Association between Smoking History and Tumor Mutation Burden in Advanced Non-Small Cell Lung Cancer. - Wang, Xinan; Ricciuti, Biagio; Nguyen, Tom; Li, Xihao; Rabin, Michael S; Awad, Mark M; Lin, Xihong; Johnson, Bruce E; Christiani, David C (Cancer Res, 2021)
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'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.

A cross-population atlas of genetic associations for 220 human phenotypes. - Sakaue, Saori; Kanai, Masahiro; Tanigawa, Yosuke; Karjalainen, Juha; Kurki, Mitja; Koshiba, Seizo; Narita, Akira; Konuma, Takahiro; Yamamoto, Kenichi; Akiyama, Masato; Ishigaki, Kazuyoshi; Suzuki, Akari; et al. (Nat Genet, 2021)
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

Genetics of 35 blood and urine biomarkers in the UK Biobank. - Sinnott-Armstrong, Nasa; Tanigawa, Yosuke; Amar, David; Mars, Nina; Benner, Christian; Aguirre, Matthew; Venkataraman, Guhan Ram; Wainberg, Michael; Ollila, Hanna M; Kiiskinen, Tuomo; Havulinna, Aki S; Pirruccello, James P; et al. (Nat Genet, 2021)
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 'multi-PRS' 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

Graphical analysis for phenome-wide causal discovery in genotyped population-scale biobanks. - Amar, David; Sinnott-Armstrong, Nasa; Ashley, Euan A; Rivas, Manuel A (Nat Commun, 2021)
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

Integration of multiomic annotation data to prioritize and characterize inflammation and immune-related risk variants in squamous cell lung cancer. - Sun, Ryan; Xu, Miao; Li, Xihao; Gaynor, Sheila; Zhou, Hufeng; Li, Zilin; Bossé, Yohan; Lam, Stephen; Tsao, Ming-Sound; Tardon, Adonina; Chen, Chu; Doherty, Jennifer; et al. (Genet Epidemiol, 2021)
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.

Polygenic risk modeling with latent trait-related genetic components. - Aguirre, Matthew; Tanigawa, Yosuke; Venkataraman, Guhan Ram; Tibshirani, Rob; Hastie, Trevor; Rivas, Manuel A (Eur J Hum Genet, 2021)
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'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.

Population sequencing data reveal a compendium of mutational processes in the human germ line. - Seplyarskiy, Vladimir B; Soldatov, Ruslan A; Koch, Evan; McGinty, Ryan J; Goldmann, Jakob M; Hernandez, Ryan D; Barnes, Kathleen; Correa, Adolfo; Burchard, Esteban G; Ellinor, Patrick T; McGarvey, Stephen T; Mitchell, Braxton D; et al. (Science, 2021)
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

Population-scale tissue transcriptomics maps long non-coding RNAs to complex disease. - de Goede, Olivia M; Nachun, Daniel C; Ferraro, Nicole M; Gloudemans, Michael J; Rao, Abhiram S; Smail, Craig; Eulalio, Tiffany Y; Aguet, Francois; Ng, Bernard; Xu, Jishu; Barbeira, Alvaro N; Castel, Stephane E; et al. (Cell, 2021)
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

Sex-specific genetic effects across biomarkers. - Flynn, Emily; Tanigawa, Yosuke; Rodriguez, Fatima; Altman, Russ B; Sinnott-Armstrong, Nasa; Rivas, Manuel A (Eur J Hum Genet, 2021)
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.

Synaptic processes and immune-related pathways implicated in Tourette syndrome. - Tsetsos, Fotis; Yu, Dongmei; Sul, Jae Hoon; Huang, Alden Y; Illmann, Cornelia; Osiecki, Lisa; Darrow, Sabrina M; Hirschtritt, Matthew E; Greenberg, Erica; Muller-Vahl, Kirsten R; Stuhrmann, Manfred; Dion, Yves; et al. (Transl Psychiatry, 2021)
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.

Allelic Heterogeneity at the CRP Locus Identified by Whole-Genome Sequencing in Multi-ancestry Cohorts. - Raffield, Laura M; Iyengar, Apoorva K; Wang, Biqi; Gaynor, Sheila M; Spracklen, Cassandra N; Zhong, Xue; Kowalski, Madeline H; Salimi, Shabnam; Polfus, Linda M; Benjamin, Emelia J; Bis, Joshua C; Bowler, Russell; et al. (Am J Hum Genet, 2020)
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× 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

Assessing Digital Phenotyping to Enhance Genetic Studies of Human Diseases. - DeBoever, Christopher; Tanigawa, Yosuke; Aguirre, Matthew; McInnes, Greg; Lavertu, Adam; Rivas, Manuel A (Am J Hum Genet, 2020)
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

A common variant in PNPLA3 is associated with age at diagnosis of NAFLD in patients from a multi-ethnic biobank. - Walker, Ryan W; Belbin, Gillian M; Sorokin, Elena P; Van Vleck, Tielman; Wojcik, Genevieve L; Moscati, Arden; Gignoux, Christopher R; Cho, Judy; Abul-Husn, Noura S; Nadkarni, Girish; Kenny, Eimear E; Loos, Ruth J F (J Hepatol, 2020)
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 × 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.

Dynamic incorporation of multiple in silico functional annotations empowