and systematic manner. In particular, reanalysis of the ILCCO data highlights the impact of highly associated SNPs from nuclear factor-κB signaling pathway genes as well as major histocompatibility complex mediated variation in immune responses. One consequence of prioritizing likely functional SNPs is the pruning of variants that might be selected for follow-up work by over an order of magnitude, from potentially tens of thousands to hundreds. The strategies we introduce provide informative and interpretable approaches for incorporating extensive genome-wide annotation data in analysis of genetic association studies.

}, issn = {1098-2272}, doi = {10.1002/gepi.22358}, author = {Sun, Ryan and Xu, Miao and Li, Xihao and Gaynor, Sheila and Zhou, Hufeng and Li, Zilin and Boss{\'e}, Yohan and Lam, Stephen and Tsao, Ming-Sound and Tardon, Adonina and Chen, Chu and Doherty, Jennifer and Goodman, Gary and Bojesen, Stig E and Landi, Maria T and Johansson, Mattias and Field, John K and Bickeb{\"o}ller, Heike and Wichmann, H-Erich and Risch, Angela and Rennert, Gadi and Arnold, Suzanne and Wu, Xifeng and Melander, Olle and Brunnstr{\"o}m, Hans and Le Marchand, Loic and Liu, Geoffrey and Andrew, Angeline and Duell, Eric and Kiemeney, Lambertus A and Shen, Hongbing and Haugen, Aage and Johansson, Mikael and Grankvist, Kjell and Caporaso, Neil and Woll, Penella and Dawn Teare, M and Scelo, Ghislaine and Hong, Yun-Chul and Yuan, Jian-Min and Lazarus, Philip and Schabath, Matthew B and Aldrich, Melinda C and Albanes, Demetrios and Mak, Raymond and Barbie, David and Brennan, Paul and Hung, Rayjean J and Amos, Christopher I and Christiani, David C and Lin, Xihong} } @article {92, title = {Integrative genomic analysis in African American children with asthma finds three novel loci associated with lung function.}, journal = {Genet Epidemiol}, volume = {45}, year = {2021}, month = {2021 Mar}, pages = {190-208}, abstract = {

Bronchodilator (BD) drugs are commonly prescribed for treatment and management of obstructive lung function present with diseases such as asthma. Administration of BD medication can partially or fully restore lung function as measured by pulmonary function tests. The genetics of baseline lung function measures taken before BD medication have been extensively studied, and the genetics of the BD response itself have received some attention. However, few studies have focused on the genetics of post-BD lung function. To address this gap, we analyzed lung function phenotypes in 1103 subjects from the Study of African Americans, Asthma, Genes, and Environment, a pediatric asthma case-control cohort, using an integrative genomic analysis approach that combined genotype, locus-specific genetic ancestry, and functional annotation information. We integrated genome-wide association study (GWAS) results with an admixture mapping scan of three pulmonary function tests (forced expiratory volume in 1 s [FEV ], forced vital capacity [FVC], and FEV /FVC) taken before and after albuterol BD administration on the same subjects, yielding six traits. We identified 18 GWAS loci, and five additional loci from admixture mapping, spanning several known and novel lung function candidate genes. Most loci identified via admixture mapping exhibited wide variation in minor allele frequency across genotyped global populations. Functional fine-mapping revealed an enrichment of epigenetic annotations from peripheral blood mononuclear cells, fetal lung tissue, and lung fibroblasts. Our results point to three novel potential genetic drivers of pre- and post-BD lung function: ADAMTS1, RAD54B, and EGLN3.

}, issn = {1098-2272}, doi = {10.1002/gepi.22365}, author = {Goddard, Pag{\'e} C and Keys, Kevin L and Mak, Angel C Y and Lee, Eunice Y and Liu, Amy K and Samedy-Bates, Lesly-Anne and Risse-Adams, Oona and Contreras, Mar{\'\i}a G and Elhawary, Jennifer R and Hu, Donglei and Huntsman, Scott and Oh, Sam S and Salazar, Sandra and Eng, Celeste and Himes, Blanca E and White, Marquitta J and Burchard, Esteban G} } @article {140, title = {Native American Ancestry and Air Pollution Interact to Impact Bronchodilator Response in Puerto Rican Children with Asthma.}, journal = {Ethn Dis}, volume = {31}, year = {2021}, month = {2021 Winter}, pages = {77-88}, abstract = {

Objective: Asthma is the most common chronic disease in children. Short-acting bronchodilator medications are the most commonly prescribed asthma treatment worldwide, regardless of disease severity. Puerto Rican children display the highest asthma morbidity and mortality of any US population. Alarmingly, Puerto Rican children with asthma display poor bronchodilator drug response (BDR). Reduced BDR may explain, in part, the increased asthma morbidity and mortality observed in Puerto Rican children with asthma. Gene-environment interactions may explain a portion of the heritability of BDR. We aimed to identify gene-environment interactions associated with BDR in Puerto Rican children with asthma.

Setting: Genetic, environmental, and psycho-social data from the Genes-environments and Admixture in Latino Americans (GALA II) case-control study.

Participants: Our discovery dataset consisted of 658 Puerto Rican children with asthma; our replication dataset consisted of 514 Mexican American children with asthma.

Main Outcome Measures: We assessed the association of pairwise interaction models with BDR using ViSEN (Visualization of Statistical Epistasis Networks).

Results: We identified a non-linear interaction between Native American genetic ancestry and air pollution significantly associated with BDR in Puerto Rican children with asthma. This interaction was robust to adjustment for age and sex but was not significantly associated with BDR in our replication population.

Conclusions: Decreased Native American ancestry coupled with increased air pollution exposure was associated with increased BDR in Puerto Rican children with asthma. Our study acknowledges BDR{\textquoteright}s phenotypic complexity, and emphasizes the importance of integrating social, environmental, and biological data to further our understanding of complex disease.

}, issn = {1945-0826}, doi = {10.18865/ed.31.1.77}, author = {Contreras, Mar{\'\i}a G and Keys, Kevin and Maga{\~n}a, Joaquin and Goddard, Pag{\'e} C and Risse-Adams, Oona and Zeiger, Andrew M and Mak, Angel C Y and Samedy-Bates, Lesly-Anne and Neophytou, Andreas M and Lee, Eunice and Thakur, Neeta and Elhawary, Jennifer R and Hu, Donglei and Huntsman, Scott and Eng, Celeste and Hu, Ting and Burchard, Esteban G and White, Marquitta J} } @article {135, title = {Polygenic risk modeling with latent trait-related genetic components.}, journal = {Eur J Hum Genet}, year = {2021}, month = {2021 Feb 08}, abstract = {

Polygenic risk models have led to significant advances in understanding complex diseases and their clinical presentation. While polygenic risk scores (PRS) can effectively predict outcomes, they do not generally account for disease subtypes or pathways which underlie within-trait diversity. Here, we introduce a latent factor model of genetic risk based on components from Decomposition of Genetic Associations (DeGAs), which we call the DeGAs polygenic risk score (dPRS). We compute DeGAs using genetic associations for 977 traits and find that dPRS performs comparably to standard PRS while offering greater interpretability. We show how to decompose an individual{\textquoteright}s genetic risk for a trait across DeGAs components, with examples for body mass index (BMI) and myocardial infarction (heart attack) in 337,151 white British individuals in the UK Biobank, with replication in a further set of 25,486 non-British white individuals. We find that BMI polygenic risk factorizes into components related to fat-free mass, fat mass, and overall health indicators like physical activity. Most individuals with high dPRS for BMI have strong contributions from both a fat-mass component and a fat-free mass component, whereas a few "outlier" individuals have strong contributions from only one of the two components. Overall, our method enables fine-scale interpretation of the drivers of genetic risk for complex traits.

}, issn = {1476-5438}, doi = {10.1038/s41431-021-00813-0}, author = {Aguirre, Matthew and Tanigawa, Yosuke and Venkataraman, Guhan Ram and Tibshirani, Rob and Hastie, Trevor and Rivas, Manuel A} } @article {145, title = {Population sequencing data reveal a compendium of mutational processes in the human germ line.}, journal = {Science}, volume = {373}, year = {2021}, month = {2021 08 27}, pages = {1030-1035}, abstract = {

Biological mechanisms underlying human germline mutations remain largely unknown. We statistically decompose variation in the rate and spectra of mutations along the genome using volume-regularized nonnegative matrix factorization. The analysis of a sequencing dataset (TOPMed) reveals nine processes that explain the variation in mutation properties between loci. We provide a biological interpretation for seven of these processes. We associate one process with bulky DNA lesions that are resolved asymmetrically with respect to transcription and replication. Two processes track direction of replication fork and replication timing, respectively. We identify a mutagenic effect of active demethylation primarily acting in regulatory regions and a mutagenic effect of long interspersed nuclear elements. We localize a mutagenic process specific to oocytes from population sequencing data. This process appears transcriptionally asymmetric.

}, keywords = {Algorithms, CpG Islands, DNA Damage, DNA Demethylation, DNA Mutational Analysis, DNA Replication, Genetic Variation, Genome, Human, Germ Cells, Germ-Line Mutation, Humans, Long Interspersed Nucleotide Elements, Mutagenesis, Oocytes, Transcription, Genetic}, issn = {1095-9203}, doi = {10.1126/science.aba7408}, author = {Seplyarskiy, Vladimir B and Soldatov, Ruslan A and Koch, Evan and McGinty, Ryan J and Goldmann, Jakob M and Hernandez, Ryan D and Barnes, Kathleen and Correa, Adolfo and Burchard, Esteban G and Ellinor, Patrick T and McGarvey, Stephen T and Mitchell, Braxton D and Vasan, Ramachandran S and Redline, Susan and Silverman, Edwin and Weiss, Scott T and Arnett, Donna K and Blangero, John and Boerwinkle, Eric and He, Jiang and Montgomery, Courtney and Rao, D C and Rotter, Jerome I and Taylor, Kent D and Brody, Jennifer A and Chen, Yii-Der Ida and de Las Fuentes, Lisa and Hwu, Chii-Min and Rich, Stephen S and Manichaikul, Ani W and Mychaleckyj, Josyf C and Palmer, Nicholette D and Smith, Jennifer A and Kardia, Sharon L R and Peyser, Patricia A and Bielak, Lawrence F and O{\textquoteright}Connor, Timothy D and Emery, Leslie S and Gilissen, Christian and Wong, Wendy S W and Kharchenko, Peter V and Sunyaev, Shamil} } @article {143, title = {Population-scale tissue transcriptomics maps long non-coding RNAs to complex disease.}, journal = {Cell}, volume = {184}, year = {2021}, month = {2021 05 13}, pages = {2633-2648.e19}, abstract = {

Long non-coding RNA (lncRNA) genes have well-established and important impacts on molecular and cellular functions. However, among the thousands of lncRNA genes, it is still a major challenge to identify the subset with disease or trait relevance. To systematically characterize these lncRNA genes, we used Genotype Tissue Expression (GTEx) project v8 genetic and multi-tissue transcriptomic data to profile the expression, genetic regulation, cellular contexts, and trait associations of 14,100 lncRNA genes across 49 tissues for 101 distinct complex genetic traits. Using these approaches, we identified 1,432 lncRNA gene-trait associations, 800 of which were not explained by stronger effects of neighboring protein-coding genes. This included associations between lncRNA quantitative trait loci and inflammatory bowel disease, type 1 and type 2 diabetes, and coronary artery disease, as well as rare variant associations to body mass index.

}, keywords = {Coronary Artery Disease, Diabetes Mellitus, Type 1, Diabetes Mellitus, Type 2, Disease, Gene Expression Profiling, Genetic Variation, Humans, Inflammatory Bowel Diseases, Multifactorial Inheritance, Organ Specificity, Population, Quantitative Trait Loci, RNA, Long Noncoding, Transcriptome}, issn = {1097-4172}, doi = {10.1016/j.cell.2021.03.050}, author = {de Goede, Olivia M and Nachun, Daniel C and Ferraro, Nicole M and Gloudemans, Michael J and Rao, Abhiram S and Smail, Craig and Eulalio, Tiffany Y and Aguet, Francois and Ng, Bernard and Xu, Jishu and Barbeira, Alvaro N and Castel, Stephane E and Kim-Hellmuth, Sarah and Park, YoSon and Scott, Alexandra J and Strober, Benjamin J and Brown, Christopher D and Wen, Xiaoquan and Hall, Ira M and Battle, Alexis and Lappalainen, Tuuli and Im, Hae Kyung and Ardlie, Kristin G and Mostafavi, Sara and Quertermous, Thomas and Kirkegaard, Karla and Montgomery, Stephen B} } @article {133, title = {RAFFI: Accurate and fast familial relationship inference in large scale biobank studies using RaPID.}, journal = {PLoS Genet}, volume = {17}, year = {2021}, month = {2021 01}, pages = {e1009315}, abstract = {

Inference of relationships from whole-genome genetic data of a cohort is a crucial prerequisite for genome-wide association studies. Typically, relationships are inferred by computing the kinship coefficients (ϕ) and the genome-wide probability of zero IBD sharing (π0) among all pairs of individuals. Current leading methods are based on pairwise comparisons, which may not scale up to very large cohorts (e.g., sample size >1 million). Here, we propose an efficient relationship inference method, RAFFI. RAFFI leverages the efficient RaPID method to call IBD segments first, then estimate the ϕ and π0 from detected IBD segments. This inference is achieved by a data-driven approach that adjusts the estimation based on phasing quality and genotyping quality. Using simulations, we showed that RAFFI is robust against phasing/genotyping errors, admix events, and varying marker densities, and achieves higher accuracy compared to KING, the current leading method, especially for more distant relatives. When applied to the phased UK Biobank data with ~500K individuals, RAFFI is approximately 18 times faster than KING. We expect RAFFI will offer fast and accurate relatedness inference for even larger cohorts.

}, keywords = {Biological Specimen Banks, Genome, Human, Genome-Wide Association Study, Genotyping Techniques, Haplotypes, Humans, Models, Genetic, Pedigree, Polymorphism, Single Nucleotide}, issn = {1553-7404}, doi = {10.1371/journal.pgen.1009315}, author = {Naseri, Ardalan and Shi, Junjie and Lin, Xihong and Zhang, Shaojie and Zhi, Degui} } @article {127, title = {Rapid response to the alpha-1 adrenergic agent phenylephrine in the perioperative period is impacted by genomics and ancestry.}, journal = {Pharmacogenomics J}, volume = {21}, year = {2021}, month = {2021 Apr}, pages = {174-189}, abstract = {

The emergence of genomic data in biobanks and health systems offers new ways to derive medically important phenotypes, including acute phenotypes occurring during inpatient clinical care. Here we study the genetic underpinnings of the rapid response to phenylephrine, an α1-adrenergic receptor agonist commonly used to treat hypotension during anesthesia and surgery. We quantified this response by extracting blood pressure (BP) measurements 5 min before and after the administration of phenylephrine. Based on this derived phenotype, we show that systematic differences exist between self-reported ancestry groups: European-Americans (EA; n = 1387) have a significantly higher systolic response to phenylephrine than African-Americans (AA; n = 1217) and Hispanic/Latinos (HA; n = 1713) (31.3\% increase, p value < 6e-08 and 22.9\% increase, p value < 5e-05 respectively), after adjusting for genetic ancestry, demographics, and relevant clinical covariates. We performed a genome-wide association study to investigate genetic factors underlying individual differences in this derived phenotype. We discovered genome-wide significant association signals in loci and genes previously associated with BP measured in ambulatory settings, and a general enrichment of association in these genes. Finally, we discovered two low frequency variants, present at ~1\% in EAs and AAs, respectively, where patients carrying one copy of these variants show no phenylephrine response. This work demonstrates our ability to derive a quantitative phenotype suited for comparative statistics and genome-wide association studies from dense clinical and physiological measures captured for managing patients during surgery. We identify genetic variants underlying non response to phenylephrine, with implications for preemptive pharmacogenomic screening to improve safety during surgery.

}, issn = {1473-1150}, doi = {10.1038/s41397-020-00194-5}, author = {Wenric, Stephane and Jeff, Janina M and Joseph, Thomas and Yee, Muh-Ching and Belbin, Gillian M and Owusu Obeng, Aniwaa and Ellis, Stephen B and Bottinger, Erwin P and Gottesman, Omri and Levin, Matthew A and Kenny, Eimear E} } @article {134, title = {Regulatory genomic circuitry of human disease loci by integrative epigenomics.}, journal = {Nature}, volume = {590}, year = {2021}, month = {2021 02}, pages = {300-307}, abstract = {

Annotating the molecular basis of human disease remains an unsolved challenge, as 93\% of disease loci are non-coding and gene-regulatory annotations are highly incomplete. Here we present EpiMap, a compendium comprising 10,000~epigenomic maps across 800~samples, which we used to define chromatin states, high-resolution enhancers, enhancer modules, upstream regulators and downstream target genes. We used this resource to annotate 30,000~genetic loci that were associated with 540~traits, predicting trait-relevant tissues, putative causal nucleotide variants in enriched tissue enhancers and candidate tissue-specific target genes for each. We partitioned multifactorial traits into tissue-specific contributing factors with distinct functional enrichments and disease comorbidity patterns, and revealed both single-factor monotropic and multifactor pleiotropic loci. Top-scoring loci frequently had multiple predicted driver variants, converging through multiple enhancers with a common target gene, multiple genes in common tissues, or multiple genes and multiple tissues, indicating extensive pleiotropy. Our results demonstrate the importance of dense, rich, high-resolution epigenomic annotations for the investigation of complex traits.

}, keywords = {Chromatin, Disease, Enhancer Elements, Genetic, Epigenesis, Genetic, Epigenomics, Female, Gene Regulatory Networks, Genetic Loci, Genome-Wide Association Study, Humans, Male, Multifactorial Inheritance, Organ Specificity, Reproducibility of Results}, issn = {1476-4687}, doi = {10.1038/s41586-020-03145-z}, author = {Boix, Carles A and James, Benjamin T and Park, Yongjin P and Meuleman, Wouter and Kellis, Manolis} } @article {96, title = {Sex-specific genetic effects across biomarkers.}, journal = {Eur J Hum Genet}, volume = {29}, year = {2021}, month = {2021 Jan}, pages = {154-163}, abstract = {

Sex differences have been shown in laboratory biomarkers; however, the extent to which this is due to genetics is unknown. In this study, we infer sex-specific genetic parameters (heritability and genetic correlation) across 33 quantitative biomarker traits in 181,064 females and 156,135 males from the UK Biobank study. We apply a Bayesian Mixture Model, Sex Effects Mixture Model~(SEMM), to Genome-wide Association Study summary statistics in order to (1) estimate the contributions of sex to the genetic variance of these biomarkers and (2) identify variants whose statistical association with these traits is sex-specific. We find that the genetics of most biomarker traits are shared between males and females, with the notable exception of testosterone, where we identify 119 female and 445 male-specific variants. These include protein-altering variants in steroid hormone production genes (POR, UGT2B7). Using the sex-specific variants as genetic instruments for Mendelian randomization, we find evidence for causal links between testosterone levels and height, body mass index, waist and hip circumference, and type 2 diabetes. We also show that sex-specific polygenic risk score models for testosterone outperform a combined model. Overall, these results demonstrate that while sex has a limited role in the genetics of most biomarker traits, sex plays an important role in testosterone genetics.

}, issn = {1476-5438}, doi = {10.1038/s41431-020-00712-w}, author = {Flynn, Emily and Tanigawa, Yosuke and Rodriguez, Fatima and Altman, Russ B and Sinnott-Armstrong, Nasa and Rivas, Manuel A} } @article {146, title = {Sub-genic intolerance, ClinVar, and the epilepsies: A whole-exome sequencing study of 29,165 individuals.}, journal = {Am J Hum Genet}, volume = {108}, year = {2021}, month = {2021 06 03}, pages = {965-982}, abstract = {

Both mild and severe epilepsies are influenced by variants in the same genes, yet an explanation for the resulting phenotypic variation is unknown. As part of the ongoing Epi25 Collaboration, we performed a whole-exome sequencing analysis of 13,487 epilepsy-affected individuals and 15,678 control individuals. While prior Epi25 studies focused on gene-based collapsing analyses, we asked how the pattern of variation within genes differs by epilepsy type. Specifically, we compared the genetic architectures of severe developmental and epileptic encephalopathies (DEEs) and two generally less severe epilepsies, genetic generalized epilepsy and non-acquired focal epilepsy (NAFE). Our gene-based rare variant collapsing analysis used geographic ancestry-based clustering that included broader ancestries than previously possible and revealed novel associations. Using the missense intolerance ratio (MTR), we found that variants in DEE-affected individuals are in significantly more intolerant genic sub-regions than those in NAFE-affected individuals. Only previously reported pathogenic variants absent in available genomic datasets showed a significant burden in epilepsy-affected individuals compared with control individuals, and the ultra-rare pathogenic variants associated with DEE were located in more intolerant genic sub-regions than variants associated with non-DEE epilepsies. MTR filtering improved the yield of ultra-rare pathogenic variants in affected individuals compared with control individuals. Finally, analysis of variants in genes without a disease association revealed a significant burden of loss-of-function variants in the genes most intolerant to such variation, indicating additional epilepsy-risk genes yet to be discovered. Taken together, our study suggests that genic and sub-genic intolerance are critical characteristics for interpreting the effects of variation in genes that influence epilepsy.

}, keywords = {Case-Control Studies, Cohort Studies, Epilepsy, Exome, Genetic Markers, Genetic Predisposition to Disease, Genetic Testing, Genetic Variation, Humans, Phenotype, Whole Exome Sequencing}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2021.04.009} } @article {137, title = {Survival Analysis on Rare Events Using Group-Regularized Multi-Response Cox Regression.}, journal = {Bioinformatics}, year = {2021}, month = {2021 Feb 09}, abstract = {

MOTIVATION: The prediction performance of Cox proportional hazard model suffers when there are only few uncensored events in the training data.

RESULTS: We propose a Sparse-Group regularized Cox regression method to improve the prediction performance of large-scale and high-dimensional survival data with few observed events. Our approach is applicable when there is one or more other survival responses that 1. has a large number of observed events; 2. share a common set of associated predictors with the rare event response. This scenario is common in the UK Biobank (Sudlow et al., 2015) dataset where records for a large number of common and less prevalent diseases of the same set of individuals are available. By analyzing these responses together, we hope to achieve higher prediction performance than when they are analyzed individually. To make this approach practical for large-scale data, we developed an accelerated proximal gradient optimization algorithm as well as a screening procedure inspired by Qian et al. (2020).

AVAILABILITY: https://github.com/rivas-lab/multisnpnet-Cox.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btab095}, author = {Li, Ruilin and Tanigawa, Yosuke and Justesen, Johanne M and Taylor, Jonathan and Hastie, Trevor and Tibshirani, Robert and Rivas, Manuel A} } @article {118, title = {Synaptic processes and immune-related pathways implicated in Tourette syndrome.}, journal = {Transl Psychiatry}, volume = {11}, year = {2021}, month = {2021 01 18}, pages = {56}, abstract = {

Tourette syndrome (TS) is a neuropsychiatric disorder of complex genetic architecture involving multiple interacting genes. Here, we sought to elucidate the pathways that underlie the neurobiology of the disorder through genome-wide analysis. We analyzed genome-wide genotypic data of 3581 individuals with TS and 7682 ancestry-matched controls and investigated associations of TS with sets of genes that are expressed in particular cell types and operate in specific neuronal and glial functions. We employed a self-contained, set-based association method (SBA) as well as a competitive gene set method (MAGMA) using individual-level genotype data to perform a comprehensive investigation of the biological background of TS. Our SBA analysis identified three significant gene sets after Bonferroni correction, implicating ligand-gated ion channel signaling, lymphocytic, and cell adhesion and transsynaptic signaling processes. MAGMA analysis further supported the involvement of the cell adhesion and trans-synaptic signaling gene set. The lymphocytic gene set was driven by variants in FLT3, raising an intriguing hypothesis for the involvement of a neuroinflammatory element in TS pathogenesis. The indications of involvement of ligand-gated ion channel signaling reinforce the role of GABA in TS, while the association of cell adhesion and trans-synaptic signaling gene set provides additional support for the role of adhesion molecules in neuropsychiatric disorders. This study reinforces previous findings but also provides new insights into the neurobiology of TS.

}, issn = {2158-3188}, doi = {10.1038/s41398-020-01082-z}, author = {Tsetsos, Fotis and Yu, Dongmei and Sul, Jae Hoon and Huang, Alden Y and Illmann, Cornelia and Osiecki, Lisa and Darrow, Sabrina M and Hirschtritt, Matthew E and Greenberg, Erica and Muller-Vahl, Kirsten R and Stuhrmann, Manfred and Dion, Yves and Rouleau, Guy A and Aschauer, Harald and Stamenkovic, Mara and Schl{\"o}gelhofer, Monika and Sandor, Paul and Barr, Cathy L and Grados, Marco A and Singer, Harvey S and N{\"o}then, Markus M and Hebebrand, Johannes and Hinney, Anke and King, Robert A and Fernandez, Thomas V and Barta, Csaba and Tarnok, Zsanett and Nagy, Peter and Depienne, Christel and Worbe, Yulia and Hartmann, Andreas and Budman, Cathy L and Rizzo, Renata and Lyon, Gholson J and McMahon, William M and Batterson, James R and Cath, Danielle C and Malaty, Irene A and Okun, Michael S and Berlin, Cheston and Woods, Douglas W and Lee, Paul C and Jankovic, Joseph and Robertson, Mary M and Gilbert, Donald L and Brown, Lawrence W and Coffey, Barbara J and Dietrich, Andrea and Hoekstra, Pieter J and Kuperman, Samuel and Zinner, Samuel H and Wagner, Michael and Knowles, James A and Jeremy Willsey, A and Tischfield, Jay A and Heiman, Gary A and Cox, Nancy J and Freimer, Nelson B and Neale, Benjamin M and Davis, Lea K and Coppola, Giovanni and Mathews, Carol A and Scharf, Jeremiah M and Paschou, Peristera and Barr, Cathy L and Batterson, James R and Berlin, Cheston and Budman, Cathy L and Cath, Danielle C and Coppola, Giovanni and Cox, Nancy J and Darrow, Sabrina and Davis, Lea K and Dion, Yves and Freimer, Nelson B and Grados, Marco A and Greenberg, Erica and Hirschtritt, Matthew E and Huang, Alden Y and Illmann, Cornelia and King, Robert A and Kurlan, Roger and Leckman, James F and Lyon, Gholson J and Malaty, Irene A and Mathews, Carol A and McMahon, William M and Neale, Benjamin M and Okun, Michael S and Osiecki, Lisa and Robertson, Mary M and Rouleau, Guy A and Sandor, Paul and Scharf, Jeremiah M and Singer, Harvey S and Smit, Jan H and Sul, Jae Hoon and Yu, Dongmei and Aschauer, Harald Aschauer Harald and Barta, Csaba and Budman, Cathy L and Cath, Danielle C and Depienne, Christel and Hartmann, Andreas and Hebebrand, Johannes and Konstantinidis, Anastasios and Mathews, Carol A and M{\"u}ller-Vahl, Kirsten and Nagy, Peter and N{\"o}then, Markus M and Paschou, Peristera and Rizzo, Renata and Rouleau, Guy A and Sandor, Paul and Scharf, Jeremiah M and Schl{\"o}gelhofer, Monika and Stamenkovic, Mara and Stuhrmann, Manfred and Tsetsos, Fotis and Tarnok, Zsanett and Wolanczyk, Tomasz and Worbe, Yulia and Brown, Lawrence and Cheon, Keun-Ah and Coffey, Barbara J and Dietrich, Andrea and Fernandez, Thomas V and Garcia-Delgar, Blanca and Gilbert, Donald and Grice, Dorothy E and Hagstr{\o}m, Julie and Hedderly, Tammy and Heiman, Gary A and Heyman, Isobel and Hoekstra, Pieter J and Huyser, Chaim and Kim, Young Key and Kim, Young-Shin and King, Robert A and Koh, Yun-Joo and Kook, Sodahm and Kuperman, Samuel and Leventhal, Bennett L and Madruga-Garrido, Marcos and Mir, Pablo and Morer, Astrid and M{\"u}nchau, Alexander and Plessen, Kerstin J and Roessner, Veit and Shin, Eun-Young and Song, Dong-Ho and Song, Jungeun and Tischfield, Jay A and Willsey, A Jeremy and Zinner, Samuel and Aschauer, Harald and Barr, Cathy L and Barta, Csaba and Batterson, James R and Berlin, Cheston and Brown, Lawrence and Budman, Cathy L and Cath, Danielle C and Coffey, Barbara J and Coppola, Giovanni and Cox, Nancy J and Darrow, Sabrina and Davis, Lea K and Depienne, Christel and Dietrich, Andrea and Dion, Yves and Fernandez, Thomas and Freimer, Nelson B and Gilbert, Donald and Grados, Marco A and Greenberg, Erica and Hartmann, Andreas and Hebebrand, Johannes and Heiman, Gary and Hirschtritt, Matthew E and Hoekstra, Pieter and Huang, Alden Y and Illmann, Cornelia and Jankovic, Joseph and King, Robert A and Kuperman, Samuel and Lee, Paul C and Lyon, Gholson J and Malaty, Irene A and Mathews, Carol A and McMahon, William M and M{\"u}ller-Vahl, Kirsten and Nagy, Peter and Neale, Benjamin M and N{\"o}then, Markus M and Okun, Michael S and Osiecki, Lisa and Paschou, Peristera and Rizzo, Renata and Robertson, Mary M and Rouleau, Guy A and Sandor, Paul and Scharf, Jeremiah M and Schl{\"o}gelhofer, Monika and Singer, Harvey S and Stamenkovic, Mara and Stuhrmann, Manfred and Sul, Jae Hoon and Tarnok, Zsanett and Tischfield, Jay and Tsetsos, Fotis and Willsey, A Jeremy and Woods, Douglas and Worbe, Yulia and Yu, Dongmei and Zinner, Samuel} } @article {112, title = {Whole exome sequencing analyses reveal gene-microbiota interactions in the context of IBD.}, journal = {Gut}, volume = {70}, year = {2021}, month = {2021 Feb}, pages = {285-296}, abstract = {

OBJECTIVE: Both the gut microbiome and host genetics are known to play significant roles in the pathogenesis of IBD. However, the interaction between these two factors and its implications in the aetiology of IBD remain underexplored. Here, we report on the influence of host genetics on the gut microbiome in IBD.

DESIGN: To evaluate the impact of host genetics on the gut microbiota of patients with IBD, we combined whole exome sequencing of the host genome and whole genome shotgun sequencing of 1464 faecal samples from 525 patients with IBD and 939 population-based controls. We followed a four-step analysis: (1) exome-wide microbial quantitative trait loci (mbQTL) analyses, (2) a targeted approach focusing on IBD-associated genomic regions and protein truncating variants (PTVs, minor allele frequency (MAF) >5\%), (3) gene-based burden tests on PTVs with MAF <5\% and exome copy number variations (CNVs) with site frequency <1\%, (4) joint analysis of both cohorts to identify the interactions between disease and host genetics.

RESULTS: We identified 12 mbQTLs, including variants in the IBD-associated genes , , and . For example, the decrease of the pathway acetyl-coenzyme A biosynthesis, which is involved in short chain fatty acids production, was associated with variants in the gene (false discovery rate <0.05). Changes in functional pathways involved in the metabolic potential were also observed in participants carrying rare PTVs or CNVs in , and genes. These genes are known for their function in the immune system. Moreover, interaction analyses confirmed previously known IBD disease-specific mbQTLs in .

CONCLUSION: This study highlights that both common and rare genetic variants affecting the immune system are key factors in shaping the gut microbiota in the context of IBD and pinpoints towards potential mechanisms for disease treatment.

}, issn = {1468-3288}, doi = {10.1136/gutjnl-2019-319706}, author = {Hu, Shixian and Vich Vila, Arnau and Gacesa, Ranko and Collij, Valerie and Stevens, Christine and Fu, Jack M and Wong, Isaac and Talkowski, Michael E and Rivas, Manuel A and Imhann, Floris and Bolte, Laura and van Dullemen, Hendrik and Dijkstra, Gerard and Visschedijk, Marijn C and Festen, Eleonora A and Xavier, Ramnik J and Fu, Jingyuan and Daly, Mark J and Wijmenga, Cisca and Zhernakova, Alexandra and Kurilshikov, Alexander and Weersma, Rinse K} } @article {87, title = {Allelic Heterogeneity at the CRP Locus Identified by Whole-Genome Sequencing in Multi-ancestry Cohorts.}, journal = {Am J Hum Genet}, volume = {106}, year = {2020}, month = {2020 01 02}, pages = {112-120}, abstract = {

Whole-genome sequencing (WGS) can improve assessment of low-frequency and rare variants, particularly in non-European populations that have been underrepresented in existing genomic studies. The genetic determinants of C-reactive protein (CRP), a biomarker of chronic inflammation, have been extensively studied, with existing genome-wide association studies (GWASs) conducted in >200,000 individuals of European ancestry. In order to discover novel loci associated with CRP levels, we examined a multi-ancestry population (n = 23,279) with WGS (\~{}38{\texttimes} coverage) from the Trans-Omics for Precision Medicine (TOPMed) program. We found evidence for eight distinct associations at the CRP locus, including two variants that have not been identified previously (rs11265259 and rs181704186), both of which are non-coding and more common in individuals of African ancestry (\~{}10\% and \~{}1\% minor allele frequency, respectively, and rare or monomorphic in 1000 Genomes populations of East Asian, South Asian, and European ancestry). We show that the minor (G) allele of rs181704186 is associated with lower CRP levels and decreased transcriptional activity and protein binding in~vitro, providing a plausible molecular mechanism for this African ancestry-specific signal. The individuals homozygous for rs181704186-G have a mean CRP level of 0.23~mg/L, in contrast to individuals heterozygous for rs181704186 with mean CRP of 2.97~mg/L and major allele homozygotes with mean CRP of 4.11~mg/L. This study demonstrates the utility of WGS in multi-ethnic populations to drive discovery of complex trait associations of large effect and to identify functional alleles in noncoding regulatory regions.

}, keywords = {African Continental Ancestry Group, Asian Continental Ancestry Group, C-Reactive Protein, Cohort Studies, European Continental Ancestry Group, Gene Frequency, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Linkage Disequilibrium, Polymorphism, Single Nucleotide, Whole Genome Sequencing}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2019.12.002}, author = {Raffield, Laura M and Iyengar, Apoorva K and Wang, Biqi and Gaynor, Sheila M and Spracklen, Cassandra N and Zhong, Xue and Kowalski, Madeline H and Salimi, Shabnam and Polfus, Linda M and Benjamin, Emelia J and Bis, Joshua C and Bowler, Russell and Cade, Brian E and Choi, Won Jung and Comellas, Alejandro P and Correa, Adolfo and Cruz, Pedro and Doddapaneni, Harsha and Durda, Peter and Gogarten, Stephanie M and Jain, Deepti and Kim, Ryan W and Kral, Brian G and Lange, Leslie A and Larson, Martin G and Laurie, Cecelia and Lee, Jiwon and Lee, Seonwook and Lewis, Joshua P and Metcalf, Ginger A and Mitchell, Braxton D and Momin, Zeineen and Muzny, Donna M and Pankratz, Nathan and Park, Cheol Joo and Rich, Stephen S and Rotter, Jerome I and Ryan, Kathleen and Seo, Daekwan and Tracy, Russell P and Viaud-Martinez, Karine A and Yanek, Lisa R and Zhao, Lue Ping and Lin, Xihong and Li, Bingshan and Li, Yun and Dupuis, Jos{\'e}e and Reiner, Alexander P and Mohlke, Karen L and Auer, Paul L} } @article {108, title = {Analysis in case-control sequencing association studies with different sequencing depths.}, journal = {Biostatistics}, volume = {21}, year = {2020}, month = {2020 07 01}, pages = {577-593}, abstract = {

With the advent of next-generation sequencing, investigators have access to higher quality sequencing data. However, to sequence all samples in a study using next generation sequencing can still be prohibitively expensive. One potential remedy could be to combine next generation sequencing data from cases with publicly available sequencing data for controls, but there could be a systematic difference in quality of sequenced data, such as sequencing depths, between sequenced study cases and publicly available controls. We propose a regression calibration (RC)-based method and a maximum-likelihood method for conducting an association study with such a combined sample by accounting for differential sequencing errors between cases and controls. The methods allow for adjusting for covariates, such as population stratification as confounders. Both methods control type I error and have comparable power to analysis conducted using the true genotype with sufficiently high but different sequencing depths. We show that the RC method allows for analysis using naive variance estimate (closely approximates true variance in practice) and standard software under certain circumstances. We evaluate the performance of the proposed methods using simulation studies and apply our methods to a combined data set of exome sequenced acute lung injury cases and healthy controls from the 1000 Genomes project.

}, issn = {1468-4357}, doi = {10.1093/biostatistics/kxy073}, author = {Chen, Sixing and Lin, Xihong} } @article {91, title = {Assessing Digital Phenotyping to Enhance Genetic Studies of Human Diseases.}, journal = {Am J Hum Genet}, volume = {106}, year = {2020}, month = {2020 05 07}, pages = {611-622}, abstract = {

Population-scale biobanks that combine genetic data and high-dimensional phenotyping for a large number of participants provide an exciting opportunity to perform genome-wide association studies (GWAS) to identify genetic variants associated with diverse quantitative traits and diseases. A major challenge for GWAS in population biobanks is ascertaining disease cases from heterogeneous data sources such as hospital records, digital questionnaire responses, or interviews. In this study, we use genetic parameters, including genetic correlation, to evaluate whether GWAS performed using cases in the UK Biobank ascertained from hospital records, questionnaire responses, and family history of disease implicate similar disease genetics across a range of effect sizes. We find that hospital record and questionnaire GWAS largely identify similar genetic effects for many complex phenotypes and that combining together both phenotyping methods improves power to detect genetic associations. We also show that family history GWAS using cases ascertained on family history of disease agrees with combined hospital record and questionnaire GWAS and that family history GWAS has better power to detect genetic associations for some phenotypes. Overall, this work demonstrates that digital phenotyping and unstructured phenotype data can be combined with structured data such as hospital records to identify cases for GWAS in biobanks and improve the ability of such studies to identify genetic associations.

}, keywords = {Asthma, Databases, Factual, Disease, Female, Genetics, Medical, Genome-Wide Association Study, Genotype, Humans, Male, Neoplasms, Phenotype, United Kingdom}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2020.03.007}, author = {DeBoever, Christopher and Tanigawa, Yosuke and Aguirre, Matthew and McInnes, Greg and Lavertu, Adam and Rivas, Manuel A} } @article {85, title = {A brief history of human disease genetics.}, journal = {Nature}, volume = {577}, year = {2020}, month = {2020 01}, pages = {179-189}, abstract = {

A primary goal of human genetics is to identify DNA sequence variants that influence biomedical traits, particularly those related to the onset and progression of human disease. Over the past 25 years, progress in realizing this objective has been transformed by advances in technology, foundational genomic resources and analytical tools, and by access to vast amounts of genotype and phenotype data. Genetic discoveries have substantially improved~our understanding of the mechanisms responsible for many rare and common diseases and driven development of novel preventative and therapeutic strategies. Medical innovation will increasingly focus on delivering care tailored to individual patterns of genetic predisposition.

}, keywords = {Animals, Genetic Testing, Genetic Variation, Genomics, Genotype, Humans, Phenotype, Rare Diseases}, issn = {1476-4687}, doi = {10.1038/s41586-019-1879-7}, author = {Claussnitzer, Melina and Cho, Judy H and Collins, Rory and Cox, Nancy J and Dermitzakis, Emmanouil T and Hurles, Matthew E and Kathiresan, Sekar and Kenny, Eimear E and Lindgren, Cecilia M and MacArthur, Daniel G and North, Kathryn N and Plon, Sharon E and Rehm, Heidi L and Risch, Neil and Rotimi, Charles N and Shendure, Jay and Soranzo, Nicole and McCarthy, Mark I} } @article {126, title = {A common variant in PNPLA3 is associated with age at diagnosis of NAFLD in patients from a multi-ethnic biobank.}, journal = {J Hepatol}, volume = {72}, year = {2020}, month = {2020 06}, pages = {1070-1081}, abstract = {

BACKGROUND \& AIMS: The Ile138Met variant (rs738409) in the PNPLA3 gene has the largest effect on non-alcoholic fatty liver disease (NAFLD), increasing the risk of progression to severe forms of liver disease. It remains unknown if the variant plays a role in age of NAFLD onset. We aimed to determine if rs738409 impacts on the age of NAFLD diagnosis.

METHODS: We applied a novel natural language processing (NLP) algorithm to a longitudinal electronic health records (EHR) dataset of >27,000 individuals with genetic data from a multi-ethnic biobank, defining NAFLD cases (n~= 1,703) and confirming controls (n~= 8,119). We conducted i) a survival analysis to determine if age at diagnosis differed by rs738409 genotype, ii) a receiver operating characteristics analysis to assess the utility of the rs738409 genotype in discriminating NAFLD cases from controls, and iii) a phenome-wide association study (PheWAS) between rs738409 and 10,095 EHR-derived disease diagnoses.

RESULTS: The PNPLA3 G risk allele was associated with: i) earlier age of NAFLD diagnosis, with the strongest effect in Hispanics (hazard ratio 1.33; 95\% CI 1.15-1.53; p <0.0001) among whom a NAFLD diagnosis was 15\% more likely in risk allele carriers vs. non-carriers; ii) increased NAFLD risk (odds ratio 1.61; 95\% CI 1.349-1.73; p <0.0001), with the strongest effect among Hispanics (odds ratio 1.43; 95\% CI 1.28-1.59; p <0.0001); iii) additional liver diseases in a PheWAS (p <4.95~{\texttimes} 10) where the risk variant also associated with earlier age of diagnosis.

CONCLUSION: Given the role of the rs738409 in NAFLD diagnosis age, our results suggest that stratifying risk within populations known to have an enhanced risk of liver disease, such as Hispanic carriers of the rs738409 variant, would be effective in earlier identification of those who would benefit most from early NAFLD prevention and treatment strategies.

LAY SUMMARY: Despite clear associations between the PNPLA3 rs738409 variant and elevated risk of progression from non-alcoholic fatty liver disease (NAFLD) to more severe forms of liver disease, it remains unknown if PNPLA3 rs738409 plays a role in the age of NAFLD onset. Herein, we found that this risk variant is associated with an earlier age of NAFLD and other liver disease diagnoses; an observation most pronounced in Hispanic Americans. We conclude that PNPLA3 rs738409 could be used to better understand liver disease risk within vulnerable populations and identify patients that may benefit from early prevention strategies.

}, issn = {1600-0641}, doi = {10.1016/j.jhep.2020.01.029}, author = {Walker, Ryan W and Belbin, Gillian M and Sorokin, Elena P and Van Vleck, Tielman and Wojcik, Genevieve L and Moscati, Arden and Gignoux, Christopher R and Cho, Judy and Abul-Husn, Noura S and Nadkarni, Girish and Kenny, Eimear E and Loos, Ruth J F} } @article {100, title = {On the cross-population generalizability of gene expression prediction models.}, journal = {PLoS Genet}, volume = {16}, year = {2020}, month = {2020 08}, pages = {e1008927}, abstract = {

The genetic control of gene expression is a core component of human physiology. For the past several years, transcriptome-wide association studies have leveraged large datasets of linked genotype and RNA sequencing information to create a powerful gene-based test of association that has been used in dozens of studies. While numerous discoveries have been made, the populations in the training data are overwhelmingly of European descent, and little is known about the generalizability of these models to other populations. Here, we test for cross-population generalizability of gene expression prediction models using a dataset of African American individuals with RNA-Seq data in whole blood. We find that the default models trained in large datasets such as GTEx and DGN fare poorly in African Americans, with a notable reduction in prediction accuracy when compared to European Americans. We replicate these limitations in cross-population generalizability using the five populations in the GEUVADIS dataset. Via realistic simulations of both populations and gene expression, we show that accurate cross-population generalizability of transcriptome prediction only arises when eQTL architecture is substantially shared across populations. In contrast, models with non-identical eQTLs showed patterns similar to real-world data. Therefore, generating RNA-Seq data in diverse populations is a critical step towards multi-ethnic utility of gene expression prediction.

}, keywords = {African Americans, Gene Expression Profiling, Genome-Wide Association Study, Humans, Models, Genetic, Quantitative Trait Loci, Reference Standards, RNA-Seq, Transcriptome}, issn = {1553-7404}, doi = {10.1371/journal.pgen.1008927}, author = {Keys, Kevin L and Mak, Angel C Y and White, Marquitta J and Eckalbar, Walter L and Dahl, Andrew W and Mefford, Joel and Mikhaylova, Anna V and Contreras, Mar{\'\i}a G and Elhawary, Jennifer R and Eng, Celeste and Hu, Donglei and Huntsman, Scott and Oh, Sam S and Salazar, Sandra and LeNoir, Michael A and Ye, Jimmie C and Thornton, Timothy A and Zaitlen, Noah and Burchard, Esteban G and Gignoux, Christopher R} } @article {99, title = {Dynamic incorporation of multiple in silico functional annotations empowers rare variant association analysis of large whole-genome sequencing studies at scale.}, journal = {Nat Genet}, volume = {52}, year = {2020}, month = {2020 09}, pages = {969-983}, abstract = {

Large-scale whole-genome sequencing studies have enabled the analysis of rare variants (RVs) associated with complex phenotypes. Commonly used RV association tests have limited scope to leverage variant functions. We propose STAAR (variant-set test for association using annotation information), a scalable and powerful RV association test method that effectively incorporates both variant categories and multiple complementary annotations using a dynamic weighting scheme. For the latter, we introduce {\textquoteright}annotation principal components{\textquoteright}, multidimensional summaries of in silico variant annotations. STAAR accounts for population structure and relatedness and is scalable for analyzing very large cohort and biobank whole-genome sequencing studies of continuous and dichotomous traits. We applied STAAR to identify RVs associated with four lipid traits in 12,316 discovery and 17,822 replication samples from the Trans-Omics for Precision Medicine Program. We discovered and replicated new RV associations, including disruptive missense RVs of NPC1L1 and an intergenic region near APOC1P1 associated with low-density lipoprotein cholesterol.

}, keywords = {Cholesterol, LDL, Computer Simulation, Genetic Predisposition to Disease, Genetic Variation, Genome, Genome-Wide Association Study, Humans, Models, Genetic, Molecular Sequence Annotation, Phenotype, Whole Genome Sequencing}, issn = {1546-1718}, doi = {10.1038/s41588-020-0676-4}, author = {Li, Xihao and Li, Zilin and Zhou, Hufeng and Gaynor, Sheila M and Liu, Yaowu and Chen, Han and Sun, Ryan and Dey, Rounak and Arnett, Donna K and Aslibekyan, Stella and Ballantyne, Christie M and Bielak, Lawrence F and Blangero, John and Boerwinkle, Eric and Bowden, Donald W and Broome, Jai G and Conomos, Matthew P and Correa, Adolfo and Cupples, L Adrienne and Curran, Joanne E and Freedman, Barry I and Guo, Xiuqing and Hindy, George and Irvin, Marguerite R and Kardia, Sharon L R and Kathiresan, Sekar and Khan, Alyna T and Kooperberg, Charles L and Laurie, Cathy C and Liu, X Shirley and Mahaney, Michael C and Manichaikul, Ani W and Martin, Lisa W and Mathias, Rasika A and McGarvey, Stephen T and Mitchell, Braxton D and Montasser, May E and Moore, Jill E and Morrison, Alanna C and O{\textquoteright}Connell, Jeffrey R and Palmer, Nicholette D and Pampana, Akhil and Peralta, Juan M and Peyser, Patricia A and Psaty, Bruce M and Redline, Susan and Rice, Kenneth M and Rich, Stephen S and Smith, Jennifer A and Tiwari, Hemant K and Tsai, Michael Y and Vasan, Ramachandran S and Wang, Fei Fei and Weeks, Daniel E and Weng, Zhiping and Wilson, James G and Yanek, Lisa R and Neale, Benjamin M and Sunyaev, Shamil R and Abecasis, Gon{\c c}alo R and Rotter, Jerome I and Willer, Cristen J and Peloso, Gina M and Natarajan, Pradeep and Lin, Xihong} } @article {123, title = {Efficient Estimation and Applications of Cross-Validated Genetic Predictions to Polygenic Risk Scores and Linear Mixed Models.}, journal = {J Comput Biol}, volume = {27}, year = {2020}, month = {2020 04}, pages = {599-612}, abstract = {

Large-scale cohorts with combined genetic and phenotypic data, coupled with methodological advances, have produced increasingly accurate genetic predictors of complex human phenotypes called polygenic risk scores (PRSs). In addition to the potential translational impacts of identifying at-risk individuals, PRS are being utilized for a growing list of scientific applications, including causal inference, identifying pleiotropy and genetic correlation, and powerful gene-based and mixed-model association tests. Existing PRS approaches rely on external large-scale genetic cohorts that have also measured the phenotype of interest. They further require matching on ancestry and genotyping platform or imputation quality. In this work, we present a novel reference-free method to produce a PRS that does not rely on an external cohort. We show that naive implementations of reference-free PRS either result in substantial overfitting or prohibitive increases in computational time. We show that our algorithm avoids both of these issues and can produce informative in-sample PRSs over a single cohort without overfitting. We then demonstrate several novel applications of reference-free PRSs, including detection of pleiotropy across 246 metabolic traits and efficient mixed-model association testing.

}, issn = {1557-8666}, doi = {10.1089/cmb.2019.0325}, author = {Mefford, Joel and Park, Danny and Zheng, Zhili and Ko, Arthur and Ala-Korpela, Mika and Laakso, Markku and Pajukanta, P{\"a}ivi and Yang, Jian and Witte, John and Zaitlen, Noah} } @article {82, title = {Electronic health record phenotypes associated with genetically regulated expression of CFTR and application to cystic fibrosis.}, journal = {Genet Med}, volume = {22}, year = {2020}, month = {2020 07}, pages = {1191-1200}, abstract = {

PURPOSE: The increasing use of electronic health records (EHRs) and biobanks offers unique opportunities to study Mendelian diseases. We described a novel approach to summarize clinical manifestations from patient EHRs into phenotypic evidence for cystic fibrosis (CF) with potential to alert unrecognized patients of the disease.

METHODS: We estimated genetically predicted expression (GReX) of cystic fibrosis transmembrane conductance regulator (CFTR) and tested for association with clinical diagnoses in the Vanderbilt University biobank (N = 9142 persons of European descent with 71 cases of CF). The top associated EHR phenotypes were assessed in combination as a phenotype risk score (PheRS) for discriminating CF case status in an additional 2.8 million patients from Vanderbilt University Medical Center (VUMC) and 125,305 adult patients including 25,314 CF cases from MarketScan, an independent external cohort.

RESULTS: GReX of CFTR was associated with EHR phenotypes consistent with CF. PheRS constructed using the EHR phenotypes and weights discovered by the genetic associations improved discriminative power for CF over the initially proposed PheRS in both VUMC and MarketScan.

CONCLUSION: Our study demonstrates the power of EHRs for clinical description of CF and the benefits of using a genetics-informed weighing scheme in construction of a phenotype risk score. This research may find broad applications for phenomic studies of Mendelian disease genes.

}, keywords = {Adult, Cystic Fibrosis, Cystic Fibrosis Transmembrane Conductance Regulator, Electronic Health Records, Humans, Mutation, Phenotype}, issn = {1530-0366}, doi = {10.1038/s41436-020-0786-5}, author = {Zhong, Xue and Yin, Zhijun and Jia, Gengjie and Zhou, Dan and Wei, Qiang and Faucon, Annika and Evans, Patrick and Gamazon, Eric R and Li, Bingshan and Tao, Ran and Rzhetsky, Andrey and Bastarache, Lisa and Cox, Nancy J} } @article {116, title = {Epidemiology of Functional Seizures Among Adults Treated at a University Hospital.}, journal = {JAMA Netw Open}, volume = {3}, year = {2020}, month = {2020 12 01}, pages = {e2027920}, abstract = {

Importance: Functional seizures (formerly psychogenic nonepileptic seizures), paroxysmal episodes that are often similar to epileptic seizures in their clinical presentation and display no aberrant brain electrical patterns, are understudied. Patients experience a long diagnostic delay, few treatment modalities, a high rate of comorbidities, and significant stigma due to the lack of knowledge about functional seizures.

Objective: To characterize the clinical epidemiology of a population of patients with functional seizures observed at Vanderbilt University Medical Center (VUMC).

Design, Setting, and Participants: This case-control study included patients with functional seizures identified in the VUMC electronic health record (VUMC-EHR) system from October 1989 to October 2018. Patients with epilepsy were excluded from the study and all remaining patients in the VUMC medical center system were used as controls. In total, the study included 1431 patients diagnosed with functional seizures, 2251 with epilepsy and functional seizures, 4715 with epilepsy without functional seizures, and 502 200 control patients who received treatment at VUMC for a minimum of a 3 years. Data were analyzed from November 2018 to March 2020.

Exposure: Diagnosis of functional seizures, as identified from the VUMC-EHR system by an automated phenotyping algorithm that incorporated International Classification of Diseases, Ninth Revision (ICD-9) codes, International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) codes, Current Procedural Terminology codes, and natural language processing.

Main Outcomes and Measures: Associations of functional seizures with comorbidities and risk factors, measured in odds ratios (ORs).

Results: Of 2 346 808 total patients in the VUMC-EHR aged 18 years or older, 3341 patients with functional seizures were identified (period prevalence, 0.14\%), 1062 (74.2\%) of whom were women and for which the median (interquartile range) age was 49.3 (39.4-59.9) years. This assessment replicated previously reported associations with psychiatric disorders including posttraumatic stress disorder (PTSD) (OR, 1.22; 95\% CI, 1.21-1.24; P < 3.02 {\texttimes} 10-5), anxiety (OR, 1.14; 95\% CI, 1.13-1.15; P < 3.02 {\texttimes} 10-5), and depression (OR, 1.14; 95\% CI, 1.13-1.15; P < 3.02 {\texttimes} 10-5), and identified novel associations with cerebrovascular disease (OR, 1.08; 95\% CI, 1.06-1.09; P < 3.02 {\texttimes} 10-5). An association was found between functional seizures and the known risk factor sexual assault trauma (OR, 10.26; 95\% CI, 10.09-10.44; P < 3.02 {\texttimes} 10-5), and sexual assault trauma was found to mediate nearly a quarter of the association between female sex and functional seizures in the VUMC-EHR.

Conclusions and Relevance: This case-control study found evidence to support previously reported associations, discovered new associations between functional seizures and PTSD, anxiety, and depression. An association between cerebrovascular disease and functional seizures was also found. Results suggested that sexual trauma may be a mediating factor in the association between female sex and functional seizures.

}, keywords = {Adult, Anxiety, Case-Control Studies, Comorbidity, Delayed Diagnosis, Depression, Female, Hospitals, University, Humans, Male, Mental Disorders, Middle Aged, Odds Ratio, Risk Factors, Seizures, Stress Disorders, Post-Traumatic}, issn = {2574-3805}, doi = {10.1001/jamanetworkopen.2020.27920}, author = {Goleva, Slavina B and Lake, Allison M and Torstenson, Eric S and Haas, Kevin F and Davis, Lea K} } @article {120, title = {Evidence for secondary-variant genetic burden and non-random distribution across biological modules in a recessive ciliopathy.}, journal = {Nat Genet}, volume = {52}, year = {2020}, month = {2020 11}, pages = {1145-1150}, abstract = {

The influence of genetic background on driver mutations is well established; however, the mechanisms by which the background interacts with Mendelian loci remain unclear. We performed a systematic secondary-variant burden analysis of two independent cohorts of patients with Bardet-Biedl syndrome (BBS) with known recessive biallelic pathogenic mutations in one of 17 BBS genes for each individual. We observed a significant enrichment of trans-acting rare nonsynonymous secondary variants in patients with BBS compared with either population controls or a cohort of individuals with a non-BBS diagnosis and recessive variants in the same gene set. Strikingly, we found a significant over-representation of secondary alleles in chaperonin-encoding genes-a finding corroborated by the observation of epistatic interactions involving this complex in vivo. These data indicate a complex genetic architecture for BBS that informs the biological properties of disease modules and presents a model for secondary-variant burden analysis in recessive disorders.

}, keywords = {Alleles, Bardet-Biedl Syndrome, Cohort Studies, Exome, Genetic Variation, Humans}, issn = {1546-1718}, doi = {10.1038/s41588-020-0707-1}, author = {Kousi, Maria and S{\"o}ylemez, Onuralp and Ozanturk, Ayseg{\"u}l and Mourtzi, Niki and Akle, Sebastian and Jungreis, Irwin and Muller, Jean and Cassa, Christopher A and Brand, Harrison and Mokry, Jill Anne and Wolf, Maxim Y and Sadeghpour, Azita and McFadden, Kelsey and Lewis, Richard A and Talkowski, Michael E and Dollfus, H{\'e}l{\`e}ne and Kellis, Manolis and Davis, Erica E and Sunyaev, Shamil R and Katsanis, Nicholas} } @article {121, title = {A fast and scalable framework for large-scale and ultrahigh-dimensional sparse regression with application to the UK Biobank.}, journal = {PLoS Genet}, volume = {16}, year = {2020}, month = {2020 10}, pages = {e1009141}, abstract = {

The UK Biobank is a very large, prospective population-based cohort study across the United Kingdom. It provides unprecedented opportunities for researchers to investigate the relationship between genotypic information and phenotypes of interest. Multiple regression methods, compared with genome-wide association studies (GWAS), have already been showed to greatly improve the prediction performance for a variety of phenotypes. In the high-dimensional settings, the lasso, since its first proposal in statistics, has been proved to be an effective method for simultaneous variable selection and estimation. However, the large-scale and ultrahigh dimension seen in the UK Biobank pose new challenges for applying the lasso method, as many existing algorithms and their implementations are not scalable to large applications. In this paper, we propose a computational framework called batch screening iterative lasso (BASIL) that can take advantage of any existing lasso solver and easily build a scalable solution for very large data, including those that are larger than the memory size. We introduce snpnet, an R package that implements the proposed algorithm on top of glmnet and optimizes for single nucleotide polymorphism (SNP) datasets. It currently supports l1-penalized linear model, logistic regression, Cox model, and also extends to the elastic net with l1/l2 penalty. We demonstrate results on the UK Biobank dataset, where we achieve competitive predictive performance for all four phenotypes considered (height, body mass index, asthma, high cholesterol) using only a small fraction of the variants compared with other established polygenic risk score methods.

}, keywords = {Algorithms, Asthma, Biological Specimen Banks, Body Height, Body Mass Index, Cholesterol, Cohort Studies, Genetics, Population, Genome-Wide Association Study, Genotype, Humans, Logistic Models, Phenotype, Polymorphism, Single Nucleotide, Proportional Hazards Models, United Kingdom}, issn = {1553-7404}, doi = {10.1371/journal.pgen.1009141}, author = {Qian, Junyang and Tanigawa, Yosuke and Du, Wenfei and Aguirre, Matthew and Chang, Chris and Tibshirani, Robert and Rivas, Manuel A and Hastie, Trevor} } @article {119, title = {Fast Lasso method for large-scale and ultrahigh-dimensional Cox model with applications to UK Biobank.}, journal = {Biostatistics}, year = {2020}, month = {2020 Sep 29}, abstract = {

We develop a scalable and highly efficient algorithm to fit a Cox proportional hazard model by maximizing the $L^1$-regularized (Lasso) partial likelihood function, based on the Batch Screening Iterative Lasso (BASIL) method developed in Qian and others (2019). Our algorithm is particularly suitable for large-scale and high-dimensional data that do not fit in the memory. The output of our algorithm is the full Lasso path, the parameter estimates at all predefined regularization parameters, as well as their validation accuracy measured using the concordance index (C-index) or the validation deviance. To demonstrate the effectiveness of our algorithm, we analyze a large genotype-survival time dataset across 306 disease outcomes from the UK Biobank (Sudlow and others, 2015). We provide a publicly available implementation of the proposed approach for genetics data on top of the PLINK2 package and name it snpnet-Cox.

}, issn = {1468-4357}, doi = {10.1093/biostatistics/kxaa038}, author = {Li, Ruilin and Chang, Christopher and Justesen, Johanne M and Tanigawa, Yosuke and Qiang, Junyang and Hastie, Trevor and Rivas, Manuel A and Tibshirani, Robert} } @article {104, title = {FasTag: Automatic text classification of unstructured medical narratives.}, journal = {PLoS One}, volume = {15}, year = {2020}, month = {2020}, pages = {e0234647}, abstract = {

Unstructured clinical narratives are continuously being recorded as part of delivery of care in electronic health records, and dedicated tagging staff spend considerable effort manually assigning clinical codes for billing purposes. Despite these efforts, however, label availability and accuracy are both suboptimal. In this retrospective study, we aimed to automate the assignment of top-level International Classification of Diseases version 9 (ICD-9) codes to clinical records from human and veterinary data stores using minimal manual labor and feature curation. Automating top-level annotations could in turn enable rapid cohort identification, especially in a veterinary setting. To this end, we trained long short-term memory (LSTM) recurrent neural networks (RNNs) on 52,722 human and 89,591 veterinary records. We investigated the accuracy of both separate-domain and combined-domain models and probed model portability. We established relevant baseline classification performances by training Decision Trees (DT) and Random Forests (RF). We also investigated whether transforming the data using MetaMap Lite, a clinical natural language processing tool, affected classification performance. We showed that the LSTM-RNNs accurately classify veterinary and human text narratives into top-level categories with an average weighted macro F1 score of 0.74 and 0.68 respectively. In the "neoplasia" category, the model trained on veterinary data had a high validation accuracy in veterinary data and moderate accuracy in human data, with F1 scores of 0.91 and 0.70 respectively. Our LSTM method scored slightly higher than that of the DT and RF models. The use of LSTM-RNN models represents a scalable structure that could prove useful in cohort identification for comparative oncology studies. Digitization of human and veterinary health information will continue to be a reality, particularly in the form of unstructured narratives. Our approach is a step forward for these two domains to learn from and inform one another.

}, keywords = {Animals, Automation, Data Mining, Databases as Topic, Humans, Narrative Medicine, Reproducibility of Results, Software, Species Specificity}, issn = {1932-6203}, doi = {10.1371/journal.pone.0234647}, author = {Venkataraman, Guhan Ram and Pineda, Arturo Lopez and Bear Don{\textquoteright}t Walk Iv, Oliver J and Zehnder, Ashley M and Ayyar, Sandeep and Page, Rodney L and Bustamante, Carlos D and Rivas, Manuel A} } @article {75, title = {Genetic diagnoses in epilepsy: The impact of dynamic exome analysis in a pediatric cohort.}, journal = {Epilepsia}, volume = {61}, year = {2020}, month = {2020 02}, pages = {249-258}, abstract = {

OBJECTIVE: We evaluated the yield of systematic analysis and/or reanalysis of whole exome sequencing (WES) data from a cohort of well-phenotyped pediatric patients with epilepsy and suspected but previously undetermined genetic etiology.

METHODS: We identified and phenotyped 125 participants with pediatric epilepsy. Etiology was unexplained at the time of enrollment despite clinical testing, which included chromosomal microarray (57 patients), epilepsy gene panel (n = 48), both (n = 28), or WES (n = 8). Clinical epilepsy diagnoses included developmental and epileptic encephalopathy (DEE), febrile infection-related epilepsy syndrome, Rasmussen encephalitis, and other focal and generalized epilepsies. We analyzed WES data and compared the yield in participants with and without prior clinical genetic testing.

RESULTS: Overall, we identified pathogenic or likely pathogenic variants in 40\% (50/125) of our study participants. Nine patients with DEE had genetic variants in recently published genes that had not been recognized as epilepsy-related at the time of clinical testing (FGF12, GABBR1, GABBR2, ITPA, KAT6A, PTPN23, RHOBTB2, SATB2), and eight patients had genetic variants in candidate epilepsy genes (CAMTA1, FAT3, GABRA6, HUWE1, PTCHD1). Ninety participants had concomitant or subsequent clinical genetic testing, which was ultimately explanatory for 26\% (23/90). Of the 67 participants whose molecular diagnoses were "unsolved" through clinical genetic testing, we identified pathogenic or likely pathogenic variants in 17 (25\%).

SIGNIFICANCE: Our data argue for early consideration of WES with iterative reanalysis for patients with epilepsy, particularly those with DEE or epilepsy with intellectual disability. Rigorous analysis of WES data of well-phenotyped patients with epilepsy leads to a broader understanding of gene-specific phenotypic spectra as well as candidate disease gene identification. We illustrate the dynamic nature of genetic diagnosis over time, with analysis and in some cases reanalysis of exome data leading to the identification of disease-associated variants among participants with previously nondiagnostic results from a variety of clinical testing strategies.

}, keywords = {Adolescent, Adult, Age of Onset, Brain Diseases, Child, Child, Preschool, Chromosomes, Human, Cohort Studies, Epilepsy, Epilepsy, Generalized, Exome, Female, Genetic Testing, Genetic Variation, Humans, Infant, Male, Microarray Analysis, Phenotype, Whole Exome Sequencing, Young Adult}, issn = {1528-1167}, doi = {10.1111/epi.16427}, author = {Rochtus, Anne and Olson, Heather E and Smith, Lacey and Keith, Louisa G and El Achkar, Christelle and Taylor, Alan and Mahida, Sonal and Park, Meredith and Kelly, McKenna and Shain, Catherine and Rockowitz, Shira and Rosen Sheidley, Beth and Poduri, Annapurna} } @article {107, title = {Genome-Wide Analysis Reveals Mucociliary Remodeling of the Nasal Airway Epithelium Induced by Urban PM.}, journal = {Am J Respir Cell Mol Biol}, volume = {63}, year = {2020}, month = {2020 08}, pages = {172-184}, abstract = {

Air pollution particulate matter <2.5 μm (PM) exposure is associated with poor respiratory outcomes. Mechanisms underlying PM-induced lung pathobiology are poorly understood but likely involve cellular and molecular changes to the airway epithelium. We extracted and chemically characterized the organic and water-soluble components of air pollution PM samples, then determined the whole transcriptome response of human nasal mucociliary airway epithelial cultures to a dose series of PM extracts. We found that PM organic extract (OE), but not water-soluble extract, elicited a potent, dose-dependent transcriptomic response from the mucociliary epithelium. Exposure to a moderate OE dose modified the expression of 424 genes, including activation of aryl hydrocarbon receptor signaling and an IL-1 inflammatory program. We generated an OE-response gene network defined by eight functional enrichment groups, which exhibited high connectivity through , , and . This OE exposure also robustly activated a mucus secretory expression program (>100 genes), which included transcriptional drivers of mucus metaplasia ( and ). Exposure to a higher OE dose modified the expression of 1,240 genes and further exacerbated expression responses observed at the moderate dose, including the mucus secretory program. Moreover, the higher OE dose significantly increased the / gel-forming mucin expression ratio and strongly downregulated ciliated cell expression programs, including key ciliating cell transcription factors (e.g., and ). Chronic OE stimulation induced mucus metaplasia-like remodeling characterized by increases in MUC5AC secretory cells and MUC5AC mucus secretions. This epithelial remodeling may underlie poor respiratory outcomes associated with high PM exposure.

}, issn = {1535-4989}, doi = {10.1165/rcmb.2019-0454OC}, author = {Montgomery, Michael T and Sajuthi, Satria P and Cho, Seung-Hyun and Everman, Jamie L and Rios, Cydney L and Goldfarbmuren, Katherine C and Jackson, Nathan D and Saef, Benjamin and Cromie, Meghan and Eng, Celeste and Medina, Vivian and Elhawary, Jennifer R and Oh, Sam S and Rodriguez-Santana, Jose and Vladar, Eszter K and Burchard, Esteban G and Seibold, Max A} } @article {122, title = {Human Demographic History Impacts Genetic Risk Prediction across Diverse Populations.}, journal = {Am J Hum Genet}, volume = {107}, year = {2020}, month = {2020 10 01}, pages = {788-789}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2020.08.020}, author = {Martin, Alicia R and Gignoux, Christopher R and Walters, Raymond K and Wojcik, Genevieve L and Neale, Benjamin M and Gravel, Simon and Daly, Mark J and Bustamante, Carlos D and Kenny, Eimear E} } @article {81, title = {Identification of cancer driver genes based on nucleotide context.}, journal = {Nat Genet}, volume = {52}, year = {2020}, month = {2020 02}, pages = {208-218}, abstract = {

Cancer genomes contain large numbers of somatic mutations but few of these mutations drive tumor development. Current approaches either identify driver genes on the basis of mutational recurrence or approximate the functional consequences of nonsynonymous mutations by using bioinformatic scores. Passenger mutations are enriched in characteristic nucleotide contexts, whereas driver mutations occur in functional positions, which are not necessarily surrounded by a particular nucleotide context. We observed that mutations in contexts that deviate from the characteristic contexts around passenger mutations provide a signal in favor of driver genes. We therefore developed a method that combines this feature with the signals traditionally used for driver-gene identification. We applied our method to whole-exome sequencing data from 11,873 tumor-normal pairs and identified 460 driver genes that clustered into 21 cancer-related pathways. Our study provides a resource of driver genes across 28 tumor types with additional driver genes identified according to mutations in unusual nucleotide contexts.

}, keywords = {Cluster Analysis, Computational Biology, Humans, Mutation, Neoplasms, Nucleotides, Proteins, Whole Exome Sequencing}, issn = {1546-1718}, doi = {10.1038/s41588-019-0572-y}, author = {Dietlein, Felix and Weghorn, Donate and Taylor-Weiner, Amaro and Richters, Andr{\'e} and Reardon, Brendan and Liu, David and Lander, Eric S and Van Allen, Eliezer M and Sunyaev, Shamil R} } @article {74, title = {Identifying causal variants and genes using functional genomics in specialized cell types and contexts.}, journal = {Hum Genet}, volume = {139}, year = {2020}, month = {2020 Jan}, pages = {95-102}, abstract = {

A central goal in human genetics is the identification of variants and genes that influence the risk of polygenic diseases. In the past decade, genome-wide association studies (GWAS) have identified tens of thousands of genetic loci associated with various diseases. Since the majority of such loci lie within non-coding regions and have many candidate variants in linkage disequilibrium, it has been challenging to accurately identify specific causal variants and genes. To aid in their discovery a variety of statistical and experimental approaches have been developed. These approaches often borrow information from functional genomics assays such as ATAC-seq, ChIP-seq and RNA-seq to annotate functional variants and identify regulatory relationships between variants and genes. While such approaches are powerful, given the diversity of cell types and environments, it is paramount to select disease-relevant contexts for follow-up analyses. In this review, we discuss the latest developments, challenges, and best practices for determining the causal mechanisms of polygenic disease risk variants with functional genomics data from specialized cell types.

}, keywords = {Cell Lineage, Genes, Genetic Predisposition to Disease, Genome, Human, Genome-Wide Association Study, Genomics, Humans, Polymorphism, Single Nucleotide, Quantitative Trait Loci}, issn = {1432-1203}, doi = {10.1007/s00439-019-02044-2}, author = {Liu, Boxiang and Montgomery, Stephen B} } @article {95, title = {Impact of admixture and ancestry on eQTL analysis and GWAS colocalization in GTEx.}, journal = {Genome Biol}, volume = {21}, year = {2020}, month = {2020 09 11}, pages = {233}, abstract = {

BACKGROUND: Population structure among study subjects may confound genetic association studies, and lack of proper correction can lead to spurious findings. The Genotype-Tissue Expression (GTEx) project largely contains individuals of European ancestry, but the v8 release also includes up to 15\% of individuals of non-European ancestry. Assessing ancestry-based adjustments in GTEx improves portability of this research across populations and further characterizes the impact of population structure on GWAS colocalization.

RESULTS: Here, we identify a subset of 117 individuals in GTEx (v8) with a high degree of population admixture and estimate genome-wide local ancestry. We perform genome-wide cis-eQTL mapping using admixed samples in seven tissues, adjusted by either global or local ancestry. Consistent with previous work, we observe improved power with local ancestry adjustment. At loci where the two adjustments produce different lead variants, we observe 31 loci (0.02\%) where a significant colocalization is called only with one eQTL ancestry adjustment method. Notably, both adjustments produce similar numbers of significant colocalizations within each of two different colocalization methods, COLOC and FINEMAP. Finally, we identify a small subset of eQTL-associated variants highly correlated with local ancestry, providing a resource to enhance functional follow-up.

CONCLUSIONS: We provide a local ancestry map for admixed individuals in the GTEx v8 release and describe the impact of ancestry and admixture on gene expression, eQTLs, and GWAS colocalization. While the majority of the results are concordant between local and global ancestry-based adjustments, we identify distinct advantages and disadvantages to each approach.

}, issn = {1474-760X}, doi = {10.1186/s13059-020-02113-0}, author = {Gay, Nicole R and Gloudemans, Michael and Antonio, Margaret L and Abell, Nathan S and Balliu, Brunilda and Park, YoSon and Martin, Alicia R and Musharoff, Shaila and Rao, Abhiram S and Aguet, Francois and Barbeira, Alvaro N and Bonazzola, Rodrigo and Hormozdiari, Farhad and Ardlie, Kristin G and Brown, Christopher D and Im, Hae Kyung and Lappalainen, Tuuli and Wen, Xiaoquan and Montgomery, Stephen B} } @article {113, title = {Improving the trans-ancestry portability of polygenic risk scores by prioritizing variants in predicted cell-type-specific regulatory elements.}, journal = {Nat Genet}, volume = {52}, year = {2020}, month = {2020 12}, pages = {1346-1354}, abstract = {

Poor trans-ancestry portability of polygenic risk scores is a consequence of Eurocentric genetic studies and limited knowledge of shared causal variants. Leveraging regulatory annotations may improve portability by prioritizing functional over tagging variants. We constructed a resource of 707 cell-type-specific IMPACT regulatory annotations by aggregating 5,345 epigenetic datasets to predict binding patterns of 142 transcription factors across 245 cell types. We then partitioned the common SNP heritability of 111 genome-wide association study summary statistics of European (average n ≈ 189,000) and East Asian (average n ≈ 157,000) origin. IMPACT annotations captured consistent SNP heritability between populations, suggesting prioritization of shared functional variants. Variant prioritization using IMPACT resulted in increased trans-ancestry portability of polygenic risk scores from Europeans to East Asians across all 21 phenotypes analyzed (49.9\% mean relative increase in R). Our study identifies a crucial role for functional annotations such as IMPACT to improve the trans-ancestry portability of genetic data.

}, keywords = {Asian Continental Ancestry Group, Base Sequence, Computational Biology, Enhancer Elements, Genetic, European Continental Ancestry Group, Gene Expression Regulation, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Models, Genetic, Molecular Sequence Annotation, Multifactorial Inheritance, Polymorphism, Single Nucleotide}, issn = {1546-1718}, doi = {10.1038/s41588-020-00740-8}, author = {Amariuta, Tiffany and Ishigaki, Kazuyoshi and Sugishita, Hiroki and Ohta, Tazro and Koido, Masaru and Dey, Kushal K and Matsuda, Koichi and Murakami, Yoshinori and Price, Alkes L and Kawakami, Eiryo and Terao, Chikashi and Raychaudhuri, Soumya} } @article {105, title = {Lung Function in African American Children with Asthma Is Associated with Novel Regulatory Variants of the KIT Ligand and Gene-By-Air-Pollution Interaction.}, journal = {Genetics}, volume = {215}, year = {2020}, month = {2020 07}, pages = {869-886}, abstract = {

Baseline lung function, quantified as forced expiratory volume in the first second of exhalation (FEV), is a standard diagnostic criterion used by clinicians to identify and classify lung diseases. Using whole-genome sequencing data from the National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine project, we identified a novel genetic association with FEV on chromosome 12 in 867 African American children with asthma ( = 1.26 {\texttimes} 10, β = 0.302). Conditional analysis within 1 Mb of the tag signal (rs73429450) yielded one major and two other weaker independent signals within this peak. We explored statistical and functional evidence for all variants in linkage disequilibrium with the three independent signals and yielded nine variants as the most likely candidates responsible for the association with FEV Hi-C data and expression QTL analysis demonstrated that these variants physically interacted with (KIT ligand, also known as ), and their minor alleles were associated with increased expression of the gene in nasal epithelial cells. Gene-by-air-pollution interaction analysis found that the candidate variant rs58475486 interacted with past-year ambient sulfur dioxide exposure ( = 0.003, β = 0.32). This study identified a novel protective genetic association with FEV, possibly mediated through , in African American children with asthma. This is the first study that has identified a genetic association between lung function and , which has established a role in orchestrating allergic inflammation in asthma.

}, issn = {1943-2631}, doi = {10.1534/genetics.120.303231}, author = {Mak, Angel C Y and Sajuthi, Satria and Joo, Jaehyun and Xiao, Shujie and Sleiman, Patrick M and White, Marquitta J and Lee, Eunice Y and Saef, Benjamin and Hu, Donglei and Gui, Hongsheng and Keys, Kevin L and Lurmann, Fred and Jain, Deepti and Abecasis, Goncalo and Kang, Hyun Min and Nickerson, Deborah A and Germer, Soren and Zody, Michael C and Winterkorn, Lara and Reeves, Catherine and Huntsman, Scott and Eng, Celeste and Salazar, Sandra and Oh, Sam S and Gilliland, Frank D and Chen, Zhanghua and Kumar, Rajesh and Mart{\'\i}nez, Fernando D and Wu, Ann Chen and Ziv, Elad and Hakonarson, Hakon and Himes, Blanca E and Williams, L Keoki and Seibold, Max A and Burchard, Esteban G} } @article {124, title = {Modeling epistasis in mice and yeast using the proportion of two or more distinct genetic backgrounds: Evidence for "polygenic epistasis".}, journal = {PLoS Genet}, volume = {16}, year = {2020}, month = {2020 10}, pages = {e1009165}, abstract = {

BACKGROUND: The majority of quantitative genetic models used to map complex traits assume that alleles have similar effects across all individuals. Significant evidence suggests, however, that epistatic interactions modulate the impact of many alleles. Nevertheless, identifying epistatic interactions remains computationally and statistically challenging. In this work, we address some of these challenges by developing a statistical test for polygenic epistasis that determines whether the effect of an allele is altered by the global genetic ancestry proportion from distinct progenitors.

RESULTS: We applied our method to data from mice and yeast. For the mice, we observed 49 significant genotype-by-ancestry interaction associations across 14 phenotypes as well as over 1,400 Bonferroni-corrected genotype-by-ancestry interaction associations for mouse gene expression data. For the yeast, we observed 92 significant genotype-by-ancestry interactions across 38 phenotypes. Given this evidence of epistasis, we test for and observe evidence of rapid selection pressure on ancestry specific polymorphisms within one of the cohorts, consistent with epistatic selection.

CONCLUSIONS: Unlike our prior work in human populations, we observe widespread evidence of ancestry-modified SNP effects, perhaps reflecting the greater divergence present in crosses using mice and yeast.

}, keywords = {Alleles, Animals, Epistasis, Genetic, Evolution, Molecular, Genotype, Humans, Mice, Models, Genetic, Multifactorial Inheritance, Phenotype, Quantitative Trait Loci, Saccharomyces cerevisiae, Selection, Genetic}, issn = {1553-7404}, doi = {10.1371/journal.pgen.1009165}, author = {Rau, Christoph D and Gonzales, Natalia M and Bloom, Joshua S and Park, Danny and Ayroles, Julien and Palmer, Abraham A and Lusis, Aldons J and Zaitlen, Noah} } @article {103, title = {Multi-Ethnic Genome-Wide Association Study of Decomposed Cardioelectric Phenotypes Illustrates Strategies to Identify and Characterize Evidence of Shared Genetic Effects for Complex Traits.}, journal = {Circ Genom Precis Med}, volume = {13}, year = {2020}, month = {2020 08}, pages = {e002680}, abstract = {

BACKGROUND: We examined how expanding electrocardiographic trait genome-wide association studies to include ancestrally diverse populations, prioritize more precise phenotypic measures, and evaluate evidence for shared genetic effects enabled the detection and characterization of loci.

METHODS: We decomposed 10 seconds, 12-lead electrocardiograms from 34 668 multi-ethnic participants (15\% Black; 30\% Hispanic/Latino) into 6 contiguous, physiologically distinct (P wave, PR segment, QRS interval, ST segment, T wave, and TP segment) and 2 composite, conventional (PR interval and QT interval) interval scale traits and conducted multivariable-adjusted, trait-specific univariate genome-wide association studies using 1000-G imputed single-nucleotide polymorphisms. Evidence of shared genetic effects was evaluated by aggregating meta-analyzed univariate results across the 6 continuous electrocardiographic traits using the combined phenotype adaptive sum of powered scores test.

RESULTS: We identified 6 novels (, and ) and 87 known loci (adaptive sum of powered score test <5{\texttimes}10). Lead single-nucleotide polymorphism rs3211938 at was common in Blacks (minor allele frequency=10\%), near monomorphic in European Americans, and had effects on the QT interval and TP segment that ranked among the largest reported to date for common variants. The other 5 novel loci were observed when evaluating the contiguous but not the composite electrocardiographic traits. Combined phenotype testing did not identify novel electrocardiographic loci unapparent using traditional univariate approaches, although this approach did assist with the characterization of known loci.

CONCLUSIONS: Despite including one-third as many participants as published electrocardiographic trait genome-wide association studies, our study identified 6 novel loci, emphasizing the importance of ancestral diversity and phenotype resolution in this era of ever-growing genome-wide association studies.

}, issn = {2574-8300}, doi = {10.1161/CIRCGEN.119.002680}, author = {Baldassari, Antoine R and Sitlani, Colleen M and Highland, Heather M and Arking, Dan E and Buyske, Steve and Darbar, Dawood and Gondalia, Rahul and Graff, Misa and Guo, Xiuqing and Heckbert, Susan R and Hindorff, Lucia A and Hodonsky, Chani J and Ida Chen, Yii-Der and Kaplan, Robert C and Peters, Ulrike and Post, Wendy and Reiner, Alex P and Rotter, Jerome I and Shohet, Ralph V and Seyerle, Amanda A and Sotoodehnia, Nona and Tao, Ran and Taylor, Kent D and Wojcik, Genevieve L and Yao, Jie and Kenny, Eimear E and Lin, Henry J and Soliman, Elsayed Z and Whitsel, Eric A and North, Kari E and Kooperberg, Charles and Avery, Christy L} } @article {106, title = {On Negative Heritability and Negative Estimates of Heritability.}, journal = {Genetics}, volume = {215}, year = {2020}, month = {2020 06}, pages = {343-357}, abstract = {

We consider the problem of interpreting negative maximum likelihood estimates of heritability that sometimes arise from popular statistical models of additive genetic variation. These may result from random noise acting on estimates of genuinely positive heritability, but we argue that they may also arise from misspecification of the standard additive mechanism that is supposed to justify the statistical procedure. Researchers should be open to the possibility that negative heritability estimates could reflect a real physical feature of the biological process from which the data were sampled.

}, issn = {1943-2631}, doi = {10.1534/genetics.120.303161}, author = {Steinsaltz, David and Dahl, Andy and Wachter, Kenneth W} } @article {93, title = {Neurophysiological and Genetic Findings in Patients With Juvenile Myoclonic Epilepsy.}, journal = {Front Integr Neurosci}, volume = {14}, year = {2020}, month = {2020}, pages = {45}, abstract = {

Objective: Transcranial magnetic stimulation (TMS), a non-invasive procedure, stimulates the cortex evaluating the central motor pathways. The response is called motor evoked potential (MEP). Polyphasia results when the response crosses the baseline more than twice (zero crossing). Recent research shows MEP polyphasia in patients with generalized genetic epilepsy (GGE) and their first-degree relatives compared with controls. Juvenile Myoclonic Epilepsy (JME), a GGE type, is not well studied regarding polyphasia. In our study, we assessed polyphasia appearance probability with TMS in JME patients, their healthy first-degree relatives and controls. Two genetic approaches were applied to uncover genetic association with polyphasia.

Methods: 20 JME patients, 23 first-degree relatives and 30 controls underwent TMS, obtaining 10-15 MEPs per participant. We evaluated MEP mean number of phases, proportion of MEP trials displaying polyphasia for each subject and variability between groups. Participants underwent whole exome sequencing (WES) via trio-based analysis and two-case scenario. Extensive bioinformatics analysis was applied.

Results: We identified increased polyphasia in patients (85\%) and relatives (70\%) compared to controls (47\%) and significantly higher mean number of zero crossings (i.e., occurrence of phases) (patients 1.49, relatives 1.46, controls 1.22; < 0.05). Trio-based analysis revealed a candidate polymorphism, p.Glu270del,in , in JME patients and their relatives presenting polyphasia. Sanger sequencing analysis in remaining participants showed no significant association. In two-case scenario, a machine learning approach was applied in variants identified from odds ratio analysis and risk prediction scores were obtained for polyphasia. The results revealed 61 variants of which none was associated with polyphasia. Risk prediction scores indeed showed lower probability for non-polyphasic subjects on having polyphasia and higher probability for polyphasic subjects on having polyphasia.

Conclusion: Polyphasia was present in JME patients and relatives in contrast to controls. Although no known clinical symptoms are linked to polyphasia this neurophysiological phenomenon is likely due to common cerebral electrophysiological abnormality. We did not discover direct association between genetic variants obtained and polyphasia. It is likely these genetic traits alone cannot provoke polyphasia, however, this predisposition combined with disturbed brain-electrical activity and tendency to generate seizures may increase the risk of developing polyphasia, mainly in patients and relatives.

}, issn = {1662-5145}, doi = {10.3389/fnint.2020.00045}, author = {Stefani, Stefani and Kousiappa, Ioanna and Nicolaou, Nicoletta and Papathanasiou, Eleftherios S and Oulas, Anastasis and Fanis, Pavlos and Neocleous, Vassos and Phylactou, Leonidas A and Spyrou, George M and Papacostas, Savvas S} } @article {125, title = {Operating characteristics of the rank-based inverse normal transformation for quantitative trait analysis in genome-wide association studies.}, journal = {Biometrics}, volume = {76}, year = {2020}, month = {2020 12}, pages = {1262-1272}, abstract = {

Quantitative traits analyzed in Genome-Wide Association Studies (GWAS) are often nonnormally distributed. For such traits, association tests based on standard linear regression are subject to reduced power and inflated type I error in finite samples. Applying the rank-based inverse normal transformation (INT) to nonnormally distributed traits has become common practice in GWAS. However, the different variations on INT-based association testing have not been formally defined, and guidance is lacking on when to use which approach. In this paper, we formally define and systematically compare the direct (D-INT) and indirect (I-INT) INT-based association tests. We discuss their assumptions, underlying generative models, and connections. We demonstrate that the relative powers of D-INT and I-INT depend on the underlying data generating process. Since neither approach is uniformly most powerful, we combine them into an adaptive omnibus test (O-INT). O-INT is robust to model misspecification, protects the type I error, and is well powered against a wide range of nonnormally distributed traits. Extensive simulations were conducted to examine the finite sample operating characteristics of these tests. Our results demonstrate that, for nonnormally distributed traits, INT-based tests outperform the standard untransformed association test, both in terms of power and type I error rate control. We apply the proposed methods to GWAS of spirometry traits in the UK Biobank. O-INT has been implemented in the R package RNOmni, which is available on~CRAN.

}, issn = {1541-0420}, doi = {10.1111/biom.13214}, author = {McCaw, Zachary R and Lane, Jacqueline M and Saxena, Richa and Redline, Susan and Lin, Xihong} } @article {76, title = {Phenome-based approach identifies RIC1-linked Mendelian syndrome through zebrafish models, biobank associations and clinical studies.}, journal = {Nat Med}, volume = {26}, year = {2020}, month = {2020 01}, pages = {98-109}, abstract = {

Discovery of genotype-phenotype relationships remains a major challenge in clinical medicine. Here, we combined three sources of phenotypic data to uncover a new mechanism for rare and common diseases resulting from collagen secretion deficits. Using a zebrafish genetic screen, we identified the ric1 gene as being essential for skeletal biology. Using a gene-based phenome-wide association study (PheWAS) in the EHR-linked BioVU biobank, we show that reduced genetically determined expression of RIC1 is associated with musculoskeletal and dental conditions. Whole-exome sequencing identified individuals homozygous-by-descent for a rare variant in RIC1 and, through a guided clinical re-evaluation, it was discovered that they share signs with the BioVU-associated phenome. We named this new Mendelian syndrome CATIFA (cleft lip, cataract, tooth abnormality, intellectual disability, facial dysmorphism, attention-deficit hyperactivity disorder) and revealed further disease mechanisms. This gene-based, PheWAS-guided approach can accelerate the discovery of clinically relevant disease phenome and associated biological mechanisms.

}, keywords = {Abnormalities, Multiple, Animals, Behavior, Animal, Biological Specimen Banks, Chondrocytes, Disease Models, Animal, Extracellular Matrix, Fibroblasts, Guanine Nucleotide Exchange Factors, Humans, Models, Biological, Musculoskeletal System, Osteogenesis, Phenomics, Phenotype, Procollagen, Protein Transport, Secretory Pathway, Syndrome, Zebrafish, Zebrafish Proteins}, issn = {1546-170X}, doi = {10.1038/s41591-019-0705-y}, author = {Unlu, Gokhan and Qi, Xinzi and Gamazon, Eric R and Melville, David B and Patel, Nisha and Rushing, Amy R and Hashem, Mais and Al-Faifi, Abdullah and Chen, Rui and Li, Bingshan and Cox, Nancy J and Alkuraya, Fowzan S and Knapik, Ela W} } @article {128, title = {A phenome-wide association study of 26 mendelian genes reveals phenotypic expressivity of common and rare variants within the general population.}, journal = {PLoS Genet}, volume = {16}, year = {2020}, month = {2020 11}, pages = {e1008802}, abstract = {

The clinical evaluation of a genetic syndrome relies upon recognition of a characteristic pattern of signs or symptoms to guide targeted genetic testing for confirmation of the diagnosis. However, individuals displaying a single phenotype of a complex syndrome may not meet criteria for clinical diagnosis or genetic testing. Here, we present a phenome-wide association study (PheWAS) approach to systematically explore the phenotypic expressivity of common and rare alleles in genes associated with four well-described syndromic diseases (Alagille (AS), Marfan (MS), DiGeorge (DS), and Noonan (NS) syndromes) in the general population. Using human phenotype ontology (HPO) terms, we systematically mapped 60 phenotypes related to AS, MS, DS and NS in 337,198 unrelated white British from the UK Biobank (UKBB) based on their hospital admission records, self-administrated questionnaires, and physiological measurements. We performed logistic regression adjusting for age, sex, and the first 5 genetic principal components, for each phenotype and each variant in the target genes (JAG1, NOTCH2 FBN1, PTPN1 and RAS-opathy genes, and genes in the 22q11.2 locus) and performed a gene burden test. Overall, we observed multiple phenotype-genotype correlations, such as the association between variation in JAG1, FBN1, PTPN11 and SOS2 with diastolic and systolic blood pressure; and pleiotropy among multiple variants in syndromic genes. For example, rs11066309 in PTPN11 was significantly associated with a lower body mass index, an increased risk of hypothyroidism and a smaller size for gestational age, all in concordance with NS-related phenotypes. Similarly, rs589668 in FBN1 was associated with an increase in body height and blood pressure, and a reduced body fat percentage as observed in Marfan syndrome. Our findings suggest that the spectrum of associations of common and rare variants in genes involved in syndromic diseases can be extended to individual phenotypes within the general population.

}, keywords = {Alagille Syndrome, Alleles, Biological Variation, Population, DiGeorge Syndrome, European Continental Ancestry Group, Female, Gene Frequency, Genetic Association Studies, Genetic Predisposition to Disease, Genetic Testing, Genetic Variation, Genome-Wide Association Study, Humans, Male, Marfan Syndrome, Noonan Syndrome, Phenotype, Polymorphism, Single Nucleotide, United Kingdom}, issn = {1553-7404}, doi = {10.1371/journal.pgen.1008802}, author = {Tcheandjieu, Catherine and Aguirre, Matthew and Gustafsson, Stefan and Saha, Priyanka and Potiny, Praneetha and Haendel, Melissa and Ingelsson, Erik and Rivas, Manuel A and Priest, James R} } @article {89, title = {A positively selected FBN1 missense variant reduces height in Peruvian individuals.}, journal = {Nature}, volume = {582}, year = {2020}, month = {2020 06}, pages = {234-239}, abstract = {

On average, Peruvian individuals are among the shortest in the world. Here we show that Native American ancestry is associated with reduced height in an ethnically diverse group of Peruvian individuals, and identify a population-specific, missense variant in the FBN1 gene (E1297G) that is significantly associated with lower height. Each copy of the minor allele (frequency of 4.7\%) reduces height by 2.2~cm (4.4~cm in homozygous individuals). To our knowledge, this is the largest effect size known for a common height-associated variant. FBN1 encodes the extracellular matrix protein fibrillin~1, which is a major structural component of microfibrils. We observed less densely packed fibrillin-1-rich microfibrils with irregular edges in the skin of individuals who were homozygous for G1297 compared with individuals who were homozygous for E1297. Moreover, we show that the E1297G locus is under positive selection in non-African populations, and that the E1297 variant shows subtle evidence of positive selection specifically within the Peruvian population. This variant is also significantly more frequent in coastal Peruvian populations than in populations from the Andes or the Amazon, which suggests that short stature might be the result of adaptation to factors that are associated with the coastal environment in Peru.

}, keywords = {Body Height, Female, Fibrillin-1, Gene Frequency, Genome-Wide Association Study, Heredity, Humans, Indians, South American, Male, Microfibrils, Mutation, Missense, Peru, Selection, Genetic}, issn = {1476-4687}, doi = {10.1038/s41586-020-2302-0}, author = {Asgari, Samira and Luo, Yang and Akbari, Ali and Belbin, Gillian M and Li, Xinyi and Harris, Daniel N and Selig, Martin and Bartell, Eric and Calderon, Roger and Slowikowski, Kamil and Contreras, Carmen and Yataco, Rosa and Galea, Jerome T and Jimenez, Judith and Coit, Julia M and Farro{\~n}ay, Chandel and Nazarian, Rosalynn M and O{\textquoteright}Connor, Timothy D and Dietz, Harry C and Hirschhorn, Joel N and Guio, Heinner and Lecca, Leonid and Kenny, Eimear E and Freeman, Esther E and Murray, Megan B and Raychaudhuri, Soumya} } @article {97, title = {PSCAN: Spatial scan tests guided by protein structures improve complex disease gene discovery and signal variant detection.}, journal = {Genome Biol}, volume = {21}, year = {2020}, month = {2020 08 26}, pages = {217}, abstract = {

Germline disease-causing variants are generally more spatially clustered in protein 3-dimensional structures than benign variants. Motivated by this tendency, we develop a fast and powerful protein-structure-based scan (PSCAN) approach for evaluating gene-level associations with complex disease and detecting signal variants. We validate PSCAN{\textquoteright}s performance on synthetic data and two real data sets for lipid traits and Alzheimer{\textquoteright}s disease. Our results demonstrate that PSCAN performs competitively with existing gene-level tests while increasing power and identifying more specific signal variant sets. Furthermore, PSCAN enables generation of hypotheses about the molecular basis for the associations in the context of protein structures and functional domains.

}, issn = {1474-760X}, doi = {10.1186/s13059-020-02121-0}, author = {Tang, Zheng-Zheng and Sliwoski, Gregory R and Chen, Guanhua and Jin, Bowen and Bush, William S and Li, Bingshan and Capra, John A} } @article {102, title = {Race, socioeconomic deprivation, and hospitalization for COVID-19 in English participants of a national biobank.}, journal = {Int J Equity Health}, volume = {19}, year = {2020}, month = {2020 07 06}, pages = {114}, abstract = {

Preliminary reports suggest that the Coronavirus Disease 2019 (COVID- 19) pandemic has led to disproportionate morbidity and mortality among historically disadvantaged populations. We investigate the racial and socioeconomic associations of COVID- 19 hospitalization among 418,794 participants of the UK Biobank, of whom 549 (0.13\%) had been hospitalized. Both Black participants (odds ratio 3.7; 95\%CI 2.5-5.3) and Asian participants (odds ratio 2.2; 95\%CI 1.5-3.2) were at substantially increased risk as compared to White participants. We further observed a striking gradient in COVID- 19 hospitalization rates according to the Townsend Deprivation Index - a composite measure of socioeconomic deprivation - and household income. Adjusting for socioeconomic factors and cardiorespiratory comorbidities led to only modest attenuation of the increased risk in Black participants, adjusted odds ratio 2.4 (95\%CI 1.5-3.7). These observations confirm and extend earlier preliminary and lay press reports of higher morbidity in non-White individuals in the context of a large population of participants in a national biobank. The extent to which this increased risk relates to variation in pre-existing comorbidities, differences in testing or hospitalization patterns, or additional disparities in social determinants of health warrants further study.

}, keywords = {Adult, Aged, Biological Specimen Banks, Continental Population Groups, Coronavirus Infections, COVID-19, Female, Health Status Disparities, Hospitalization, Humans, Male, Middle Aged, Pandemics, Pneumonia, Viral, Prospective Studies, Socioeconomic Factors, United Kingdom}, issn = {1475-9276}, doi = {10.1186/s12939-020-01227-y}, author = {Patel, Aniruddh P and Paranjpe, Manish D and Kathiresan, Nina P and Rivas, Manuel A and Khera, Amit V} } @article {90, title = {Rare protein-altering variants in ANGPTL7 lower intraocular pressure and protect against glaucoma.}, journal = {PLoS Genet}, volume = {16}, year = {2020}, month = {2020 05}, pages = {e1008682}, abstract = {

Protein-altering variants that are protective against human disease provide in vivo validation of therapeutic targets. Here we use genotyping data from UK Biobank (n = 337,151 unrelated White British individuals) and FinnGen (n = 176,899) to conduct a search for protein-altering variants conferring lower intraocular pressure (IOP) and protection against glaucoma. Through rare protein-altering variant association analysis, we find a missense variant in ANGPTL7 in UK Biobank (rs28991009, p.Gln175His, MAF = 0.8\%, genotyped in 82,253 individuals with measured IOP and an independent set of 4,238 glaucoma patients and 250,660 controls) that significantly lowers IOP (β = -0.53 and -0.67 mmHg for heterozygotes, -3.40 and -2.37 mmHg for homozygotes, P = 5.96 x 10-9 and 1.07 x 10-13 for corneal compensated and Goldman-correlated IOP, respectively) and is associated with 34\% reduced risk of glaucoma (P = 0.0062). In FinnGen, we identify an ANGPTL7 missense variant at a greater than 50-fold increased frequency in Finland compared with other populations (rs147660927, p.Arg220Cys, MAF Finland = 4.3\%), which was genotyped in 6,537 glaucoma patients and 170,362 controls and is associated with a 29\% lower glaucoma risk (P = 1.9 x 10-12 for all glaucoma types and also protection against its subtypes including exfoliation, primary open-angle, and primary angle-closure). We further find three rarer variants in UK Biobank, including a protein-truncating variant, which confer a strong composite lowering of IOP (P = 0.0012 and 0.24 for Goldman-correlated and corneal compensated IOP, respectively), suggesting the protective mechanism likely resides in the loss of interaction or function. Our results support inhibition or down-regulation of ANGPTL7 as a therapeutic strategy for glaucoma.

}, keywords = {Adult, Aged, Aged, 80 and over, Angiopoietin-like Proteins, Biological Specimen Banks, Case-Control Studies, Cohort Studies, Female, Finland, Gene Frequency, Genetic Predisposition to Disease, Genetics, Population, Genome-Wide Association Study, Glaucoma, Humans, Intraocular Pressure, Loss of Function Mutation, Male, Middle Aged, Mutation, Missense, Polymorphism, Single Nucleotide, United Kingdom}, issn = {1553-7404}, doi = {10.1371/journal.pgen.1008682}, author = {Tanigawa, Yosuke and Wainberg, Michael and Karjalainen, Juha and Kiiskinen, Tuomo and Venkataraman, Guhan and Lemmel{\"a}, Susanna and Turunen, Joni A and Graham, Robert R and Havulinna, Aki S and Perola, Markus and Palotie, Aarno and Daly, Mark J and Rivas, Manuel A} } @article {86, title = {A Robust Method Uncovers Significant Context-Specific Heritability in Diverse Complex Traits.}, journal = {Am J Hum Genet}, volume = {106}, year = {2020}, month = {2020 01 02}, pages = {71-91}, abstract = {

Gene-environment interactions (GxE) can be fundamental in applications ranging from functional genomics to precision medicine and is a conjectured source of substantial heritability. However, unbiased methods to profile GxE genome-wide are nascent and, as we show, cannot accommodate general environment variables, modest sample sizes, heterogeneous noise, and binary traits. To address this gap, we propose a simple, unifying mixed model for gene-environment interaction (GxEMM). In simulations and theory, we show that GxEMM can dramatically improve estimates and eliminate false positives when the assumptions of existing methods fail. We apply GxEMM to a range of human and model organism datasets and find broad evidence of context-specific genetic effects, including GxSex, GxAdversity, and GxDisease interactions across thousands of clinical and molecular phenotypes. Overall, GxEMM is broadly applicable for testing and quantifying polygenic interactions, which can be useful for explaining heritability and invaluable for determining biologically relevant environments.

}, keywords = {Adult, Animals, Computer Simulation, Female, Gene-Environment Interaction, Genetic Markers, Genome-Wide Association Study, Humans, Male, Mental Disorders, Middle Aged, Models, Genetic, Multifactorial Inheritance, Phenomics, Phenotype, Rats}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2019.11.015}, author = {Dahl, Andy and Nguyen, Khiem and Cai, Na and Gandal, Michael J and Flint, Jonathan and Zaitlen, Noah} } @article {110, title = {A Transcriptome-Wide Association Study Identifies Candidate Susceptibility Genes for Pancreatic Cancer Risk.}, journal = {Cancer Res}, volume = {80}, year = {2020}, month = {2020 10 15}, pages = {4346-4354}, abstract = {

Pancreatic cancer is among the most well-characterized cancer types, yet a large proportion of the heritability of pancreatic cancer risk remains unclear. Here, we performed a large transcriptome-wide association study to systematically investigate associations between genetically predicted gene expression in normal pancreas tissue and pancreatic cancer risk. Using data from 305 subjects of mostly European descent in the Genotype-Tissue Expression Project, we built comprehensive genetic models to predict normal pancreas tissue gene expression, modifying the UTMOST (unified test for molecular signatures). These prediction models were applied to the genetic data of 8,275 pancreatic cancer cases and 6,723 controls of European ancestry. Thirteen genes showed an association of genetically predicted expression with pancreatic cancer risk at an FDR <= 0.05, including seven previously reported genes (, and ) and six novel genes not yet reported for pancreatic cancer risk [6q27: OR (95\% confidence interval (CI), 1.54 (1.25-1.89); 13q12.13: OR (95\% CI), 0.78 (0.70-0.88); 14q24.3: OR (95\% CI), 1.35 (1.17-1.56); 17q12: OR (95\% CI), 6.49 (2.96-14.27); 17q21.1: OR (95\% CI), 1.94 (1.45-2.58); and 20p13: OR (95\% CI): 1.41 (1.20-1.66)]. The associations for 10 of these genes (, and ) remained statistically significant even after adjusting for risk SNPs identified in previous genome-wide association study. Collectively, this analysis identified novel candidate susceptibility genes for pancreatic cancer that warrant further investigation. SIGNIFICANCE: A transcriptome-wide association analysis identified seven previously reported and six novel candidate susceptibility genes for pancreatic cancer risk.

}, keywords = {Age Factors, Case-Control Studies, European Continental Ancestry Group, Female, Gene Expression Regulation, Neoplastic, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Male, Models, Genetic, Pancreatic Neoplasms, Polymorphism, Single Nucleotide}, issn = {1538-7445}, doi = {10.1158/0008-5472.CAN-20-1353}, author = {Liu, Duo and Zhou, Dan and Sun, Yanfa and Zhu, Jingjing and Ghoneim, Dalia and Wu, Chong and Yao, Qizhi and Gamazon, Eric R and Cox, Nancy J and Wu, Lang} } @article {94, title = {Transcriptomic signatures across human tissues identify functional rare genetic variation.}, journal = {Science}, volume = {369}, year = {2020}, month = {2020 09 11}, abstract = {

Rare genetic variants are abundant across the human genome, and identifying their function and phenotypic impact is a major challenge. Measuring aberrant gene expression has aided in identifying functional, large-effect rare variants (RVs). Here, we expanded detection of genetically driven transcriptome abnormalities by analyzing gene expression, allele-specific expression, and alternative splicing from multitissue RNA-sequencing data, and demonstrate that each signal informs unique classes of RVs. We developed Watershed, a probabilistic model that integrates multiple genomic and transcriptomic signals to predict variant function, validated these predictions in additional cohorts and through experimental assays, and used them to assess RVs in the UK Biobank, the Million Veterans Program, and the Jackson Heart Study. Our results link thousands of RVs to diverse molecular effects and provide evidence to associate RVs affecting the transcriptome with human traits.

}, keywords = {Genetic Variation, Genome, Human, Humans, Multifactorial Inheritance, Organ Specificity, Transcriptome}, issn = {1095-9203}, doi = {10.1126/science.aaz5900}, author = {Ferraro, Nicole M and Strober, Benjamin J and Einson, Jonah and Abell, Nathan S and Aguet, Francois and Barbeira, Alvaro N and Brandt, Margot and Bucan, Maja and Castel, Stephane E and Davis, Joe R and Greenwald, Emily and Hess, Gaelen T and Hilliard, Austin T and Kember, Rachel L and Kotis, Bence and Park, YoSon and Peloso, Gina and Ramdas, Shweta and Scott, Alexandra J and Smail, Craig and Tsang, Emily K and Zekavat, Seyedeh M and Ziosi, Marcello and Ardlie, Kristin G and Assimes, Themistocles L and Bassik, Michael C and Brown, Christopher D and Correa, Adolfo and Hall, Ira and Im, Hae Kyung and Li, Xin and Natarajan, Pradeep and Lappalainen, Tuuli and Mohammadi, Pejman and Montgomery, Stephen B and Battle, Alexis} } @article {109, title = {Type 2 and interferon inflammation regulate SARS-CoV-2 entry factor expression in the airway epithelium.}, journal = {Nat Commun}, volume = {11}, year = {2020}, month = {2020 10 12}, pages = {5139}, abstract = {

Coronavirus disease 2019 (COVID-19) is caused by SARS-CoV-2, an emerging virus that utilizes host proteins ACE2 and TMPRSS2 as entry factors. Understanding the factors affecting the pattern and levels of expression of these genes is important for deeper understanding of SARS-CoV-2 tropism and pathogenesis. Here we explore the role of genetics and co-expression networks in regulating these genes in the airway, through the analysis of nasal airway transcriptome data from 695 children. We identify expression quantitative trait loci for both ACE2 and TMPRSS2, that vary in frequency across world populations. We find TMPRSS2 is part of a mucus secretory network, highly upregulated by type 2 (T2) inflammation through the action of interleukin-13, and that the interferon response to respiratory viruses highly upregulates ACE2 expression. IL-13 and virus infection mediated effects on ACE2 expression were also observed at the protein level in the airway epithelium. Finally, we define airway responses to common coronavirus infections in children, finding that these infections generate host responses similar to other viral species, including upregulation of IL6 and ACE2. Our results reveal possible mechanisms influencing SARS-CoV-2 infectivity and COVID-19 clinical outcomes.

}, keywords = {Angiotensin-Converting Enzyme 2, Betacoronavirus, Child, Coronavirus Infections, COVID-19, Epithelial Cells, Gene Expression Profiling, Gene Expression Regulation, Genetic Variation, Host-Pathogen Interactions, Humans, Inflammation, Interferons, Interleukin-13, Middle Aged, Nasal Mucosa, Pandemics, Peptidyl-Dipeptidase A, Pneumonia, Viral, SARS-CoV-2, Serine Endopeptidases, Virus Internalization}, issn = {2041-1723}, doi = {10.1038/s41467-020-18781-2}, author = {Sajuthi, Satria P and DeFord, Peter and Li, Yingchun and Jackson, Nathan D and Montgomery, Michael T and Everman, Jamie L and Rios, Cydney L and Pruesse, Elmar and Nolin, James D and Plender, Elizabeth G and Wechsler, Michael E and Mak, Angel C Y and Eng, Celeste and Salazar, Sandra and Medina, Vivian and Wohlford, Eric M and Huntsman, Scott and Nickerson, Deborah A and Germer, Soren and Zody, Michael C and Abecasis, Goncalo and Kang, Hyun Min and Rice, Kenneth M and Kumar, Rajesh and Oh, Sam and Rodriguez-Santana, Jose and Burchard, Esteban G and Seibold, Max A} } @article {115, title = {Type 2 and interferon inflammation strongly regulate SARS-CoV-2 related gene expression in the airway epithelium.}, journal = {bioRxiv}, year = {2020}, month = {2020 Apr 10}, abstract = {

Coronavirus disease 2019 (COVID-19) outcomes vary from asymptomatic infection to death. This disparity may reflect different airway levels of the SARS-CoV-2 receptor, ACE2, and the spike protein activator, TMPRSS2. Here we explore the role of genetics and co-expression networks in regulating these genes in the airway, through the analysis of nasal airway transcriptome data from 695 children. We identify expression quantitative trait loci (eQTL) for both and , that vary in frequency across world populations. Importantly, we find is part of a mucus secretory network, highly upregulated by T2 inflammation through the action of interleukin-13, and that interferon response to respiratory viruses highly upregulates expression. Finally, we define airway responses to coronavirus infections in children, finding that these infections upregulate while also stimulating a more pronounced cytotoxic immune response relative to other respiratory viruses. Our results reveal mechanisms likely influencing SARS-CoV-2 infectivity and COVID-19 clinical outcomes.

}, doi = {10.1101/2020.04.09.034454}, author = {Sajuthi, Satria P and DeFord, Peter and Jackson, Nathan D and Montgomery, Michael T and Everman, Jamie L and Rios, Cydney L and Pruesse, Elmar and Nolin, James D and Plender, Elizabeth G and Wechsler, Michael E and Mak, Angel Cy and Eng, Celeste and Salazar, Sandra and Medina, Vivian and Wohlford, Eric M and Huntsman, Scott and Nickerson, Deborah A and Germer, Soren and Zody, Michael C and Abecasis, Goncalo and Kang, Hyun Min and Rice, Kenneth M and Kumar, Rajesh and Oh, Sam and Rodriguez-Santana, Jose and Burchard, Esteban G and Seibold, Max A} } @article {111, title = {A unified framework for joint-tissue transcriptome-wide association and Mendelian randomization analysis.}, journal = {Nat Genet}, volume = {52}, year = {2020}, month = {2020 11}, pages = {1239-1246}, abstract = {

Here, we present a joint-tissue imputation (JTI) approach and a Mendelian randomization framework for causal inference, MR-JTI. JTI borrows information across transcriptomes of different tissues, leveraging shared genetic regulation, to improve prediction performance in a tissue-dependent manner. Notably, JTI includes the single-tissue imputation method PrediXcan as a special case and outperforms other single-tissue approaches (the Bayesian sparse linear mixed model and Dirichlet process regression). MR-JTI models variant-level heterogeneity (primarily due to horizontal pleiotropy, addressing a major challenge of transcriptome-wide association study interpretation) and performs causal inference with type I error control. We make explicit the connection between the genetic architecture of gene expression and of complex traits and the suitability of Mendelian randomization as a causal inference strategy for transcriptome-wide association studies. We provide a resource of imputation models generated from GTEx and PsychENCODE panels. Analysis of biobanks and meta-analysis data, and extensive simulations show substantially improved statistical power, replication and causal mapping rate for JTI relative to existing approaches.

}, keywords = {Animals, Gene Expression Profiling, Genetic Association Studies, Humans, Lipoproteins, LDL, Mendelian Randomization Analysis, Mice, Models, Genetic, Multifactorial Inheritance, Predictive Value of Tests}, issn = {1546-1718}, doi = {10.1038/s41588-020-0706-2}, author = {Zhou, Dan and Jiang, Yi and Zhong, Xue and Cox, Nancy J and Liu, Chunyu and Gamazon, Eric R} } @article {101, title = {Whole-exome sequencing in adult patients with developmental and epileptic encephalopathy: It is never too late.}, journal = {Clin Genet}, volume = {98}, year = {2020}, month = {2020 11}, pages = {477-485}, abstract = {

Developmental and epileptic encephalopathies (DEE) encompass rare, sporadic neurodevelopmental disorders and usually with pediatric onset. As these conditions are characterized by marked clinical and genetic heterogeneity, whole-exome sequencing (WES) represents the strategy of choice for the molecular diagnosis. While its usefulness is well established in pediatric DEE cohorts, our study is aimed at assessing the WES feasibility in adult DEE patients who experienced a diagnostic odyssey prior to the advent of this technique. We analyzed exomes from 71 unrelated adult DEE patients, consecutively recruited from an Italian cohort for the EPI25 Project. All patients underwent accurate clinical and electrophysiological characterization. An overwhelming percentage (90.1\%) had already undergone negative genetic testing. Variants were classified according to the American College of Medical Genetics and Genomics guidelines. WES disclosed 24 (likely) pathogenic variants among 18 patients in epilepsy-related genes with either autosomal dominant, recessive or X-linked inheritance. Ten of these were novel. We obtained a diagnostic yield of 25.3\%, higher among patients with brain malformations, early-onset epilepsy and dysmorphisms. Despite a median diagnostic delay of 38.7 years, WES analysis provided the long-awaited diagnosis for 18 adult patients, which also had an impact on the clinical management of 50\% of them.

}, issn = {1399-0004}, doi = {10.1111/cge.13823}, author = {Minardi, Raffaella and Licchetta, Laura and Baroni, Maria Chiara and Pippucci, Tommaso and Stipa, Carlotta and Mostacci, Barbara and Severi, Giulia and Toni, Francesco and Bergonzini, Luca and Carelli, Valerio and Seri, Marco and Tinuper, Paolo and Bisulli, Francesca} } @article {88, title = {Whole-Genome and RNA Sequencing Reveal Variation and Transcriptomic Coordination in the Developing Human Prefrontal Cortex.}, journal = {Cell Rep}, volume = {31}, year = {2020}, month = {2020 04 07}, pages = {107489}, abstract = {

Gene expression levels vary across developmental stage, cell type, and region in the brain. Genomic variants also contribute to the variation in expression, and some neuropsychiatric disorder loci may exert their effects through this mechanism. To investigate these relationships, we present BrainVar, a unique resource of paired whole-genome and bulk tissue RNA sequencing from the dorsolateral prefrontal cortex of 176 individuals across prenatal and postnatal development. Here we identify common variants that alter gene expression (expression quantitative trait loci [eQTLs]) constantly across development or predominantly during prenatal or postnatal stages. Both "constant" and "temporal-predominant" eQTLs are enriched for loci associated with neuropsychiatric traits and disorders and colocalize with specific variants. Expression levels of more than 12,000 genes rise or fall in a concerted late-fetal transition, with the transitional genes enriched for cell-type-specific genes and neuropsychiatric risk loci, underscoring the importance of cataloging developmental trajectories in understanding cortical physiology and pathology.

}, keywords = {Base Sequence, Brain, Computational Biology, Databases, Genetic, Genetic Predisposition to Disease, Genetic Variation, Genome-Wide Association Study, Genomics, Humans, Phenotype, Polymorphism, Single Nucleotide, Prefrontal Cortex, Quantitative Trait Loci, Sequence Analysis, RNA, Transcriptome, Whole Exome Sequencing, Whole Genome Sequencing}, issn = {2211-1247}, doi = {10.1016/j.celrep.2020.03.053}, author = {Werling, Donna M and Pochareddy, Sirisha and Choi, Jinmyung and An, Joon-Yong and Sheppard, Brooke and Peng, Minshi and Li, Zhen and Dastmalchi, Claudia and Santpere, Gabriel and Sousa, Andr{\'e} M M and Tebbenkamp, Andrew T N and Kaur, Navjot and Gulden, Forrest O and Breen, Michael S and Liang, Lindsay and Gilson, Michael C and Zhao, Xuefang and Dong, Shan and Klei, Lambertus and Cicek, A Ercument and Buxbaum, Joseph D and Adle-Biassette, Homa and Thomas, Jean-Leon and Aldinger, Kimberly A and O{\textquoteright}Day, Diana R and Glass, Ian A and Zaitlen, Noah A and Talkowski, Michael E and Roeder, Kathryn and State, Matthew W and Devlin, Bernie and Sanders, Stephan J and Sestan, Nenad} } @article {54, title = {ACAT: A Fast and Powerful p Value Combination Method for Rare-Variant Analysis in Sequencing Studies.}, journal = {Am J Hum Genet}, volume = {104}, year = {2019}, month = {2019 Mar 07}, pages = {410-421}, abstract = {

Set-based analysis that jointly tests the association of variants in a group has emerged as a popular tool for analyzing rare and low-frequency variants in sequencing studies. The existing set-based tests can suffer significant power loss when only a small proportion of variants are causal, and their powers can be sensitive to the number, effect sizes, and effect directions of the causal variants and the choices of weights. Here we propose an aggregated Cauchy association test (ACAT), a general, powerful, and computationally efficient p value combination method for boosting power in sequencing studies. First, by combining variant-level p values, we use ACAT to construct a set-based test (ACAT-V) that is particularly powerful in the presence of only a small number of causal variants in a variant set. Second, by combining different variant-set-level p values, we use ACAT to construct an omnibus test (ACAT-O) that combines the strength of multiple complimentary set-based tests, including the burden test, sequence kernel association test (SKAT), and ACAT-V. Through analysis of extensively simulated data and the whole-genome sequencing data from the Atherosclerosis Risk in Communities (ARIC) study, we demonstrate that ACAT-V complements the SKAT and the burden test, and that ACAT-O has a substantially more robust and higher power than those of the alternative tests.

}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2019.01.002}, author = {Liu, Yaowu and Chen, Sixing and Li, Zilin and Morrison, Alanna C and Boerwinkle, Eric and Lin, Xihong} } @article {59, title = {Adjusting for Principal Components of Molecular Phenotypes Induces Replicating False Positives.}, journal = {Genetics}, volume = {211}, year = {2019}, month = {2019 04}, pages = {1179-1189}, abstract = {

High-throughput measurements of molecular phenotypes provide an unprecedented opportunity to model cellular processes and their impact on disease. These highly structured datasets are usually strongly confounded, creating false positives and reducing power. This has motivated many approaches based on principal components analysis (PCA) to estimate and correct for confounders, which have become indispensable elements of association tests between molecular phenotypes and both genetic and nongenetic factors. Here, we show that these correction approaches induce a bias, and that it persists for large sample sizes and replicates out-of-sample. We prove this theoretically for PCA by deriving an analytic, deterministic, and intuitive bias approximation. We assess other methods with realistic simulations, which show that perturbing any of several basic parameters can cause false positive rate (FPR) inflation. Our experiments show the bias depends on covariate and confounder sparsity, effect sizes, and their correlation. Surprisingly, when the covariate and confounder have [Formula: see text], standard two-step methods all have [Formula: see text]-fold FPR inflation. Our analysis informs best practices for confounder correction in genomic studies, and suggests many false discoveries have been made and replicated in some differential expression analyses.

}, keywords = {Animals, Genome-Wide Association Study, Humans, Models, Genetic, Phenotype, Principal Component Analysis, Quantitative Trait Loci, Reproducibility of Results}, issn = {1943-2631}, doi = {10.1534/genetics.118.301768}, author = {Dahl, Andy and Guillemot, Vincent and Mefford, Joel and Aschard, Hugues and Zaitlen, Noah} } @article {70, title = {Applicability of the Mutation-Selection Balance Model to Population Genetics of Heterozygous Protein-Truncating Variants in Humans.}, journal = {Mol Biol Evol}, volume = {36}, year = {2019}, month = {2019 08 01}, pages = {1701-1710}, abstract = {

The fate of alleles in the human population is believed to be highly affected by the stochastic force of genetic drift. Estimation of the strength of natural selection in humans generally necessitates a careful modeling of drift including complex effects of the population history and structure. Protein-truncating variants (PTVs) are expected to evolve under strong purifying selection and to have a relatively high per-gene mutation rate. Thus, it is appealing to model the population genetics of PTVs under a simple deterministic mutation-selection balance, as has been proposed earlier (Cassa et~al. 2017). Here, we investigated the limits of this approximation using both computer simulations and data-driven approaches. Our simulations rely on a model of demographic history estimated from 33,370 individual exomes of the Non-Finnish European subset of the ExAC data set (Lek et~al. 2016). Additionally, we compared the African and European subset of the ExAC study and analyzed de novo PTVs. We show that the mutation-selection balance model is applicable to the majority of human genes, but not to genes under the weakest selection.

}, issn = {1537-1719}, doi = {10.1093/molbev/msz092}, author = {Weghorn, Donate and Balick, Daniel J and Cassa, Christopher and Kosmicki, Jack A and Daly, Mark J and Beier, David R and Sunyaev, Shamil R} } @article {64, title = {A Bayesian framework that integrates multi-omics data and gene networks predicts risk genes from schizophrenia GWAS data.}, journal = {Nat Neurosci}, volume = {22}, year = {2019}, month = {2019 05}, pages = {691-699}, abstract = {

Genome-wide association studies (GWAS) have identified more than 100 schizophrenia (SCZ)-associated loci, but using these findings to illuminate disease biology remains a challenge. Here we present integrative risk gene selector (iRIGS), a Bayesian framework that integrates multi-omics data and gene networks to infer risk genes in GWAS loci. By applying iRIGS to SCZ GWAS data, we predicted a set of high-confidence risk genes, most of which are not the nearest genes to the GWAS index variants. High-confidence risk genes account for a significantly enriched heritability, as estimated by stratified linkage disequilibrium score regression. Moreover, high-confidence risk genes are predominantly expressed in brain tissues, especially prenatally, and are enriched for targets of approved drugs, suggesting opportunities to reposition existing drugs for SCZ. Thus, iRIGS can leverage accumulating functional genomics and GWAS data to advance our understanding of SCZ etiology and potential therapeutics.

}, keywords = {Animals, Bayes Theorem, Disease Models, Animal, Gene Regulatory Networks, Genetic Predisposition to Disease, Genome-Wide Association Study, Genomics, Humans, Mice, Risk Factors, Schizophrenia}, issn = {1546-1726}, doi = {10.1038/s41593-019-0382-7}, author = {Wang, Quan and Chen, Rui and Cheng, Feixiong and Wei, Qiang and Ji, Ying and Yang, Hai and Zhong, Xue and Tao, Ran and Wen, Zhexing and Sutcliffe, James S and Liu, Chunyu and Cook, Edwin H and Cox, Nancy J and Li, Bingshan} } @article {72, title = {A Bootstrap Method for Goodness of Fit and Model Selection with a Single Observed Network.}, journal = {Sci Rep}, volume = {9}, year = {2019}, month = {2019 Nov 13}, pages = {16674}, abstract = {

Network models are applied in numerous domains where data arise from systems of interactions among pairs of actors. Both statistical and mechanistic network models are increasingly capable of capturing various dependencies among these actors. Yet, these dependencies pose statistical challenges for analyzing such data, especially when the data set comprises only a single observation of one network, often leading to intractable likelihoods regardless of the modeling paradigm and limiting the application of existing statistical methods for networks. We explore a subsampling bootstrap procedure to serve as the basis for goodness of fit and model selection with a single observed network that circumvents the intractability of such likelihoods. Our approach is based on flexible resampling distributions formed from the single observed network, allowing for more nuanced and higher dimensional comparisons than point estimates of quantities of interest. We include worked examples for model selection, with simulation, and assessment of goodness of fit, with duplication-divergence model fits for yeast (S.cerevisiae) protein-protein interaction data from the literature. The proposed approach produces a flexible resampling distribution that can be based on any network statistics of one{\textquoteright}s choosing and can be employed for both statistical and mechanistic network models.

}, issn = {2045-2322}, doi = {10.1038/s41598-019-53166-6}, author = {Chen, Sixing and Onnela, Jukka-Pekka} } @article {66, title = {Components of genetic associations across 2,138 phenotypes in the UK Biobank highlight adipocyte biology.}, journal = {Nat Commun}, volume = {10}, year = {2019}, month = {2019 Sep 06}, pages = {4064}, abstract = {

Population-based biobanks with genomic and dense phenotype data provide opportunities for generating effective therapeutic hypotheses and understanding the genomic role in disease predisposition. To characterize latent components of genetic associations, we apply truncated singular value decomposition (DeGAs) to matrices of summary statistics derived from genome-wide association analyses across 2,138 phenotypes measured in 337,199 White British individuals in the UK Biobank study. We systematically identify key components of genetic associations and the contributions of variants, genes, and phenotypes to each component. As an illustration of the utility of the approach to inform downstream experiments, we report putative loss of function variants, rs114285050 (GPR151) and rs150090666 (PDE3B), that substantially contribute to obesity-related traits and experimentally demonstrate the role of these genes in adipocyte biology. Our approach to dissect components of genetic associations across the human phenome will accelerate biomedical hypothesis generation by providing insights on previously unexplored latent structures.

}, issn = {2041-1723}, doi = {10.1038/s41467-019-11953-9}, author = {Tanigawa, Yosuke and Li, Jiehan and Justesen, Johanne M and Horn, Heiko and Aguirre, Matthew and DeBoever, Christopher and Chang, Chris and Narasimhan, Balasubramanian and Lage, Kasper and Hastie, Trevor and Park, Chong Y and Bejerano, Gill and Ingelsson, Erik and Rivas, Manuel A} } @article {60, title = {De novo pattern discovery enables robust assessment of functional consequences of non-coding variants.}, journal = {Bioinformatics}, volume = {35}, year = {2019}, month = {2019 May 01}, pages = {1453-1460}, abstract = {

MOTIVATION: Given the complexity of genome regions, prioritize the functional effects of non-coding variants remains a challenge. Although several frameworks have been proposed for the evaluation of the functionality of non-coding variants, most of them used {\textquoteright}black boxes{\textquoteright} methods that simplify the task as the pathogenicity/benign classification problem, which ignores the distinct regulatory mechanisms of variants and leads to less desirable performance. In this study, we developed DVAR, an unsupervised framework that leverage various biochemical and evolutionary evidence to distinguish the gene regulatory categories of variants and assess their comprehensive functional impact simultaneously.

RESULTS: DVAR performed de novo pattern discovery in high-dimensional data and identified five regulatory clusters of non-coding variants. Leveraging the new insights into the multiple functional patterns, it measures both the between-class and the within-class functional implication of the variants to achieve accurate prioritization. Compared to other two-class learning methods, it showed improved performance in identification of clinically significant variants, fine-mapped GWAS variants, eQTLs and expression-modulating variants. Moreover, it has superior performance on disease causal variants verified by genome-editing (like CRISPR-Cas9), which could provide a pre-selection strategy for genome-editing technologies across the whole genome. Finally, evaluated in BioVU and UK Biobank, two large-scale DNA biobanks linked to complete electronic health records, DVAR demonstrated its effectiveness in prioritizing non-coding variants associated with medical phenotypes.

AVAILABILITY AND IMPLEMENTATION: The C++ and Python source codes, the pre-computed DVAR-cluster labels and DVAR-scores across the whole genome are available at https://www.vumc.org/cgg/dvar.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

}, issn = {1367-4811}, doi = {10.1093/bioinformatics/bty826}, author = {Yang, Hai and Chen, Rui and Wang, Quan and Wei, Qiang and Ji, Ying and Zheng, Guangze and Zhong, Xue and Cox, Nancy J and Li, Bingshan} } @article {65, title = {Diagnostic Algorithms to Study Post-Concussion Syndrome Using Electronic Health Records: Validating a Method to Capture an Important Patient Population.}, journal = {J Neurotrauma}, volume = {36}, year = {2019}, month = {2019 Jul 15}, pages = {2167-2177}, abstract = {

Post-concussion syndrome (PCS) is characterized by persistent cognitive, somatic, and emotional symptoms after a mild traumatic brain injury (mTBI). Genetic and other biological variables may contribute to PCS etiology, and the emergence of biobanks linked to electronic health records (EHRs) offers new opportunities for research on PCS. We sought to validate the EHR data of PCS patients by comparing two diagnostic algorithms deployed in the Vanderbilt University Medical Center de-identified database of 2.8 million patient EHRs. The algorithms identified individuals with PCS by: 1) natural language processing (NLP) of narrative text in the EHR combined with structured demographic, diagnostic, and encounter data; or 2) coded billing and procedure data. The predictive value of each algorithm was assessed, and cases and controls identified by each approach were compared on demographic and medical characteristics. The NLP algorithm identified 507 cases and 10,857 controls. The negative predictive value in controls was 78\% and the positive predictive value (PPV) in cases was 82\%. Conversely, the coded algorithm identified 1142 patients with two or more PCS billing codes and had a PPV of 76\%. Comparisons of PCS controls to both case groups recovered known epidemiology of PCS: cases were more likely than controls to be female and to have pre-morbid diagnoses of anxiety, migraine, and post-traumatic stress disorder. In contrast, controls and cases were equally likely to have attention deficit hyperactive disorder and learning disabilities, in accordance with the findings of recent systematic reviews of PCS risk factors. We conclude that EHRs are a valuable research tool for PCS. Ascertainment based on coded data alone had a predictive value comparable to an NLP algorithm, recovered known PCS risk factors, and maximized the number of included patients.

}, issn = {1557-9042}, doi = {10.1089/neu.2018.5916}, author = {Dennis, Jessica and Yengo-Kahn, Aaron M and Kirby, Paul and Solomon, Gary S and Cox, Nancy J and Zuckerman, Scott L} } @article {52, title = {Differential NOVA2-Mediated Splicing in Excitatory and Inhibitory Neurons Regulates Cortical Development and Cerebellar Function.}, journal = {Neuron}, volume = {101}, year = {2019}, month = {2019 Feb 20}, pages = {707-720.e5}, abstract = {

RNA-binding proteins (RBPs) regulate genetic diversity, but the degree to which they do so in individual cell types in~vivo is unknown. We developed NOVA2 cTag-crosslinking and immunoprecipitation (CLIP) to generate functional RBP-RNA maps from different neuronal populations in the mouse brain. Combining cell type datasets from Nova2-cTag and Nova2 conditional knockout mice revealed differential NOVA2 regulatory actions on alternative splicing (AS) on the same transcripts expressed in different neurons. This includes functional differences in transcripts expressed in cortical and cerebellar excitatory versus inhibitory neurons, where we find NOVA2 is required for, respectively, development of laminar structure, motor coordination, and synapse formation. We also find that NOVA2-regulated AS is coupled to NOVA2 regulation of intron retention in hundreds of transcripts, which can sequester the trans-acting splicing factor PTBP2. In summary, cTag-CLIP complements single-cell RNA sequencing (RNA-seq) studies by providing a means for understanding RNA regulation of functional cell diversity.

}, keywords = {Alternative Splicing, Animals, Antigens, Neoplasm, Cells, Cultured, Cerebellum, Cerebral Cortex, Excitatory Postsynaptic Potentials, Female, Inhibitory Postsynaptic Potentials, Male, Mice, Mice, Inbred C57BL, Nerve Tissue Proteins, Neuro-Oncological Ventral Antigen, Neurogenesis, Neurons, Polypyrimidine Tract-Binding Protein, RNA-Binding Proteins}, issn = {1097-4199}, doi = {10.1016/j.neuron.2018.12.019}, author = {Saito, Yuhki and Yuan, Yuan and Zucker-Scharff, Ilana and Fak, John J and Jereb, Sa{\v s}a and Tajima, Yoko and Licatalosi, Donny D and Darnell, Robert B} } @article {71, title = {Drug-Resistant Juvenile Myoclonic Epilepsy: Misdiagnosis of Progressive Myoclonus Epilepsy.}, journal = {Front Neurol}, volume = {10}, year = {2019}, month = {2019}, pages = {946}, abstract = {

Juvenile myoclonic epilepsy (JME) is a common epilepsy syndrome characterized by bilateral myoclonic and tonic-clonic seizures typically starting in adolescence and responding well to medication. Misdiagnosis of a more severe progressive myoclonus epilepsy (PME) as JME has been suggested as a cause of drug-resistance. Medical records of the Epilepsy Center Hessen-Marburg between 2005 and 2014 were automatically selected using keywords and manually reviewed regarding the presence of a JME diagnosis at any timepoint. The identified patients were evaluated regarding seizure outcome and drug resistance according to ILAE criteria. 87/168 identified JME patients were seizure-free at last follow-up including 61 drug-responsive patients (group NDR). Seventy-eight patients were not seizure-free including 26 drug-resistant patients (group DR). Valproate was the most efficacious AED. The JME diagnosis was revised in 7 patients of group DR including 6 in whom the diagnosis had already been questioned or revised during clinical follow-up. One of these was finally diagnosed with PME (genetically confirmed Lafora disease) based on genetic testing. She was initially reviewed at age 29 yrs and considered to be inconsistent with PME. Intellectual disability ( = 0.025), cognitive impairment ( < 0.001), febrile seizures in first-degree relatives ( = 0.023) and prominent dialeptic seizures ( = 0.009) where significantly more frequent in group DR. Individuals with PME are rarely found among drug-resistant alleged JME patients in a tertiary epilepsy center. Even a very detailed review by experienced epileptologists may not identify the presence of PME before the typical features evolve underpinning the need for early genetic testing in drug-resistant JME patients.

}, issn = {1664-2295}, doi = {10.3389/fneur.2019.00946}, author = {Martin, Sarah and Strzelczyk, Adam and Lindlar, Silvia and Krause, Kristina and Reif, Philipp S and Menzler, Katja and Chiocchetti, Andreas G and Rosenow, Felix and Knake, Susanne and Klein, Karl Martin} } @article {50, title = {Efficient Variant Set Mixed Model Association Tests for Continuous and Binary Traits in Large-Scale Whole-Genome Sequencing Studies.}, journal = {Am J Hum Genet}, volume = {104}, year = {2019}, month = {2019 Feb 07}, pages = {260-274}, abstract = {

With advances in whole-genome sequencing (WGS) technology, more advanced statistical methods for testing genetic association with rare variants are being developed. Methods in which variants are grouped for analysis are also known as variant-set, gene-based, and aggregate unit tests. The burden test and sequence kernel association test (SKAT) are two widely used variant-set tests, which were originally developed for samples of unrelated individuals and later have been extended to family data with known pedigree structures. However, computationally efficient and powerful variant-set tests are needed to make analyses tractable in large-scale WGS studies with complex study samples. In this paper, we propose the variant-set mixed model association tests (SMMAT) for continuous and binary traits using the generalized linear mixed model framework. These tests can be applied to large-scale WGS studies involving samples with population structure and relatedness, such as in the National Heart, Lung, and Blood Institute{\textquoteright}s Trans-Omics for Precision Medicine (TOPMed) program. SMMATs share the same null model for different variant sets, and a virtue of this null model, which includes covariates only, is that it needs to be fit only once for all tests in each genome-wide analysis. Simulation studies show that all the proposed SMMATs correctly control type I error rates for both continuous and binary traits in the presence of population structure and relatedness. We also illustrate our tests in a real data example of analysis of plasma fibrinogen levels in the TOPMed program (n = 23,763), using the Analysis Commons, a cloud-based computing platform.

}, keywords = {Chromosomes, Human, Pair 4, Cloud Computing, Female, Fibrinogen, Genetic Association Studies, Genetics, Population, Humans, Male, Models, Genetic, National Heart, Lung, and Blood Institute (U.S.), Precision Medicine, Research Design, Time Factors, United States, Whole Genome Sequencing}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2018.12.012}, author = {Chen, Han and Huffman, Jennifer E and Brody, Jennifer A and Wang, Chaolong and Lee, Seunggeun and Li, Zilin and Gogarten, Stephanie M and Sofer, Tamar and Bielak, Lawrence F and Bis, Joshua C and Blangero, John and Bowler, Russell P and Cade, Brian E and Cho, Michael H and Correa, Adolfo and Curran, Joanne E and de Vries, Paul S and Glahn, David C and Guo, Xiuqing and Johnson, Andrew D and Kardia, Sharon and Kooperberg, Charles and Lewis, Joshua P and Liu, Xiaoming and Mathias, Rasika A and Mitchell, Braxton D and O{\textquoteright}Connell, Jeffrey R and Peyser, Patricia A and Post, Wendy S and Reiner, Alex P and Rich, Stephen S and Rotter, Jerome I and Silverman, Edwin K and Smith, Jennifer A and Vasan, Ramachandran S and Wilson, James G and Yanek, Lisa R and Redline, Susan and Smith, Nicholas L and Boerwinkle, Eric and Borecki, Ingrid B and Cupples, L Adrienne and Laurie, Cathy C and Morrison, Alanna C and Rice, Kenneth M and Lin, Xihong} } @article {42, title = {Error-prone bypass of DNA lesions during lagging-strand replication is a common source of germline and cancer mutations.}, journal = {Nat Genet}, volume = {51}, year = {2019}, month = {2019 Jan}, pages = {36-41}, abstract = {

Studies in experimental systems have identified a multitude of mutational mechanisms including DNA replication infidelity and DNA damage followed by inefficient repair or replicative bypass. However, the relative contributions of these mechanisms to human germline mutation remain unknown. Here, we show that error-prone damage bypass on the lagging strand plays a major role in human mutagenesis. Transcription-coupled DNA repair removes lesions on the transcribed strand; lesions on the non-transcribed strand are preferentially converted into mutations. In human polymorphism we detect a striking similarity between mutation types predominant on the non-transcribed strand and on the strand lagging during replication. Moreover, damage-induced mutations in cancers accumulate asymmetrically with respect to the direction of replication, suggesting that DNA lesions are resolved asymmetrically. We experimentally demonstrate that replication delay greatly attenuates the mutagenic effect of ultraviolet irradiation, confirming that replication converts DNA damage into mutations. We estimate that at least 10\% of human mutations arise due to DNA damage.

}, keywords = {Cells, Cultured, DNA, DNA Damage, DNA Repair, DNA Replication, Germ-Line Mutation, Humans, Mutagenesis, Neoplasms, Polymorphism, Single Nucleotide, Transcription, Genetic}, issn = {1546-1718}, doi = {10.1038/s41588-018-0285-7}, author = {Seplyarskiy, Vladimir B and Akkuratov, Evgeny E and Akkuratova, Natalia and Andrianova, Maria A and Nikolaev, Sergey I and Bazykin, Georgii A and Adameyko, Igor and Sunyaev, Shamil R} } @article {49, title = {An evolutionary compass for detecting signals of polygenic selection and mutational bias.}, journal = {Evol Lett}, volume = {3}, year = {2019}, month = {2019 Feb}, pages = {69-79}, abstract = {

Selection and mutation shape the genetic variation underlying~human traits, but the specific evolutionary mechanisms driving~complex trait variation are largely unknown. We developed a statistical method that uses polarized genome-wide association study (GWAS) summary statistics from a single population to detect signals of mutational bias and selection. We found evidence for nonneutral signals on variation underlying several traits (body mass index [BMI], schizophrenia, Crohn{\textquoteright}s disease, educational attainment, and height). We then used simulations that incorporate simultaneous negative and positive selection to show that these signals are consistent with mutational bias and shifts in the fitness-phenotype relationship, but not stabilizing selection or mutational bias alone. We additionally replicate two of our top three signals (BMI and educational attainment) in an external cohort, and show that population stratification may have confounded GWAS summary statistics for height in the GIANT cohort. Our results provide a flexible and powerful framework for evolutionary analysis of complex phenotypes in humans and other species, and offer insights into the evolutionary mechanisms driving variation in human polygenic~traits.

}, issn = {2056-3744}, doi = {10.1002/evl3.97}, author = {Uricchio, Lawrence H and Kitano, Hugo C and Gusev, Alexander and Zaitlen, Noah A} } @article {73, title = {Exome sequencing reveals a high prevalence of BRCA1 and BRCA2 founder variants in a diverse population-based biobank.}, journal = {Genome Med}, volume = {12}, year = {2019}, month = {2019 Dec 31}, pages = {2}, abstract = {

BACKGROUND: Pathogenic variants in BRCA1 and BRCA2 (BRCA1/2) lead to increased risk of breast, ovarian, and other cancers, but most variant-positive individuals in the general population are unaware of their risk, and little is known about prevalence in non-European populations. We investigated BRCA1/2 prevalence and impact in the electronic health record (EHR)-linked BioMe Biobank in New York City.

METHODS: Exome sequence data from 30,223 adult BioMe participants were evaluated for pathogenic variants in BRCA1/2. Prevalence estimates were made in population groups defined by genetic ancestry and self-report. EHR data were used to evaluate clinical characteristics of variant-positive individuals.

RESULTS: There were 218 (0.7\%) individuals harboring expected pathogenic variants, resulting in an overall prevalence of 1 in 139. The highest prevalence was in individuals with Ashkenazi Jewish (AJ; 1 in 49), Filipino and other~Southeast Asian (1 in 81), and non-AJ European (1 in 103) ancestry. Among 218 variant-positive individuals, 112 (51.4\%) harbored known founder variants: 80 had AJ founder variants (BRCA1 c.5266dupC and c.68_69delAG, and BRCA2 c.5946delT), 8 had a Puerto Rican founder variant (BRCA2 c.3922G>T), and 24 had one of 19 other founder variants. Non-European populations were more likely to harbor BRCA1/2 variants that were not classified in ClinVar or that had uncertain or conflicting evidence for pathogenicity (uncertain/conflicting). Within mixed ancestry populations, such as Hispanic/Latinos with genetic ancestry from Africa, Europe, and the Americas, there was a strong correlation between the proportion of African genetic ancestry and the likelihood of harboring an uncertain/conflicting variant. Approximately 28\% of variant-positive individuals had a personal history, and 45\% had a personal or family history of BRCA1/2-associated cancers. Approximately 27\% of variant-positive individuals had prior clinical genetic testing for BRCA1/2. However, individuals with AJ founder variants were twice as likely to have had a clinical test (39\%) than those with other pathogenic variants (20\%).

CONCLUSIONS: These findings deepen our knowledge about BRCA1/2 variants and associated cancer risk in diverse populations, indicate a gap in knowledge about potential cancer-related variants in non-European populations, and suggest that genomic screening in diverse patient populations may be an effective tool to identify at-risk individuals.

}, issn = {1756-994X}, doi = {10.1186/s13073-019-0691-1}, author = {Abul-Husn, Noura S and Soper, Emily R and Odgis, Jacqueline A and Cullina, Sinead and Bobo, Dean and Moscati, Arden and Rodriguez, Jessica E and Loos, Ruth J F and Cho, Judy H and Belbin, Gillian M and Suckiel, Sabrina A and Kenny, Eimear E} } @article {67, title = {Extreme Polygenicity of Complex Traits Is Explained by Negative Selection.}, journal = {Am J Hum Genet}, volume = {105}, year = {2019}, month = {2019 Sep 05}, pages = {456-476}, abstract = {

Complex traits and common diseases are extremely polygenic, their heritability spread across thousands of loci. One possible explanation is that thousands of genes and loci have similarly important biological effects when mutated. However, we hypothesize that for most complex traits, relatively few genes and loci are critical, and negative selection-purging large-effect mutations in these regions-leaves behind common-variant associations in thousands of less critical regions instead. We refer to this phenomenon as flattening. To quantify its effects, we introduce a mathematical definition of polygenicity, the effective number of independently associated SNPs (M), which describes how evenly the heritability of a trait is spread across the genome. We developed a method, stratified LD fourth moments regression (S-LD4M), to estimate M, validating that it produces robust estimates in simulations. Analyzing 33 complex traits (average N = 361k), we determined that heritability is spread \~{}4{\texttimes} more evenly among common SNPs than among low-frequency SNPs. This difference, together with evolutionary modeling of new mutations, suggests that complex traits would be orders of magnitude less polygenic if not for the influence of negative selection. We also determined that heritability is spread more evenly within functionally important regions in proportion to their heritability enrichment; functionally important regions do not harbor common SNPs with greatly increased causal effect sizes, due to selective constraint. Our results suggest that for most complex traits, the genes and loci with the most critical biological effects often differ from those with the strongest common-variant associations.

}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2019.07.003}, author = {O{\textquoteright}Connor, Luke J and Schoech, Armin P and Hormozdiari, Farhad and Gazal, Steven and Patterson, Nick and Price, Alkes L} } @article {62, title = {Genome sequencing analysis identifies Epstein-Barr virus subtypes associated with high risk of nasopharyngeal carcinoma.}, journal = {Nat Genet}, volume = {51}, year = {2019}, month = {2019 Jul}, pages = {1131-1136}, abstract = {

Epstein-Barr virus (EBV) infection is ubiquitous worldwide and is associated with multiple cancers, including nasopharyngeal carcinoma (NPC). The importance of EBV viral genomic variation in NPC development and its striking epidemic in southern China has been poorly explored. Through large-scale genome sequencing of 270 EBV isolates and two-stage association study of EBV isolates from China, we identify two non-synonymous EBV variants within BALF2 that are strongly associated with the risk of NPC (odds ratio (OR) = 8.69, P = 9.69 {\texttimes} 10 for SNP 162476_C; OR = 6.14, P = 2.40 {\texttimes} 10 for SNP 163364_T). The cumulative effects of these variants contribute to 83\% of the overall risk of NPC in southern China. Phylogenetic analysis of the risk variants reveals a unique origin in Asia, followed by clonal expansion in NPC-endemic regions. Our results provide novel insights into the NPC endemic in southern China and also enable the identification of high-risk individuals for NPC prevention.

}, issn = {1546-1718}, doi = {10.1038/s41588-019-0436-5}, author = {Xu, Miao and Yao, Youyuan and Chen, Hui and Zhang, Shanshan and Cao, Su-Mei and Zhang, Zhe and Luo, Bing and Liu, Zhiwei and Li, Zilin and Xiang, Tong and He, Guiping and Feng, Qi-Sheng and Chen, Li-Zhen and Guo, Xiang and Jia, Wei-Hua and Chen, Ming-Yuan and Zhang, Xiao and Xie, Shang-Hang and Peng, Roujun and Chang, Ellen T and Pedergnana, Vincent and Feng, Lin and Bei, Jin-Xin and Xu, Rui-Hua and Zeng, Mu-Sheng and Ye, Weimin and Adami, Hans-Olov and Lin, Xihong and Zhai, Weiwei and Zeng, Yi-Xin and Liu, Jianjun} } @article {41, title = {Global Biobank Engine: enabling genotype-phenotype browsing for biobank summary statistics.}, journal = {Bioinformatics}, volume = {35}, year = {2019}, month = {2019 Jul 15}, pages = {2495-2497}, abstract = {

SUMMARY: Large biobanks linking phenotype to genotype have led to an explosion of genetic association studies across a wide range of phenotypes. Sharing the knowledge generated by these resources with the scientific community remains a challenge due to patient privacy and the vast amount of data. Here, we present Global Biobank Engine (GBE), a web-based tool that enables exploration of the relationship between genotype and phenotype in biobank cohorts, such as the UK Biobank. GBE supports browsing for results from genome-wide association studies, phenome-wide association studies, gene-based tests and genetic correlation between phenotypes. We envision GBE as a platform that facilitates the dissemination of summary statistics from biobanks to the scientific and clinical communities.

AVAILABILITY AND IMPLEMENTATION: GBE currently hosts data from the UK Biobank and can be found freely available at biobankengine.stanford.edu.

}, keywords = {Biological Specimen Banks, Genome-Wide Association Study, Genotype, Humans, Phenomics, Phenotype}, issn = {1367-4811}, doi = {10.1093/bioinformatics/bty999}, author = {McInnes, Gregory and Tanigawa, Yosuke and DeBoever, Chris and Lavertu, Adam and Olivieri, Julia Eve and Aguirre, Matthew and Rivas, Manuel A} } @article {56, title = {GRIK5 Genetically Regulated Expression Associated with Eye and Vascular Phenomes: Discovery through Iteration among Biobanks, Electronic Health Records, and Zebrafish.}, journal = {Am J Hum Genet}, volume = {104}, year = {2019}, month = {2019 Mar 07}, pages = {503-519}, abstract = {

Although the use of model systems for studying the mechanism of mutations that have a large effect is common, we highlight here the ways that zebrafish-model-system studies of a gene, GRIK5, that contributes to the polygenic liability to develop eye diseases have helped to illuminate a mechanism that implicates vascular biology in eye disease. A gene-expression prediction derived from a reference transcriptome panel applied to BioVU, a large electronic health record (EHR)-linked biobank at Vanderbilt University Medical Center, implicated reduced GRIK5 expression in diverse eye diseases. We tested the function of GRIK5 by depletion of its ortholog in zebrafish, and we observed reduced blood vessel numbers and integrity in the eye and increased vascular permeability. Analyses of EHRs in >2.6 million Vanderbilt subjects revealed significant comorbidity of eye and vascular diseases (relative risks 2-15); this comorbidity was confirmed in 150 million individuals from a large insurance claims dataset. Subsequent studies in >60,000 genotyped BioVU participants confirmed the association of reduced genetically predicted expression of GRIK5 with comorbid vascular and eye diseases. Our studies pioneer an approach that allows a rapid iteration of the discovery of gene-phenotype relationships to the primary genetic mechanism contributing to the pathophysiology of human disease. Our findings also add dimension to the understanding of the biology driven by glutamate receptors such as GRIK5 (also referred to as GLUK5 in protein form) and to mechanisms contributing to human eye diseases.

}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2019.01.017}, author = {Unlu, Gokhan and Gamazon, Eric R and Qi, Xinzi and Levic, Daniel S and Bastarache, Lisa and Denny, Joshua C and Roden, Dan M and Mayzus, Ilya and Breyer, Max and Zhong, Xue and Konkashbaev, Anuar I and Rzhetsky, Andrey and Knapik, Ela W and Cox, Nancy J} } @article {63, title = {Identification of rare-disease genes using blood transcriptome sequencing and large control cohorts.}, journal = {Nat Med}, volume = {25}, year = {2019}, month = {2019 06}, pages = {911-919}, abstract = {

It is estimated that 350 million individuals worldwide suffer from rare diseases, which are predominantly caused by mutation in a single gene. The current molecular diagnostic rate is estimated at 50\%, with whole-exome sequencing (WES) among the most successful approaches. For patients in whom WES is uninformative, RNA sequencing (RNA-seq) has shown diagnostic utility in specific tissues and diseases. This includes muscle biopsies from patients with undiagnosed rare muscle disorders, and cultured fibroblasts from patients with mitochondrial disorders. However, for many individuals, biopsies are not performed for clinical care, and tissues are difficult to access. We sought to assess the utility of RNA-seq from blood as a diagnostic tool for rare diseases of different pathophysiologies. We generated whole-blood RNA-seq from 94 individuals with undiagnosed rare diseases spanning 16 diverse disease categories. We developed a robust approach to compare data from these individuals with large sets of RNA-seq data for controls (n = 1,594 unrelated controls and n = 49 family members) and demonstrated the impacts of expression, splicing, gene and variant filtering strategies on disease gene identification. Across our cohort, we observed that RNA-seq yields a 7.5\% diagnostic rate, and an additional 16.7\% with improved candidate gene resolution.

}, keywords = {Acid Ceramidase, Case-Control Studies, Child, Child, Preschool, Cohort Studies, Female, Genetic Variation, Humans, Male, Models, Genetic, Mutation, Oxidoreductases Acting on CH-CH Group Donors, Potassium Channels, Rare Diseases, RNA, RNA Splicing, Sequence Analysis, RNA, Whole Exome Sequencing}, issn = {1546-170X}, doi = {10.1038/s41591-019-0457-8}, author = {Fr{\'e}sard, Laure and Smail, Craig and Ferraro, Nicole M and Teran, Nicole A and Li, Xin and Smith, Kevin S and Bonner, Devon and Kernohan, Kristin D and Marwaha, Shruti and Zappala, Zachary and Balliu, Brunilda and Davis, Joe R and Liu, Boxiang and Prybol, Cameron J and Kohler, Jennefer N and Zastrow, Diane B and Reuter, Chloe M and Fisk, Dianna G and Grove, Megan E and Davidson, Jean M and Hartley, Taila and Joshi, Ruchi and Strober, Benjamin J and Utiramerur, Sowmithri and Lind, Lars and Ingelsson, Erik and Battle, Alexis and Bejerano, Gill and Bernstein, Jonathan A and Ashley, Euan A and Boycott, Kym M and Merker, Jason D and Wheeler, Matthew T and Montgomery, Stephen B} } @article {55, title = {Methods for the Analysis and Interpretation for Rare Variants Associated with Complex Traits.}, journal = {Curr Protoc Hum Genet}, volume = {101}, year = {2019}, month = {2019 04}, pages = {e83}, abstract = {

With the advent of Next Generation Sequencing (NGS) technologies, whole genome and whole exome DNA sequencing has become affordable for routine genetic studies. Coupled with improved genotyping arrays and genotype imputation methodologies, it is increasingly feasible to obtain rare genetic variant information in large datasets. Such datasets allow researchers to gain a more complete understanding of the genetic architecture of complex traits caused by rare variants. State-of-the-art statistical methods for the statistical genetics analysis of sequence-based association, including efficient algorithms for association analysis in biobank-scale datasets, gene-association tests, meta-analysis, fine mapping methods that integrate functional genomic dataset, and phenome-wide association studies (PheWAS), are reviewed here. These methods are expected to be highly useful for next generation statistical genetics analysis in the era of precision medicine. {\textcopyright} 2019 by John Wiley \& Sons, Inc.

}, keywords = {Algorithms, Genetic Predisposition to Disease, Genome, Human, Genome-Wide Association Study, Genotype, High-Throughput Nucleotide Sequencing, Humans, Multifactorial Inheritance, Phenotype, Polymorphism, Single Nucleotide, Whole Exome Sequencing, Whole Genome Sequencing}, issn = {1934-8258}, doi = {10.1002/cphg.83}, author = {Weissenkampen, J Dylan and Jiang, Yu and Eckert, Scott and Jiang, Bibo and Li, Bingshan and Liu, Dajiang J} } @article {51, title = {A multi-task convolutional deep neural network for variant calling in single molecule sequencing.}, journal = {Nat Commun}, volume = {10}, year = {2019}, month = {2019 Mar 01}, pages = {998}, abstract = {

The accurate identification of DNA sequence variants is an important, but challenging task in genomics. It is particularly difficult for single molecule sequencing, which has a per-nucleotide error rate of ~5-15\%. Meeting this demand, we developed Clairvoyante, a multi-task five-layer convolutional neural network model for predicting variant type (SNP or indel), zygosity, alternative allele and indel length from aligned reads. For the well-characterized NA12878 human sample, Clairvoyante achieves 99.67, 95.78, 90.53\% F1-score on 1KP common variants, and 98.65, 92.57, 87.26\% F1-score for whole-genome analysis, using Illumina, PacBio, and Oxford Nanopore data, respectively. Training on a second human sample shows Clairvoyante is sample agnostic and finds variants in less than 2 h on a standard server. Furthermore, we present 3,135 variants that are missed using Illumina but supported independently by both PacBio and Oxford Nanopore reads. Clairvoyante is available open-source ( https://github.com/aquaskyline/Clairvoyante ), with modules to train, utilize and visualize the model.

}, keywords = {Base Sequence, Computational Biology, DNA Mutational Analysis, Genome, Human, Genome-Wide Association Study, Genomics, Genotype, Genotyping Techniques, Humans, INDEL Mutation, Nanopores, Neural Networks, Computer, Polymorphism, Single Nucleotide, Sequence Analysis, DNA, Software}, issn = {2041-1723}, doi = {10.1038/s41467-019-09025-z}, author = {Luo, Ruibang and Sedlazeck, Fritz J and Lam, Tak-Wah and Schatz, Michael C} } @article {57, title = {Opportunities and challenges for transcriptome-wide association studies.}, journal = {Nat Genet}, volume = {51}, year = {2019}, month = {2019 04}, pages = {592-599}, abstract = {

Transcriptome-wide association studies (TWAS) integrate genome-wide association studies (GWAS) and gene expression datasets to identify gene-trait associations. In this Perspective, we explore properties of TWAS as a potential approach to prioritize causal genes at GWAS loci, by using simulations and case studies of literature-curated candidate causal genes for schizophrenia, low-density-lipoprotein cholesterol and Crohn{\textquoteright}s disease. We explore risk loci where TWAS accurately prioritizes the likely causal gene as well as loci where TWAS prioritizes multiple genes, some likely to be non-causal, owing to sharing of expression quantitative trait loci (eQTL). TWAS is especially prone to spurious prioritization with expression data from non-trait-related tissues or cell types, owing to substantial cross-cell-type variation in expression levels and eQTL strengths. Nonetheless, TWAS prioritizes candidate causal genes more accurately than simple baselines. We suggest best practices for causal-gene prioritization with TWAS and discuss future opportunities for improvement. Our results showcase the strengths and limitations of using eQTL datasets to determine causal genes at GWAS loci.

}, keywords = {Crohn Disease, Genetic Predisposition to Disease, Genetic Variation, Genome-Wide Association Study, Humans, Lipoproteins, LDL, Quantitative Trait Loci, Schizophrenia, Transcriptome}, issn = {1546-1718}, doi = {10.1038/s41588-019-0385-z}, author = {Wainberg, Michael and Sinnott-Armstrong, Nasa and Mancuso, Nicholas and Barbeira, Alvaro N and Knowles, David A and Golan, David and Ermel, Raili and Ruusalepp, Arno and Quertermous, Thomas and Hao, Ke and Bj{\"o}rkegren, Johan L M and Im, Hae Kyung and Pasaniuc, Bogdan and Rivas, Manuel A and Kundaje, Anshul} } @article {58, title = {Personalized Medicine and the Power of Electronic Health Records.}, journal = {Cell}, volume = {177}, year = {2019}, month = {2019 Mar 21}, pages = {58-69}, abstract = {

Personalized medicine has largely been enabled by the integration of genomic and other data with electronic health records (EHRs) in the United States and elsewhere. Increased EHR adoption across various clinical settings and the establishment of EHR-linked population-based biobanks provide unprecedented opportunities for the types of translational and implementation research that drive personalized medicine. We review advances in the digitization of health information and the proliferation of genomic research in health systems and provide insights into emerging paths for the widespread implementation of personalized medicine.

}, issn = {1097-4172}, doi = {10.1016/j.cell.2019.02.039}, author = {Abul-Husn, Noura S and Kenny, Eimear E} } @article {68, title = {Phenome-wide Burden of Copy-Number Variation in the UK Biobank.}, journal = {Am J Hum Genet}, volume = {105}, year = {2019}, month = {2019 Aug 01}, pages = {373-383}, abstract = {

Copy-number variations (CNVs) represent a significant proportion of the genetic differences between individuals and many CNVs associate causally with syndromic disease and clinical outcomes. Here, we characterize the landscape of copy-number variation and their phenome-wide effects in a sample of 472,228 array-genotyped individuals from the UK Biobank. In addition to population-level selection effects against genic loci conferring high mortality, we describe genetic burden from potentially pathogenic and previously uncharacterized CNV loci across more than 3,000 quantitative and dichotomous traits, with separate analyses for common and rare classes of variation. Specifically, we highlight the effects of CNVs at two well-known syndromic loci 16p11.2 and 22q11.2, previously uncharacterized variation at 9p23, and several genic associations in the context of acute coronary artery disease and high body mass index. Our data constitute a deeply contextualized portrait of population-wide burden of copy-number variation, as well as a series of dosage-mediated genic associations across the medical phenome.

}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2019.07.001}, author = {Aguirre, Matthew and Rivas, Manuel A and Priest, James} } @article {53, title = {Polygenic adaptation on height is overestimated due to uncorrected stratification in genome-wide association studies.}, journal = {Elife}, volume = {8}, year = {2019}, month = {2019 Mar 21}, abstract = {

Genetic predictions of height differ among human populations and these differences have been interpreted as evidence of polygenic adaptation. These differences were first detected using SNPs genome-wide significantly associated with height, and shown to grow stronger when large numbers of sub-significant SNPs were included, leading to excitement about the prospect of analyzing large fractions of the genome to detect polygenic adaptation for multiple traits. Previous studies of height have been based on SNP effect size measurements in the GIANT Consortium meta-analysis. Here we repeat the analyses in the UK Biobank, a much more homogeneously designed study. We show that polygenic adaptation signals based on large numbers of SNPs below genome-wide significance are extremely sensitive to biases due to uncorrected population stratification. More generally, our results imply that typical constructions of polygenic scores are sensitive to population stratification and that population-level differences should be interpreted with caution.

Editorial note: This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor{\textquoteright}s assessment is that all the issues have been addressed (see decision letter).

}, issn = {2050-084X}, doi = {10.7554/eLife.39702}, author = {Sohail, Mashaal and Maier, Robert M and Ganna, Andrea and Bloemendal, Alex and Martin, Alicia R and Turchin, Michael C and Chiang, Charleston Wk and Hirschhorn, Joel and Daly, Mark J and Patterson, Nick and Neale, Benjamin and Mathieson, Iain and Reich, David and Sunyaev, Shamil R} } @article {48, title = {Quantification of frequency-dependent genetic architectures in 25 UK Biobank traits reveals action of negative selection.}, journal = {Nat Commun}, volume = {10}, year = {2019}, month = {2019 Feb 15}, pages = {790}, abstract = {

Understanding the role of rare variants is important in elucidating the genetic basis of human disease. Negative selection can cause rare variants to have larger per-allele effect sizes than common variants. Here, we develop a method to estimate the minor allele frequency (MAF) dependence of SNP effect sizes. We use a model in which per-allele effect sizes have variance proportional to [p(1 - p)], where p is the MAF and negative values of α imply larger effect sizes for rare variants. We estimate α for 25 UK Biobank diseases and complex traits. All traits produce negative α estimates, with best-fit mean of -0.38 (s.e. 0.02) across traits. Despite larger rare variant effect sizes, rare variants (MAF < 1\%) explain less than 10\% of total SNP-heritability for most traits analyzed. Using evolutionary modeling and forward simulations, we validate the α model of MAF-dependent trait effects and assess plausible values of relevant evolutionary parameters.

}, keywords = {Algorithms, Alleles, Biological Specimen Banks, Gene Frequency, Genome-Wide Association Study, Genotype, Humans, Models, Genetic, Polymorphism, Single Nucleotide, Quantitative Trait, Heritable, Selection, Genetic, United Kingdom}, issn = {2041-1723}, doi = {10.1038/s41467-019-08424-6}, author = {Schoech, Armin P and Jordan, Daniel M and Loh, Po-Ru and Gazal, Steven and O{\textquoteright}Connor, Luke J and Balick, Daniel J and Palamara, Pier F and Finucane, Hilary K and Sunyaev, Shamil R and Price, Alkes L} } @article {69, title = {Ultra-Rare Genetic Variation in the Epilepsies: A Whole-Exome Sequencing Study of 17,606 Individuals.}, journal = {Am J Hum Genet}, volume = {105}, year = {2019}, month = {2019 Aug 01}, pages = {267-282}, abstract = {

Sequencing-based studies have identified novel risk genes associated with severe epilepsies and revealed an excess of rare deleterious variation in less-severe forms of epilepsy. To identify the shared and distinct ultra-rare genetic risk factors for different types of epilepsies, we performed a whole-exome sequencing (WES) analysis of 9,170 epilepsy-affected individuals and 8,436 controls of European ancestry. We focused on three phenotypic groups: severe developmental and epileptic encephalopathies (DEEs), genetic generalized epilepsy (GGE), and non-acquired focal epilepsy (NAFE). We observed that compared to controls, individuals with any type of epilepsy carried an excess of ultra-rare, deleterious variants in constrained genes and in genes previously associated with epilepsy; we saw the strongest enrichment in individuals with DEEs and the least strong in individuals with NAFE. Moreover, we found that inhibitory GABA receptor genes were enriched for missense variants across all three classes of epilepsy, whereas no enrichment was seen in excitatory receptor genes. The larger gene groups for the GABAergic pathway or cation channels also showed a significant mutational burden in DEEs and GGE. Although no single gene surpassed exome-wide significance among individuals with GGE or NAFE, highly constrained genes and genes encoding ion channels were among the lead associations; such genes included CACNA1G, EEF1A2, and GABRG2 for GGE and LGI1, TRIM3, and GABRG2 for NAFE. Our study, the largest epilepsy WES study to date, confirms a convergence in the genetics of severe and less-severe epilepsies associated with ultra-rare coding variation, and it highlights a ubiquitous role for GABAergic inhibition in epilepsy etiology.

}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2019.05.020} } @article {37, title = {An ancestry-based approach for detecting interactions.}, journal = {Genet Epidemiol}, volume = {42}, year = {2018}, month = {2018 Feb}, pages = {49-63}, abstract = {

BACKGROUND: Epistasis and gene-environment interactions are known to contribute significantly to variation of complex phenotypes in model organisms. However, their identification in human association studies remains challenging for myriad reasons. In the case of epistatic interactions, the large number of potential interacting sets of genes presents computational, multiple hypothesis correction, and other statistical power issues. In the case of gene-environment interactions, the lack of consistently measured environmental covariates in most disease studies precludes searching for interactions and creates difficulties for replicating studies.

RESULTS: In this work, we develop a new statistical approach to address these issues that leverages genetic ancestry, defined as the proportion of ancestry derived from each ancestral population (e.g., the fraction of European/African ancestry in African Americans), in admixed populations. We applied our method to gene expression and methylation data from African American and Latino admixed individuals, respectively, identifying nine interactions that were significant at P<5{\texttimes}10-8. We show that two of the interactions in methylation data replicate, and the remaining six are significantly enriched for low P-values (P<1.8{\texttimes}10-6).

CONCLUSION: We show that genetic ancestry can be a useful proxy for unknown and unmeasured covariates in the search for interaction effects. These results have important implications for our understanding of the genetic architecture of complex traits.

}, keywords = {Black or African American, Black People, DNA Methylation, Epistasis, Genetic, Gene-Environment Interaction, Hispanic or Latino, Humans, Models, Genetic, Phenotype, White People}, issn = {1098-2272}, doi = {10.1002/gepi.22087}, author = {Park, Danny S and Eskin, Itamar and Kang, Eun Yong and Gamazon, Eric R and Eng, Celeste and Gignoux, Christopher R and Galanter, Joshua M and Burchard, Esteban and Ye, Chun J and Aschard, Hugues and Eskin, Eleazar and Halperin, Eran and Zaitlen, Noah} } @article {35, title = {A common loss-of-function variant is associated with lower vitamin B concentration in African Americans.}, journal = {Blood}, volume = {131}, year = {2018}, month = {2018 Jun 21}, pages = {2859-2863}, keywords = {Black or African American, Female, Genome-Wide Association Study, Humans, Loss of Function Mutation, Male, Polymorphism, Single Nucleotide, Transcobalamins, Vitamin B 12, Vitamin B Deficiency}, issn = {1528-0020}, doi = {10.1182/blood-2018-03-841023}, author = {Hu, Yao and Raffield, Laura M and Polfus, Linda M and Moscati, Arden and Nadkarni, Girish and Preuss, Michael H and Zhong, Xue and Wei, Qiang and Rich, Stephen S and Li, Yun and Wilson, James G and Correa, Adolfo and Loos, Ruth J F and Li, Bingshan and Auer, Paul L and Reiner, Alex P} } @article {36, title = {Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases.}, journal = {Nat Genet}, volume = {50}, year = {2018}, month = {2018 May}, pages = {693-698}, abstract = {

Horizontal pleiotropy occurs when the variant has an effect on disease outside of its effect on the exposure in Mendelian randomization (MR). Violation of the {\textquoteright}no horizontal pleiotropy{\textquoteright} assumption can cause severe bias in MR. We developed the Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO) test to identify horizontal pleiotropic outliers in multi-instrument summary-level MR testing. We showed using simulations that the MR-PRESSO test is best suited when horizontal pleiotropy occurs in <50\% of instruments. Next we applied the MR-PRESSO test, along with several other MR tests, to complex traits and diseases and found that horizontal pleiotropy (i) was detectable in over 48\% of significant causal relationships in MR; (ii) introduced distortions in the causal estimates in MR that ranged on average from -131\% to 201\%; (iii) induced false-positive causal relationships in up to 10\% of relationships; and (iv) could be corrected in some but not all instances.

}, keywords = {Disease, Genetic Pleiotropy, Genetic Predisposition to Disease, Genetic Variation, Humans}, issn = {1546-1718}, doi = {10.1038/s41588-018-0099-7}, author = {Verbanck, Marie and Chen, Chia-Yen and Neale, Benjamin and Do, Ron} } @article {44, title = {Fine-mapping and functional studies highlight potential causal variants for rheumatoid arthritis and type 1 diabetes.}, journal = {Nat Genet}, volume = {50}, year = {2018}, month = {2018 Oct}, pages = {1366-1374}, abstract = {

To define potentially causal variants for autoimmune disease, we fine-mapped 76 rheumatoid arthritis (11,475 cases, 15,870 controls) and type 1 diabetes loci (9,334 cases, 11,111 controls). After sequencing 799 1-kilobase regulatory (H3K4me3) regions within these loci in 568 individuals, we observed accurate imputation for 89\% of common variants. We defined credible sets of <=5 causal variants at 5 rheumatoid arthritis and 10 type 1 diabetes loci. We identified potentially causal missense variants at DNASE1L3, PTPN22, SH2B3, and TYK2, and noncoding variants at MEG3, CD28-CTLA4, and IL2RA. We also identified potential candidate causal variants at SIRPG and TNFAIP3. Using functional assays, we confirmed allele-specific protein binding and differential enhancer activity for three variants: the CD28-CTLA4 rs117701653 SNP, MEG3 rs34552516 indel, and TNFAIP3 rs35926684 indel.

}, keywords = {Alleles, Arthritis, Rheumatoid, Case-Control Studies, CD28 Antigens, Chromosome Mapping, CTLA-4 Antigen, Diabetes Mellitus, Type 1, Gene Frequency, Genetic Loci, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Jurkat Cells, Mutation, Polymorphism, Single Nucleotide, Quantitative Trait Loci, RNA, Long Noncoding, Tumor Necrosis Factor alpha-Induced Protein 3}, issn = {1546-1718}, doi = {10.1038/s41588-018-0216-7}, author = {Westra, Harm-Jan and Mart{\'\i}nez-Bonet, Marta and Onengut-Gumuscu, Suna and Lee, Annette and Luo, Yang and Teslovich, Nikola and Worthington, Jane and Martin, Javier and Huizinga, Tom and Klareskog, Lars and Rantapaa-Dahlqvist, Solbritt and Chen, Wei-Min and Quinlan, Aaron and Todd, John A and Eyre, Steve and Nigrovic, Peter A and Gregersen, Peter K and Rich, Stephen S and Raychaudhuri, Soumya} } @article {40, title = {Functional architecture of low-frequency variants highlights strength of negative selection across coding and non-coding annotations.}, journal = {Nat Genet}, volume = {50}, year = {2018}, month = {2018 Nov}, pages = {1600-1607}, abstract = {

Common variant heritability has been widely reported to be concentrated in variants within cell-type-specific non-coding functional annotations, but little is known about low-frequency variant functional architectures. We partitioned the heritability of both low-frequency (0.5\%<= minor allele frequency <5\%) and common (minor allele frequency >=5\%) variants in 40 UK Biobank traits across a broad set of functional annotations. We determined that non-synonymous coding variants explain 17 {\textpm} 1\% of low-frequency variant heritability ([Formula: see text]) versus 2.1 {\textpm} 0.2\% of common variant heritability ([Formula: see text]). Cell-type-specific non-coding annotations that were significantly enriched for [Formula: see text] of corresponding traits were similarly enriched for [Formula: see text] for most traits, but more enriched for brain-related annotations and traits. For example, H3K4me3 marks in brain dorsolateral prefrontal cortex explain 57 {\textpm} 12\% of [Formula: see text] versus 12 {\textpm} 2\% of [Formula: see text] for neuroticism. Forward simulations confirmed that low-frequency variant enrichment depends on the mean selection coefficient of causal variants in the annotation, and can be used to predict effect size variance of causal rare variants (minor allele frequency <0.5\%).

}, keywords = {Alleles, Biological Specimen Banks, Gene Frequency, Genetic Variation, Genetics, Population, Genome-Wide Association Study, Humans, Linkage Disequilibrium, Molecular Sequence Annotation, Open Reading Frames, Polymorphism, Single Nucleotide, Selection, Genetic, United Kingdom, White People}, issn = {1546-1718}, doi = {10.1038/s41588-018-0231-8}, author = {Gazal, Steven and Loh, Po-Ru and Finucane, Hilary K and Ganna, Andrea and Schoech, Armin and Sunyaev, Shamil and Price, Alkes L} } @article {43, title = {Functional equivalence of genome sequencing analysis pipelines enables harmonized variant calling across human genetics projects.}, journal = {Nat Commun}, volume = {9}, year = {2018}, month = {2018 Oct 02}, pages = {4038}, abstract = {

Hundreds of thousands of human whole genome sequencing (WGS) datasets will be generated over the next few years. These data are more valuable in aggregate: joint analysis of genomes from many sources increases sample size and statistical power. A central challenge for joint analysis is that different WGS data processing pipelines cause substantial differences in variant calling in combined datasets, necessitating computationally expensive reprocessing. This approach is no longer tenable given the scale of current studies and data volumes. Here, we define WGS data processing standards that allow different groups to produce functionally equivalent (FE) results, yet still innovate on data processing pipelines. We present initial FE pipelines developed at five genome centers and show that they yield similar variant calling results and produce significantly less variability than sequencing replicates. This work alleviates a key technical bottleneck for genome aggregation and helps lay the foundation for community-wide human genetics studies.

}, keywords = {Genome, Human, Human Genetics, Humans, Whole Genome Sequencing}, issn = {2041-1723}, doi = {10.1038/s41467-018-06159-4}, author = {Regier, Allison A and Farjoun, Yossi and Larson, David E and Krasheninina, Olga and Kang, Hyun Min and Howrigan, Daniel P and Chen, Bo-Juen and Kher, Manisha and Banks, Eric and Ames, Darren C and English, Adam C and Li, Heng and Xing, Jinchuan and Zhang, Yeting and Matise, Tara and Abecasis, Gon{\c c}alo R and Salerno, Will and Zody, Michael C and Neale, Benjamin M and Hall, Ira M} } @article {31, title = {Functional genomics of stromal cells in chronic inflammatory diseases.}, journal = {Curr Opin Rheumatol}, volume = {30}, year = {2018}, month = {2018 Jan}, pages = {65-71}, abstract = {

PURPOSE OF REVIEW: Stroma is a broad term referring to the connective tissue matrix in which other cells reside. It is composed of diverse cell types with functions such as extracellular matrix maintenance, blood and lymph vessel development, and effector cell recruitment. The tissue microenvironment is determined by the molecular characteristics and relative abundances of different stromal cells such as fibroblasts, endothelial cells, pericytes, and mesenchymal precursor cells. Stromal cell heterogeneity is explained by embryonic developmental lineage, stages of differentiation to other cell types, and activation states. Interaction between immune and stromal cell types is critical to wound healing, cancer, and a wide range of inflammatory diseases. Here, we review recent studies of inflammatory diseases that use functional genomics and single-cell technologies to identify and characterize stromal cell types associated with pathogenesis.

RECENT FINDINGS: High dimensional strategies using mRNA sequencing, mass cytometry, and fluorescence activated cell-sorting with fresh primary tissue samples are producing detailed views of what is happening in diseased tissue in rheumatoid arthritis, inflammatory bowel disease, and cancer. Fibroblasts positive for CD90 (Thy-1) are enriched in the synovium of rheumatoid arthritis patients. Single-cell RNA-seq studies will lead to more discoveries about the stroma in the near future.

SUMMARY: Stromal cells form the microenvironment of inflamed and diseased tissues. Functional genomics is producing an increasingly detailed view of subsets of stromal cells with pathogenic functions in rheumatic diseases and cancer. Future genomics studies will discover disease mechanisms by perturbing molecular pathways with chemokines and therapies known to affect patient outcomes. Functional genomics studies with large sample sizes of patient tissues will identify patient subsets with different disease phenotypes or treatment responses.

}, keywords = {Arthritis, Rheumatoid, Cell Differentiation, Chemokines, Endothelial Cells, Fibroblasts, Flow Cytometry, Genomics, Humans, Inflammatory Bowel Diseases, Mesenchymal Stem Cells, Pericytes, Rheumatic Diseases, RNA, Messenger, Sequence Analysis, RNA, Single-Cell Analysis, Stromal Cells, Synovial Membrane, Thy-1 Antigens}, issn = {1531-6963}, doi = {10.1097/BOR.0000000000000455}, author = {Slowikowski, Kamil and Wei, Kevin and Brenner, Michael B and Raychaudhuri, Soumya} } @article {61, title = {Genetic diversity in populations across Latin America: implications for population and medical genetic studies.}, journal = {Curr Opin Genet Dev}, volume = {53}, year = {2018}, month = {2018 12}, pages = {98-104}, abstract = {

Hispanic/Latino (H/L) populations, although linked by culture and aspects of shared history, reflect the complexity of history and migration influencing the Americas. The original settlement by indigenous Americans, followed by postcolonial admixture from multiple continents, has yielded localized genetic patterns. In addition, numerous H/L populations appear to have signatures of pre-colonization and post-colonization bottlenecks, indicating that tens of millions of H/Ls may harbor signatures of founder effects today. Based on both population and medical genetic findings we highlight the extreme differentiation across the Americas, providing evidence for why H/Ls should not be considered a single population in modern human genetics. We highlight the need for additional sampling of understudied H/L groups, and ramifications of these findings for genomic medicine in one-tenth of the world{\textquoteright}s population.

}, keywords = {African Continental Ancestry Group, European Continental Ancestry Group, Genetic Variation, Genetics, Medical, Genetics, Population, Genome, Human, Genomics, Humans, Latin America}, issn = {1879-0380}, doi = {10.1016/j.gde.2018.07.006}, author = {Belbin, Gillian M and Nieves-Col{\'o}n, Maria A and Kenny, Eimear E and Moreno-Estrada, Andres and Gignoux, Christopher R} } @article {39, title = {Imputation-Aware Tag SNP Selection To Improve Power for Large-Scale, Multi-ethnic Association Studies.}, journal = {G3 (Bethesda)}, volume = {8}, year = {2018}, month = {2018 Oct 03}, pages = {3255-3267}, abstract = {

The emergence of very large cohorts in genomic research has facilitated a focus on genotype-imputation strategies to power rare variant association. These strategies have benefited from improvements in imputation methods and association tests, however little attention has been paid to ways in which array design can increase rare variant association power. Therefore, we developed a novel framework to select tag SNPs using the reference panel of 26 populations from Phase 3 of the 1000 Genomes Project. We evaluate tag SNP performance mean imputed r at untyped sites using leave-one-out internal validation and standard imputation methods, rather than pairwise linkage disequilibrium. Moving beyond pairwise metrics allows us to account for haplotype diversity across the genome for improve imputation accuracy and demonstrates population-specific biases from pairwise estimates. We also examine array design strategies that contrast multi-ethnic cohorts single populations, and show a boost in performance for the former can be obtained by prioritizing tag SNPs that contribute information across multiple populations simultaneously. Using our framework, we demonstrate increased imputation accuracy for rare variants (frequency < 1\%) by 0.5-3.1\% for an array of one million sites and 0.7-7.1\% for an array of 500,000 sites, depending on the population. Finally, we show how recent explosive growth in non-African populations means tag SNPs capture on average 30\% fewer other variants than in African populations. The unified framework presented here will enable investigators to make informed decisions for the design of new arrays, and help empower the next phase of rare variant association for global health.

}, keywords = {Computational Biology, Databases, Nucleic Acid, Ethnicity, Genetic Association Studies, Genetics, Population, Genome-Wide Association Study, Humans, Linkage Disequilibrium, Models, Genetic, Polymorphism, Single Nucleotide, Reproducibility of Results, Selection, Genetic}, issn = {2160-1836}, doi = {10.1534/g3.118.200502}, author = {Wojcik, Genevieve L and Fuchsberger, Christian and Taliun, Daniel and Welch, Ryan and Martin, Alicia R and Shringarpure, Suyash and Carlson, Christopher S and Abecasis, Goncalo and Kang, Hyun Min and Boehnke, Michael and Bustamante, Carlos D and Gignoux, Christopher R and Kenny, Eimear E} } @article {28, title = {Medical relevance of protein-truncating variants across 337,205 individuals in the UK Biobank study.}, journal = {Nat Commun}, volume = {9}, year = {2018}, month = {2018 Apr 24}, pages = {1612}, abstract = {

Protein-truncating variants can have profound effects on gene function and are critical for clinical genome interpretation and generating therapeutic hypotheses, but their relevance to medical phenotypes has not been systematically assessed. Here, we characterize the effect of 18,228 protein-truncating variants across 135 phenotypes from the UK Biobank and find 27 associations between medical phenotypes and protein-truncating variants in genes outside the major histocompatibility complex. We perform phenome-wide analyses and directly measure the effect in homozygous carriers, commonly referred to as "human knockouts," across medical phenotypes for genes implicated as being protective against disease or associated with at least one phenotype in our study. We find several genes with strong pleiotropic or non-additive effects. Our results illustrate the importance of protein-truncating variants in a variety of diseases.

}, keywords = {Databases, Nucleic Acid, Genome-Wide Association Study, Humans, Phenotype, Proteins, Sequence Deletion, United Kingdom}, issn = {2041-1723}, doi = {10.1038/s41467-018-03910-9}, author = {DeBoever, Christopher and Tanigawa, Yosuke and Lindholm, Malene E and McInnes, Greg and Lavertu, Adam and Ingelsson, Erik and Chang, Chris and Ashley, Euan A and Bustamante, Carlos D and Daly, Mark J and Rivas, Manuel A} } @article {12, title = {Multiple phenotype association tests using summary statistics in genome-wide association studies.}, journal = {Biometrics}, volume = {74}, year = {2018}, month = {2018 Mar}, pages = {165-175}, abstract = {

We study in this article jointly testing the associations of a genetic variant with correlated multiple phenotypes using the summary statistics of individual phenotype analysis from Genome-Wide Association Studies (GWASs). We estimated the between-phenotype correlation matrix using the summary statistics of individual phenotype GWAS analyses, and developed genetic association tests for multiple phenotypes by accounting for between-phenotype correlation without the need to access individual-level data. Since genetic variants often affect multiple phenotypes differently across the genome and the between-phenotype correlation can be arbitrary, we proposed robust and powerful multiple phenotype testing procedures by jointly testing a common mean and a variance component in linear mixed models for summary statistics. We computed the p-values of the proposed tests analytically. This computational advantage makes our methods practically appealing in large-scale GWASs. We performed simulation studies to show that the proposed tests maintained correct type I error rates, and to compare their powers in various settings with the existing methods. We applied the proposed tests to a GWAS Global Lipids Genetics Consortium summary statistics data set and identified additional genetic variants that were missed by the original single-trait analysis.

}, keywords = {Analysis of Variance, Computer Simulation, Genome-Wide Association Study, Humans, Linear Models, Lipids, Models, Genetic, Phenotype}, issn = {1541-0420}, doi = {10.1111/biom.12735}, author = {Liu, Zhonghua and Lin, Xihong} } @article {29, title = {Phenotype risk scores identify patients with unrecognized Mendelian disease patterns.}, journal = {Science}, volume = {359}, year = {2018}, month = {2018 Mar 16}, pages = {1233-1239}, abstract = {

Genetic association studies often examine features independently, potentially missing subpopulations with multiple phenotypes that share a single cause. We describe an approach that aggregates phenotypes on the basis of patterns described by Mendelian diseases. We mapped the clinical features of 1204 Mendelian diseases into phenotypes captured from the electronic health record (EHR) and summarized this evidence as phenotype risk scores (PheRSs). In an initial validation, PheRS distinguished cases and controls of five Mendelian diseases. Applying PheRS to 21,701 genotyped individuals uncovered 18 associations between rare variants and phenotypes consistent with Mendelian diseases. In 16 patients, the rare genetic variants were associated with severe outcomes such as organ transplants. PheRS can augment rare-variant interpretation and may identify subsets of patients with distinct genetic causes for common diseases.

}, keywords = {Databases, Genetic, DNA Mutational Analysis, Electronic Health Records, Exome, Genetic Association Studies, Genetic Diseases, Inborn, Genetic Predisposition to Disease, Genetic Variation, Humans, Phenotype, Risk Factors}, issn = {1095-9203}, doi = {10.1126/science.aal4043}, author = {Bastarache, Lisa and Hughey, Jacob J and Hebbring, Scott and Marlo, Joy and Zhao, Wanke and Ho, Wanting T and Van Driest, Sara L and McGregor, Tracy L and Mosley, Jonathan D and Wells, Quinn S and Temple, Michael and Ramirez, Andrea H and Carroll, Robert and Osterman, Travis and Edwards, Todd and Ruderfer, Douglas and Velez Edwards, Digna R and Hamid, Rizwan and Cogan, Joy and Glazer, Andrew and Wei, Wei-Qi and Feng, QiPing and Brilliant, Murray and Zhao, Zhizhuang J and Cox, Nancy J and Roden, Dan M and Denny, Joshua C} } @article {45, title = {Quantifying the Polygenic Contribution to Cutaneous Squamous Cell Carcinoma Risk.}, journal = {J Invest Dermatol}, volume = {138}, year = {2018}, month = {2018 Jul}, pages = {1507-1510}, abstract = {

Genetic factors play an important role in cutaneous squamous cell carcinoma risk. Genome-wide association studies have identified 21 single nucleotide polymorphisms associated with cutaneous squamous cell carcinoma risk. Yet no studies have attempted to quantify the contribution of heritability to cutaneous squamous cell carcinoma risk by calculating the population attributable risk using a combination of all discovered genetic variants. Using an additive multi-locus linear logistic model, we determined the cumulative association of these 21 genetic regions to cutaneous squamous cell carcinoma population attributable risk. We computed a multi-locus population attributable risk of 62\%, suggesting that if the effects of all the risk alleles were removed from a population, the cutaneous squamous cell carcinoma risk would drop by 62\%. Using stratified analysis, we also examined the impact of sex on polygenic risk score, and found that men have an increased relative risk throughout the spectrum of the polygenic risk score. Quantifying the impact of genetic predisposition on the proportion of cancer cases can guide future research decisions and public health policy planning.

}, keywords = {Carcinoma, Squamous Cell, Female, Genetic Loci, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Linear Models, Logistic Models, Male, Models, Biological, Polymorphism, Single Nucleotide, Risk Assessment, Risk Factors, Sex Factors, Skin Neoplasms}, issn = {1523-1747}, doi = {10.1016/j.jid.2018.01.031}, author = {Sordillo, Joanne E and Kraft, Peter and Wu, Ann Chen and Asgari, Maryam M} } @article {34, title = {A synthetic-diploid benchmark for accurate variant-calling evaluation.}, journal = {Nat Methods}, volume = {15}, year = {2018}, month = {2018 Aug}, pages = {595-597}, abstract = {

Existing benchmark datasets for use in evaluating variant-calling accuracy are constructed from a consensus of known short-variant callers, and they are thus biased toward easy regions that are accessible by these algorithms. We derived a new benchmark dataset from the de novo PacBio assemblies of two fully homozygous human cell lines, which provides a relatively more accurate and less biased estimate of small-variant-calling error rates in a realistic context.

}, keywords = {Algorithms, Benchmarking, Cell Line, Tumor, Databases, Genetic, Diploidy, Female, Genetic Variation, Genome, Human, Homozygote, Humans, Hydatidiform Mole, Pregnancy, Synthetic Biology, Uterine Neoplasms, Whole Genome Sequencing}, issn = {1548-7105}, doi = {10.1038/s41592-018-0054-7}, author = {Li, Heng and Bloom, Jonathan M and Farjoun, Yossi and Fleharty, Mark and Gauthier, Laura and Neale, Benjamin and MacArthur, Daniel} } @article {20, title = {Testing for gene-environment interaction under exposure misspecification.}, journal = {Biometrics}, volume = {74}, year = {2018}, month = {2018 Jun}, pages = {653-662}, abstract = {

Complex interplay between genetic and environmental factors characterizes the etiology of many diseases. Modeling gene-environment (GxE) interactions is often challenged by the unknown functional form of the environment term in the true data-generating mechanism. We study the impact of misspecification of the environmental exposure effect on inference for the GxE interaction term in linear and logistic regression models. We first examine the asymptotic bias of the GxE interaction regression coefficient, allowing for confounders as well as arbitrary misspecification of the exposure and confounder effects. For linear regression, we show that under gene-environment independence and some confounder-dependent conditions, when the environment effect is misspecified, the regression coefficient of the GxE interaction can be unbiased. However, inference on the GxE interaction is still often incorrect. In logistic regression, we show that the regression coefficient is generally biased if the genetic factor is associated with the outcome directly or indirectly. Further, we show that the standard robust sandwich variance estimator for the GxE interaction does not perform well in practical GxE studies, and we provide an alternative testing procedure that has better finite sample properties.

}, keywords = {Bias, Confounding Factors, Epidemiologic, Environmental Exposure, Gene-Environment Interaction, Humans, Linear Models, Models, Genetic, Scientific Experimental Error}, issn = {1541-0420}, doi = {10.1111/biom.12813}, author = {Sun, Ryan and Carroll, Raymond J and Christiani, David C and Lin, Xihong} } @article {15, title = {Cohort-specific imputation of gene expression improves prediction of warfarin dose for African Americans.}, journal = {Genome Med}, volume = {9}, year = {2017}, month = {2017 Nov 24}, pages = {98}, abstract = {

BACKGROUND: Genome-wide association studies are useful for discovering genotype-phenotype associations but are limited because they require large cohorts to identify a signal, which can be population-specific. Mapping genetic variation to genes improves power and allows the effects of both protein-coding variation as well as variation in expression to be combined into "gene level" effects.

METHODS: Previous work has shown that warfarin dose can be predicted using information from genetic variation that affects protein-coding regions. Here, we introduce a method that improves dose prediction by integrating tissue-specific gene expression. In particular, we use drug pathways and expression quantitative trait loci knowledge to impute gene expression-on the assumption that differential expression of key pathway genes may impact dose requirement. We focus on 116 genes from the pharmacokinetic and pharmacodynamic pathways of warfarin within training and validation sets comprising both European and African-descent individuals.

RESULTS: We build gene-tissue signatures associated with warfarin dose in a cohort-specific manner and identify a signature of 11 gene-tissue pairs that significantly augments the International Warfarin Pharmacogenetics Consortium dosage-prediction algorithm in both populations.

CONCLUSIONS: Our results demonstrate that imputed expression can improve dose prediction and bridge population-specific compositions. MATLAB code is available at https://github.com/assafgo/warfarin-cohort.

}, keywords = {Anticoagulants, Black or African American, Female, Gene Expression, Genome-Wide Association Study, Humans, Male, Tissue Distribution, Warfarin}, issn = {1756-994X}, doi = {10.1186/s13073-017-0495-0}, author = {Gottlieb, Assaf and Daneshjou, Roxana and DeGorter, Marianne and Bourgeois, Stephane and Svensson, Peter J and Wadelius, Mia and Deloukas, Panos and Montgomery, Stephen B and Altman, Russ B} } @article {21, title = {Covariate selection for association screening in multiphenotype genetic studies.}, journal = {Nat Genet}, volume = {49}, year = {2017}, month = {2017 Dec}, pages = {1789-1795}, abstract = {

Testing for associations in big data faces the problem of multiple comparisons, wherein true signals are difficult to detect on the background of all associations queried. This difficulty is particularly salient in human genetic association studies, in which phenotypic variation is often driven by numerous variants of small effect. The current strategy to improve power to identify these weak associations consists of applying standard marginal statistical approaches and increasing study sample sizes. Although successful, this approach does not leverage the environmental and genetic factors shared among the multiple phenotypes collected in contemporary cohorts. Here we developed covariates for multiphenotype studies (CMS), an approach that improves power when correlated phenotypes are measured on the same samples. Our analyses of real and simulated data provide direct evidence that correlated phenotypes can be used to achieve increases in power to levels often surpassing the power gained by a twofold increase in sample size.

}, keywords = {Algorithms, Genetic Association Studies, Genetic Variation, Genome-Wide Association Study, Genotype, Humans, Models, Genetic, Multivariate Analysis, Phenotype, Reproducibility of Results, Sample Size}, issn = {1546-1718}, doi = {10.1038/ng.3975}, author = {Aschard, Hugues and Guillemot, Vincent and Vilhjalmsson, Bjarni and Patel, Chirag J and Skurnik, David and Ye, Chun J and Wolpin, Brian and Kraft, Peter and Zaitlen, Noah} } @article {22, title = {Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases.}, journal = {Am J Epidemiol}, volume = {186}, year = {2017}, month = {2017 Oct 01}, pages = {753-761}, abstract = {

Recently, many new approaches, study designs, and statistical and analytical methods have emerged for studying gene-environment interactions (G{\texttimes}Es) in large-scale studies of human populations. There are opportunities in this field, particularly with respect to the incorporation of -omics and next-generation sequencing data and continual improvement in measures of environmental exposures implicated in complex disease outcomes. In a workshop called "Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases," held October 17-18, 2014, by the National Institute of Environmental Health Sciences and the National Cancer Institute in conjunction with the annual American Society of Human Genetics meeting, participants explored new approaches and tools that have been developed in recent years for G{\texttimes}E discovery. This paper highlights current and critical issues and themes in G{\texttimes}E research that need additional consideration, including the improved data analytical methods, environmental exposure assessment, and incorporation of functional data and annotations.

}, keywords = {Disease, Gene-Environment Interaction, Genetic Predisposition to Disease, Genome-Wide Association Study, High-Throughput Nucleotide Sequencing, Humans, Software}, issn = {1476-6256}, doi = {10.1093/aje/kwx227}, author = {McAllister, Kimberly and Mechanic, Leah E and Amos, Christopher and Aschard, Hugues and Blair, Ian A and Chatterjee, Nilanjan and Conti, David and Gauderman, W James and Hsu, Li and Hutter, Carolyn M and Jankowska, Marta M and Kerr, Jacqueline and Kraft, Peter and Montgomery, Stephen B and Mukherjee, Bhramar and Papanicolaou, George J and Patel, Chirag J and Ritchie, Marylyn D and Ritz, Beate R and Thomas, Duncan C and Wei, Peng and Witte, John S} } @article {7, title = {The Effects of Migration and Assortative Mating on Admixture Linkage Disequilibrium.}, journal = {Genetics}, volume = {205}, year = {2017}, month = {2017 Jan}, pages = {375-383}, abstract = {

Statistical models in medical and population genetics typically assume that individuals assort randomly in a population. While this simplifies model complexity, it contradicts an increasing body of evidence of nonrandom mating in human populations. Specifically, it has been shown that assortative mating is significantly affected by genomic ancestry. In this work, we examine the effects of ancestry-assortative mating on the linkage disequilibrium between local ancestry tracks of individuals in an admixed population. To accomplish this, we develop an extension to the Wright-Fisher model that allows for ancestry-based assortative mating. We show that ancestry-assortment perturbs the distribution of local ancestry linkage disequilibrium (LAD) and the variance of ancestry in a population as a function of the number of generations since admixture. This assortment effect can induce errors in demographic inference of admixed populations when methods assume random mating. We derive closed form formulae for LAD under an assortative-mating model with and without migration. We observe that LAD depends on the correlation of global ancestry of couples in each generation, the migration rate of each of the ancestral populations, the initial proportions of ancestral populations, and the number of generations since admixture. We also present the first direct evidence of ancestry-assortment in African Americans and examine LAD in simulated and real admixed population data of African Americans. We find that demographic inference under the assumption of random mating significantly underestimates the number of generations since admixture, and that accounting for assortative mating using the patterns of LAD results in estimates that more closely agrees with the historical narrative.

}, keywords = {Alleles, Black or African American, Datasets as Topic, Gene Frequency, Genetics, Population, Genomics, Human Migration, Humans, Linkage Disequilibrium, Models, Genetic, Models, Statistical, Polymorphism, Single Nucleotide}, issn = {1943-2631}, doi = {10.1534/genetics.116.192138}, author = {Zaitlen, Noah and Huntsman, Scott and Hu, Donglei and Spear, Melissa and Eng, Celeste and Oh, Sam S and White, Marquitta J and Mak, Angel and Davis, Adam and Meade, Kelly and Brigino-Buenaventura, Emerita and LeNoir, Michael A and Bibbins-Domingo, Kirsten and Burchard, Esteban G and Halperin, Eran} } @article {11, title = {Estimating the selective effects of heterozygous protein-truncating variants from human exome data.}, journal = {Nat Genet}, volume = {49}, year = {2017}, month = {2017 May}, pages = {806-810}, abstract = {

The evolutionary cost of gene loss is a central question in genetics and has been investigated in model organisms and human cell lines. In humans, tolerance of the loss of one or both functional copies of a gene is related to the gene{\textquoteright}s causal role in disease. However, estimates of the selection and dominance coefficients in humans have been elusive. Here we analyze exome sequence data from 60,706 individuals to make genome-wide estimates of selection against heterozygous loss of gene function. Using this distribution of selection coefficients for heterozygous protein-truncating variants (PTVs), we provide corresponding Bayesian estimates for individual genes. We find that genes under the strongest selection are enriched in embryonic lethal mouse knockouts, Mendelian disease-associated genes, and regulators of transcription. Screening by essentiality, we find a large set of genes under strong selection that are likely to have crucial functions but have not yet been thoroughly characterized.

}, keywords = {Algorithms, Animals, Bayes Theorem, Exome, Gene Frequency, Genetic Predisposition to Disease, Genetic Variation, Genome-Wide Association Study, Genotype, Heterozygote, Humans, Mice, Knockout, Models, Genetic, Mutation, Selection, Genetic, Sequence Analysis, DNA}, issn = {1546-1718}, doi = {10.1038/ng.3831}, author = {Cassa, Christopher A and Weghorn, Donate and Balick, Daniel J and Jordan, Daniel M and Nusinow, David and Samocha, Kaitlin E and O{\textquoteright}Donnell-Luria, Anne and MacArthur, Daniel G and Daly, Mark J and Beier, David R and Sunyaev, Shamil R} } @article {46, title = {The Generalized Higher Criticism for Testing SNP-Set Effects in Genetic Association Studies.}, journal = {J Am Stat Assoc}, volume = {112}, year = {2017}, month = {2017}, pages = {64-76}, abstract = {

It is of substantial interest to study the effects of genes, genetic pathways, and networks on the risk of complex diseases. These genetic constructs each contain multiple SNPs, which are often correlated and function jointly, and might be large in number. However, only a sparse subset of SNPs in a genetic construct is generally associated with the disease of interest. In this article, we propose the generalized higher criticism (GHC) to test for the association between an SNP set and a disease outcome. The higher criticism is a test traditionally used in high-dimensional signal detection settings when marginal test statistics are independent and the number of parameters is very large. However, these assumptions do not always hold in genetic association studies, due to linkage disequilibrium among SNPs and the finite number of SNPs in an SNP set in each genetic construct. The proposed GHC overcomes the limitations of the higher criticism by allowing for arbitrary correlation structures among the SNPs in an SNP-set, while performing accurate analytic -value calculations for any finite number of SNPs in the SNP-set. We obtain the detection boundary of the GHC test. We compared empirically using simulations the power of the GHC method with existing SNP-set tests over a range of genetic regions with varied correlation structures and signal sparsity. We apply the proposed methods to analyze the CGEM breast cancer genome-wide association study. Supplementary materials for this article are available online.

}, issn = {0162-1459}, doi = {10.1080/01621459.2016.1192039}, author = {Barnett, Ian and Mukherjee, Rajarshi and Lin, Xihong} } @article {18, title = {Genetic effects on gene expression across human tissues.}, journal = {Nature}, volume = {550}, year = {2017}, month = {2017 Oct 11}, pages = {204-213}, abstract = {

Characterization of the molecular function of the human genome and its variation across individuals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-individual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of disease.

}, keywords = {Alleles, Chromosomes, Human, Disease, Female, Gene Expression Profiling, Gene Expression Regulation, Genetic Variation, Genome, Human, Genotype, Humans, Male, Organ Specificity, Quantitative Trait Loci}, issn = {1476-4687}, doi = {10.1038/nature24277}, author = {Battle, Alexis and Brown, Christopher D and Engelhardt, Barbara E and Montgomery, Stephen B} } @article {32, title = {Genetic identification of a common collagen disease in puerto ricans via identity-by-descent mapping in a health system.}, journal = {Elife}, volume = {6}, year = {2017}, month = {2017 Sep 12}, abstract = {

Achieving confidence in the causality of a disease locus is a complex task that often requires supporting data from both statistical genetics and clinical genomics. Here we describe a combined approach to identify and characterize a genetic disorder that leverages distantly related patients in a health system and population-scale mapping. We utilize genomic data to uncover components of distant pedigrees, in the absence of recorded pedigree information, in the multi-ethnic Bio biobank in New York City. By linking to medical records, we discover a locus associated with both elevated genetic relatedness and extreme short stature. We link the gene, , with a little-known genetic disease, previously thought to be rare and recessive. We demonstrate that disease manifests in both heterozygotes and homozygotes, indicating a common collagen disorder impacting up to 2\% of individuals of Puerto Rican ancestry, leading to a better understanding of the continuum of complex and Mendelian disease.

}, keywords = {Adolescent, Adult, Aged, Child, Collagen Diseases, Female, Fibrillar Collagens, Genotype, Heterozygote, Hispanic or Latino, Homozygote, Humans, Male, Middle Aged, Molecular Epidemiology, Multigene Family, Musculoskeletal Diseases, New York City, Pedigree, Whole Genome Sequencing, Young Adult}, issn = {2050-084X}, doi = {10.7554/eLife.25060}, author = {Belbin, Gillian Morven and Odgis, Jacqueline and Sorokin, Elena P and Yee, Muh-Ching and Kohli, Sumita and Glicksberg, Benjamin S and Gignoux, Christopher R and Wojcik, Genevieve L and Van Vleck, Tielman and Jeff, Janina M and Linderman, Michael and Schurmann, Claudia and Ruderfer, Douglas and Cai, Xiaoqiang and Merkelson, Amanda and Justice, Anne E and Young, Kristin L and Graff, Misa and North, Kari E and Peters, Ulrike and James, Regina and Hindorff, Lucia and Kornreich, Ruth and Edelmann, Lisa and Gottesman, Omri and Stahl, Eli Ea and Cho, Judy H and Loos, Ruth Jf and Bottinger, Erwin P and Nadkarni, Girish N and Abul-Husn, Noura S and Kenny, Eimear E} } @article {19, title = {A genetic stochastic process model for genome-wide joint analysis of biomarker dynamics and disease susceptibility with longitudinal data.}, journal = {Genet Epidemiol}, volume = {41}, year = {2017}, month = {2017 Nov}, pages = {620-635}, abstract = {

Unraveling the underlying biological mechanisms or pathways behind the effects of genetic variations on complex diseases remains one of the major challenges in the post-GWAS (where GWAS is genome-wide association study) era. To further explore the relationship between genetic variations, biomarkers, and diseases for elucidating underlying pathological mechanism, a huge effort has been placed on examining pleiotropic and gene-environmental interaction effects. We propose a novel genetic stochastic process model (GSPM) that can be applied to GWAS and jointly investigate the genetic effects on longitudinally measured biomarkers and risks of diseases. This model is characterized by more profound biological interpretation and takes into account the dynamics of biomarkers during follow-up when investigating the hazards of a disease. We illustrate the rationale and evaluate the performance of the proposed model through two GWAS. One is to detect single nucleotide polymorphisms (SNPs) having interaction effects on type 2 diabetes (T2D) with body mass index (BMI) and the other is to detect SNPs affecting the optimal BMI level for protecting from T2D. We identified multiple SNPs that showed interaction effects with BMI on T2D, including a novel SNP rs11757677 in the CDKAL1 gene (P = 5.77 {\texttimes} 10 ). We also found a SNP rs1551133 located on 2q14.2 that reversed the effect of BMI on T2D (P = 6.70 {\texttimes} 10 ). In conclusion, the proposed GSPM provides a promising and useful tool in GWAS of longitudinal data for interrogating pleiotropic and interaction effects to gain more insights into the relationship between genes, quantitative biomarkers, and risks of complex diseases.

}, keywords = {Biomarkers, Body Mass Index, Computational Biology, Diabetes Mellitus, Type 2, Female, Gene-Environment Interaction, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Male, Models, Genetic, Models, Statistical, Polymorphism, Single Nucleotide, Risk, Stochastic Processes}, issn = {1098-2272}, doi = {10.1002/gepi.22058}, author = {He, Liang and Zhbannikov, Ilya and Arbeev, Konstantin G and Yashin, Anatoliy I and Kulminski, Alexander M} } @article {14, title = {Human Demographic History Impacts Genetic Risk Prediction across Diverse Populations.}, journal = {Am J Hum Genet}, volume = {100}, year = {2017}, month = {2017 Apr 06}, pages = {635-649}, abstract = {

The vast majority of genome-wide association studies (GWASs) are performed in Europeans, and their transferability to other populations is dependent on many factors (e.g., linkage disequilibrium, allele frequencies, genetic architecture). As medical genomics studies become increasingly large and diverse, gaining insights into population history and consequently the transferability of disease risk measurement is critical. Here, we disentangle recent population history in the widely used 1000 Genomes Project reference panel, with an emphasis on populations underrepresented in medical studies. To examine the transferability of single-ancestry GWASs, we used published summary statistics to calculate polygenic risk scores for eight well-studied phenotypes. We identify directional inconsistencies in all scores; for example, height is predicted to decrease with genetic distance from Europeans, despite robust anthropological evidence that West Africans are as tall as Europeans on average. To gain deeper quantitative insights into GWAS transferability, we developed a complex trait coalescent-based simulation framework considering effects of polygenicity, causal allele frequency divergence, and heritability. As expected, correlations between true and inferred risk are typically highest in the population from which summary statistics were derived. We demonstrate that scores inferred from European GWASs are biased by genetic drift in other populations even when choosing the same causal variants and that biases in any direction are possible and unpredictable. This work cautions that summarizing findings from large-scale GWASs may have limited portability to other populations using standard approaches and highlights the need for generalized risk prediction methods and the inclusion of more diverse individuals in medical genomics.

}, keywords = {Americas, Genetic Predisposition to Disease, Genetics, Medical, Genetics, Population, Haplotypes, Human Genome Project, Humans, Multifactorial Inheritance, Racial Groups}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2017.03.004}, author = {Martin, Alicia R and Gignoux, Christopher R and Walters, Raymond K and Wojcik, Genevieve L and Neale, Benjamin M and Gravel, Simon and Daly, Mark J and Bustamante, Carlos D and Kenny, Eimear E} } @article {30, title = {The impact of rare variation on gene expression across tissues.}, journal = {Nature}, volume = {550}, year = {2017}, month = {2017 Oct 11}, pages = {239-243}, abstract = {

Rare genetic variants are abundant in humans and are expected to contribute to individual disease risk. While genetic association studies have successfully identified common genetic variants associated with susceptibility, these studies are not practical for identifying rare variants. Efforts to distinguish pathogenic variants from benign rare variants have leveraged the genetic code to identify deleterious protein-coding alleles, but no analogous code exists for non-coding variants. Therefore, ascertaining which rare variants have phenotypic effects remains a major challenge. Rare non-coding variants have been associated with extreme gene expression in studies using single tissues, but their effects across tissues are unknown. Here we identify gene expression outliers, or individuals showing extreme expression levels for a particular gene, across 44 human tissues by using combined analyses of whole genomes and multi-tissue RNA-sequencing data from the Genotype-Tissue Expression (GTEx) project v6p release. We find that 58\% of underexpression and 28\% of overexpression outliers have nearby conserved rare variants compared to 8\% of non-outliers. Additionally, we developed RIVER (RNA-informed variant effect on regulation), a Bayesian statistical model that incorporates expression data to predict a regulatory effect for rare variants with higher accuracy than models using genomic annotations alone. Overall, we demonstrate that rare variants contribute to large gene expression changes across tissues and provide an integrative method for interpretation of rare variants in individual genomes.

}, keywords = {Bayes Theorem, Female, Gene Expression Profiling, Genetic Variation, Genome, Human, Genomics, Genotype, Humans, Male, Models, Genetic, Organ Specificity, Sequence Analysis, RNA}, issn = {1476-4687}, doi = {10.1038/nature24267}, author = {Li, Xin and Kim, Yungil and Tsang, Emily K and Davis, Joe R and Damani, Farhan N and Chiang, Colby and Hess, Gaelen T and Zappala, Zachary and Strober, Benjamin J and Scott, Alexandra J and Li, Amy and Ganna, Andrea and Bassik, Michael C and Merker, Jason D and Hall, Ira M and Battle, Alexis and Montgomery, Stephen B} } @article {26, title = {Improved methods for multi-trait fine mapping of pleiotropic risk loci.}, journal = {Bioinformatics}, volume = {33}, year = {2017}, month = {2017 Jan 15}, pages = {248-255}, abstract = {

MOTIVATION: Genome-wide association studies (GWAS) have identified thousands of regions in the genome that contain genetic variants that increase risk for complex traits and diseases. However, the variants uncovered in GWAS are typically not biologically causal, but rather, correlated to the true causal variant through linkage disequilibrium (LD). To discern the true causal variant(s), a variety of statistical fine-mapping methods have been proposed to prioritize variants for functional validation.

RESULTS: In this work we introduce a new approach, fastPAINTOR, that leverages evidence across correlated traits, as well as functional annotation data, to improve fine-mapping accuracy at pleiotropic risk loci. To improve computational efficiency, we describe an new importance sampling scheme to perform model inference. First, we demonstrate in simulations that by leveraging functional annotation data, fastPAINTOR increases fine-mapping resolution relative to existing methods. Next, we show that jointly modeling pleiotropic risk regions improves fine-mapping resolution compared to standard single trait and pleiotropic fine mapping strategies. We report a reduction in the number of SNPs required for follow-up in order to capture 90\% of the causal variants from 23 SNPs per locus using a single trait to 12 SNPs when fine-mapping two traits simultaneously. Finally, we analyze summary association data from a large-scale GWAS of lipids and show that these improvements are largely sustained in real data.

AVAILABILITY AND IMPLEMENTATION: The fastPAINTOR framework is implemented in the PAINTOR v3.0 package which is publicly available to the research community http://bogdan.bioinformatics.ucla.edu/software/paintor CONTACT: gkichaev@ucla.eduSupplementary information: Supplementary data are available at Bioinformatics online.

}, keywords = {Chromosome Mapping, Genetic Diseases, Inborn, Genetic Loci, Genetic Pleiotropy, Genome-Wide Association Study, Genomics, Humans, Linkage Disequilibrium, Lipid Metabolism, Models, Genetic, Polymorphism, Single Nucleotide, Software}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btw615}, author = {Kichaev, Gleb and Roytman, Megan and Johnson, Ruth and Eskin, Eleazar and Lindstr{\"o}m, Sara and Kraft, Peter and Pasaniuc, Bogdan} } @article {23, title = {Incorporation of Biological Knowledge Into the Study of Gene-Environment Interactions.}, journal = {Am J Epidemiol}, volume = {186}, year = {2017}, month = {2017 Oct 01}, pages = {771-777}, abstract = {

A growing knowledge base of genetic and environmental information has greatly enabled the study of disease risk factors. However, the computational complexity and statistical burden of testing all variants by all environments has required novel study designs and hypothesis-driven approaches. We discuss how incorporating biological knowledge from model organisms, functional genomics, and integrative approaches can empower the discovery of novel gene-environment interactions and discuss specific methodological considerations with each approach. We consider specific examples where the application of these approaches has uncovered effects of gene-environment interactions relevant to drug response and immunity, and we highlight how such improvements enable a greater understanding of the pathogenesis of disease and the realization of precision medicine.

}, keywords = {Animals, Disease, Gene-Environment Interaction, Genome-Wide Association Study, Genomics, Humans, Models, Animal, Sequence Analysis, RNA}, issn = {1476-6256}, doi = {10.1093/aje/kwx229}, author = {Ritchie, Marylyn D and Davis, Joe R and Aschard, Hugues and Battle, Alexis and Conti, David and Du, Mengmeng and Eskin, Eleazar and Fallin, M Daniele and Hsu, Li and Kraft, Peter and Moore, Jason H and Pierce, Brandon L and Bien, Stephanie A and Thomas, Duncan C and Wei, Peng and Montgomery, Stephen B} } @article {24, title = {Lessons Learned From Past Gene-Environment Interaction Successes.}, journal = {Am J Epidemiol}, volume = {186}, year = {2017}, month = {2017 Oct 01}, pages = {778-786}, abstract = {

Genetic and environmental factors are both known to contribute to susceptibility to complex diseases. Therefore, the study of gene-environment interaction (G{\texttimes}E) has been a focus of research for several years. In this article, select examples of G{\texttimes}E from the literature are described to highlight different approaches and underlying principles related to the success of these studies. These examples can be broadly categorized as studies of single metabolism genes, genes in complex metabolism pathways, ranges of exposure levels, functional approaches and model systems, and pharmacogenomics. Some studies illustrated the success of studying exposure metabolism for which candidate genes can be identified. Moreover, some G{\texttimes}E successes depended on the availability of high-quality exposure assessment and longitudinal measures, study populations with a wide range of exposure levels, and the inclusion of ethnically and geographically diverse populations. In several examples, large population sizes were required to detect G{\texttimes}Es. Other examples illustrated the impact of accurately defining scale of the interactions (i.e., additive or multiplicative). Last, model systems and functional approaches provided insights into G{\texttimes}E in several examples. Future studies may benefit from these lessons learned.

}, keywords = {Biomedical Research, Disease, Environmental Exposure, Gene-Environment Interaction, Genetic Predisposition to Disease, Genetic Variation, Genome-Wide Association Study, Humans, Models, Biological}, issn = {1476-6256}, doi = {10.1093/aje/kwx230}, author = {Ritz, Beate R and Chatterjee, Nilanjan and Garcia-Closas, Montserrat and Gauderman, W James and Pierce, Brandon L and Kraft, Peter and Tanner, Caroline M and Mechanic, Leah E and McAllister, Kimberly} } @article {17, title = {Leveraging blood and tissue CD4+ T cell heterogeneity at the single cell level to identify mechanisms of disease in rheumatoid arthritis.}, journal = {Curr Opin Immunol}, volume = {49}, year = {2017}, month = {2017 Dec}, pages = {27-36}, abstract = {

CD4+ T cells have been long known to play an important role in the pathogenesis of rheumatoid arthritis (RA), but the specific cell populations and states that drive the disease have been challenging to identify with low dimensional single cell data and bulk assays. The advent of high dimensional single cell technologies-like single cell RNA-seq or mass cytometry-has offered promise to defining key populations, but brings new methodological and statistical challenges. Recent single cell profiling studies have revealed a broad diversity of cell types among CD4+ T cells, identifying novel populations that are expanded or altered in RA. Here, we will review recent findings on CD4+ T cell heterogeneity and RA that have come from single cell profiling studies and discuss the best practices for conducting these studies.

}, keywords = {Animals, Arthritis, Rheumatoid, Blood Cells, CD4-Positive T-Lymphocytes, Datasets as Topic, Flow Cytometry, Humans, Immunophenotyping, Organ Specificity, Single-Cell Analysis, T-Lymphocyte Subsets}, issn = {1879-0372}, doi = {10.1016/j.coi.2017.08.005}, author = {Fonseka, Chamith Y and Rao, Deepak A and Raychaudhuri, Soumya} } @article {8, title = {Limited statistical evidence for shared genetic effects of eQTLs and autoimmune-disease-associated loci in three major immune-cell types.}, journal = {Nat Genet}, volume = {49}, year = {2017}, month = {2017 Apr}, pages = {600-605}, abstract = {

Most autoimmune-disease-risk effects identified by genome-wide association studies (GWAS) localize to open chromatin with gene-regulatory activity. GWAS loci are also enriched in expression quantitative trait loci (eQTLs), thus suggesting that most risk variants alter gene expression. However, because causal variants are difficult to identify, and cis-eQTLs occur frequently, it remains challenging to identify specific instances of disease-relevant changes to gene regulation. Here, we used a novel joint likelihood framework with higher resolution than that of previous methods to identify loci where autoimmune-disease risk and an eQTL are driven by a single shared genetic effect. Using eQTLs from three major immune subpopulations, we found shared effects in only \~{}25\% of the loci examined. Thus, we show that a fraction of gene-regulatory changes suggest strong mechanistic hypotheses for disease risk, but we conclude that most risk mechanisms are not likely to involve changes in basal gene expression.

}, keywords = {Autoimmune Diseases, Gene Expression, Gene Regulatory Networks, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Immunity, Polymorphism, Single Nucleotide, Quantitative Trait Loci}, issn = {1546-1718}, doi = {10.1038/ng.3795}, author = {Chun, Sung and Casparino, Alexandra and Patsopoulos, Nikolaos A and Croteau-Chonka, Damien C and Raby, Benjamin A and De Jager, Philip L and Sunyaev, Shamil R and Cotsapas, Chris} } @article {38, title = {Linkage disequilibrium-dependent architecture of human complex traits shows action of negative selection.}, journal = {Nat Genet}, volume = {49}, year = {2017}, month = {2017 Oct}, pages = {1421-1427}, abstract = {

Recent work has hinted at the linkage disequilibrium (LD)-dependent architecture of human complex traits, where SNPs with low levels of LD (LLD) have larger per-SNP heritability. Here we analyzed summary statistics from 56 complex traits (average N = 101,401) by extending stratified LD score regression to continuous annotations. We determined that SNPs with low LLD have significantly larger per-SNP heritability and that roughly half of this effect can be explained by functional annotations negatively correlated with LLD, such as DNase I hypersensitivity sites (DHSs). The remaining signal is largely driven by our finding that more recent common variants tend to have lower LLD and to explain more heritability (P = 2.38 {\texttimes} 10); the youngest 20\% of common SNPs explain 3.9 times more heritability than the oldest 20\%, consistent with the action of negative selection. We also inferred jointly significant effects of other LD-related annotations and confirmed via forward simulations that they jointly predict deleterious effects.

}, keywords = {Alleles, Chi-Square Distribution, Datasets as Topic, Genetic Fitness, Genetic Variation, Humans, Linkage Disequilibrium, Models, Genetic, Molecular Sequence Annotation, Multifactorial Inheritance, Polymorphism, Single Nucleotide, Selection, Genetic}, issn = {1546-1718}, doi = {10.1038/ng.3954}, author = {Gazal, Steven and Finucane, Hilary K and Furlotte, Nicholas A and Loh, Po-Ru and Palamara, Pier Francesco and Liu, Xuanyao and Schoech, Armin and Bulik-Sullivan, Brendan and Neale, Benjamin M and Gusev, Alexander and Price, Alkes L} } @article {13, title = {Negative selection in humans and fruit flies involves synergistic epistasis.}, journal = {Science}, volume = {356}, year = {2017}, month = {2017 May 05}, pages = {539-542}, abstract = {

Negative selection against deleterious alleles produced by mutation influences within-population variation as the most pervasive form of natural selection. However, it is not known whether deleterious alleles affect fitness independently, so that cumulative fitness loss depends exponentially on the number of deleterious alleles, or synergistically, so that each additional deleterious allele results in a larger decrease in relative fitness. Negative selection with synergistic epistasis should produce negative linkage disequilibrium between deleterious alleles and, therefore, an underdispersed distribution of the number of deleterious alleles in the genome. Indeed, we detected underdispersion of the number of rare loss-of-function alleles in eight independent data sets from human and fly populations. Thus, selection against rare protein-disrupting alleles is characterized by synergistic epistasis, which may explain how human and fly populations persist despite high genomic mutation rates.

}, keywords = {Alleles, Animals, Drosophila melanogaster, Epistasis, Genetic, Genetic Fitness, Genome, Human, Genome, Insect, Humans, Linkage Disequilibrium, Mutation Rate, Mutation, Missense, Selection, Genetic}, issn = {1095-9203}, doi = {10.1126/science.aah5238}, author = {Sohail, Mashaal and Vakhrusheva, Olga A and Sul, Jae Hoon and Pulit, Sara L and Francioli, Laurent C and van den Berg, Leonard H and Veldink, Jan H and de Bakker, Paul I W and Bazykin, Georgii A and Kondrashov, Alexey S and Sunyaev, Shamil R} } @article {9, title = {Population- and individual-specific regulatory variation in Sardinia.}, journal = {Nat Genet}, volume = {49}, year = {2017}, month = {2017 May}, pages = {700-707}, abstract = {

Genetic studies of complex traits have mainly identified associations with noncoding variants. To further determine the contribution of regulatory variation, we combined whole-genome and transcriptome data for 624 individuals from Sardinia to identify common and rare variants that influence gene expression and splicing. We identified 21,183 expression quantitative trait loci (eQTLs) and 6,768 splicing quantitative trait loci (sQTLs), including 619 new QTLs. We identified high-frequency QTLs and found evidence of selection near genes involved in malarial resistance and increased multiple sclerosis risk, reflecting the epidemiological history of Sardinia. Using family relationships, we identified 809 segregating expression outliers (median z score of 2.97), averaging 13.3 genes per individual. Outlier genes were enriched for proximal rare variants, providing a new approach to study large-effect regulatory variants and their relevance to traits. Our results provide insight into the effects of regulatory variants and their relationship to population history and individual genetic risk.

}, keywords = {Alternative Splicing, Chromosome Mapping, Family Health, Female, Gene Expression Profiling, Genetic Predisposition to Disease, Genetic Variation, Genetics, Population, Genome-Wide Association Study, Genotype, Humans, Italy, Male, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Transcription Initiation Site}, issn = {1546-1718}, doi = {10.1038/ng.3840}, author = {Pala, Mauro and Zappala, Zachary and Marongiu, Mara and Li, Xin and Davis, Joe R and Cusano, Roberto and Crobu, Francesca and Kukurba, Kimberly R and Gloudemans, Michael J and Reinier, Frederic and Berutti, Riccardo and Piras, Maria G and Mulas, Antonella and Zoledziewska, Magdalena and Marongiu, Michele and Sorokin, Elena P and Hess, Gaelen T and Smith, Kevin S and Busonero, Fabio and Maschio, Andrea and Steri, Maristella and Sidore, Carlo and Sanna, Serena and Fiorillo, Edoardo and Bassik, Michael C and Sawcer, Stephen J and Battle, Alexis and Novembre, John and Jones, Chris and Angius, Andrea and Abecasis, Gon{\c c}alo R and Schlessinger, David and Cucca, Francesco and Montgomery, Stephen B} } @article {16, title = {Testing for the indirect effect under the null for genome-wide mediation analyses.}, journal = {Genet Epidemiol}, volume = {41}, year = {2017}, month = {2017 Dec}, pages = {824-833}, abstract = {

Mediation analysis helps researchers assess whether part or all of an exposure{\textquoteright}s effect on an outcome is due to an intermediate variable. The indirect effect can help in designing interventions on the mediator as opposed to the exposure and better understanding the outcome{\textquoteright}s mechanisms. Mediation analysis has seen increased use in genome-wide epidemiological studies to test for an exposure of interest being mediated through a genomic measure such as gene expression or DNA methylation (DNAm). Testing for the indirect effect is challenged by the fact that the null hypothesis is composite. We examined the performance of commonly used mediation testing methods for the indirect effect in genome-wide mediation studies. When there is no association between the exposure and the mediator and no association between the mediator and the outcome, we show that these common tests are overly conservative. This is a case that will arise frequently in genome-wide mediation studies. Caution is hence needed when applying the commonly used mediation tests in genome-wide mediation studies. We evaluated the performance of these methods using simulation studies, and performed an epigenome-wide mediation association study in the Normative Aging Study, analyzing DNAm as a mediator of the effect of pack-years on FEV .

}, keywords = {Basic Helix-Loop-Helix Transcription Factors, DNA Methylation, Epigenomics, Genome-Wide Association Study, Humans, Lung Neoplasms, Models, Genetic, Repressor Proteins}, issn = {1098-2272}, doi = {10.1002/gepi.22084}, author = {Barfield, Richard and Shen, Jincheng and Just, Allan C and Vokonas, Pantel S and Schwartz, Joel and Baccarelli, Andrea A and VanderWeele, Tyler J and Lin, Xihong} } @article {25, title = {VEXOR: an integrative environment for prioritization of functional variants in fine-mapping analysis.}, journal = {Bioinformatics}, volume = {33}, year = {2017}, month = {2017 May 01}, pages = {1389-1391}, abstract = {

MOTIVATION: The identification of the functional variants responsible for observed genome-wide association studies (GWAS) signals is one of the most challenging tasks of the post-GWAS research era. Several tools have been developed to annotate genetic variants by their genomic location and potential functional implications. Each of these tools has its own requirements and internal logic, which forces the user to become acquainted with each interface.

RESULTS: From an awareness of the amount of work needed to analyze a single locus, we have built a flexible, versatile and easy-to-use web interface designed to help in prioritizing variants and predicting their potential functional implications. This interface acts as a single-point of entry linking association results with reference tools and relevant experiments.

AVAILABILITY AND IMPLEMENTATION: VEXOR is an integrative web application implemented through the Shiny framework and available at: http://romix.genome.ulaval.ca/vexor.

CONTACT: arnaud.droit@crchuq.ulaval.ca.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

}, keywords = {Genome, Human, Genome-Wide Association Study, Genomics, Humans, Polymorphism, Genetic, Sequence Analysis, DNA, Software}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btw826}, author = {Lema{\c c}on, Audrey and Joly Beauparlant, Charles and Soucy, Penny and Allen, Jamie and Easton, Douglas and Kraft, Peter and Simard, Jacques and Droit, Arnaud} } @article {47, title = {Weighted pseudolikelihood for SNP set analysis with multiple secondary outcomes in case-control genetic association studies.}, journal = {Biometrics}, volume = {73}, year = {2017}, month = {2017 Dec}, pages = {1210-1220}, abstract = {

We propose a weighted pseudolikelihood method for analyzing the association of a SNP set, example, SNPs in a gene or a genetic pathway or network, with multiple secondary phenotypes in case-control genetic association studies. To boost analysis power, we assume that the SNP-specific effects are shared across all secondary phenotypes using a scaled mean model. We estimate regression parameters using Inverse Probability Weighted (IPW) estimating equations obtained from the weighted pseudolikelihood, which accounts for case-control sampling to prevent potential ascertainment bias. To test the effect of a SNP set, we propose a weighted variance component pseudo-score test. We also propose a penalized IPW pseudolikelihood method for selecting a subset of SNPs that are associated with the multiple secondary phenotypes. We show that the proposed variable selection procedure has the oracle properties and is robust to misspecification of the correlation structure among secondary phenotypes. We select the tuning parameter using a weighted Bayesian Information-like Criterion (wBIC). We evaluate the finite sample performance of the proposed methods via simulations, and illustrate the methods by the analysis of the multiple secondary smoking behavior outcomes in a lung cancer case-control genetic association study.

}, keywords = {Case-Control Studies, Computer Simulation, Genetic Association Studies, Humans, Likelihood Functions, Lung Neoplasms, Phenotype, Polymorphism, Single Nucleotide, Smoking}, issn = {1541-0420}, doi = {10.1111/biom.12680}, author = {Sofer, Tamar and Schifano, Elizabeth D and Christiani, David C and Lin, Xihong} } @article {33, title = {Whole-transcriptome sequencing in blood provides a diagnosis of spinal muscular atrophy with progressive myoclonic epilepsy.}, journal = {Hum Mutat}, volume = {38}, year = {2017}, month = {2017 Jun}, pages = {611-614}, abstract = {

At least 15\% of the disease-causing mutations affect mRNA splicing. Many splicing mutations are missed in a clinical setting due to limitations of in silico prediction algorithms or their location in noncoding regions. Whole-transcriptome sequencing is a promising new tool to identify these mutations; however, it will be a challenge to obtain disease-relevant tissue for RNA. Here, we describe an individual with a sporadic atypical spinal muscular atrophy, in whom clinical DNA sequencing reported one pathogenic ASAH1 mutation (c.458A>G;p.Tyr153Cys). Transcriptome sequencing on patient leukocytes identified a highly significant and atypical ASAH1 isoform not explained by c.458A>G(p<10 ). Subsequent Sanger-sequencing identified the splice mutation responsible for the isoform (c.504A>C;p.Lys168Asn) and provided a molecular diagnosis of autosomal-recessive spinal muscular atrophy with progressive myoclonic epilepsy. Our findings demonstrate the utility of RNA sequencing from blood to identify splice-impacting disease mutations for nonhematological conditions, providing a diagnosis for these otherwise unsolved patients.

}, keywords = {Acid Ceramidase, Child, Preschool, Humans, Male, Muscular Atrophy, Spinal, Mutation, Myoclonic Epilepsies, Progressive, Pathology, Molecular, RNA Splicing, Sequence Analysis, DNA, Transcriptome}, issn = {1098-1004}, doi = {10.1002/humu.23211}, author = {Kernohan, Kristin D and Fr{\'e}sard, Laure and Zappala, Zachary and Hartley, Taila and Smith, Kevin S and Wagner, Justin and Xu, Hongbin and McBride, Arran and Bourque, Pierre R and Consortium, Care Rare Canada and Bennett, Steffany A L and Dyment, David A and Boycott, Kym M and Montgomery, Stephen B and Warman Chardon, Jodi} }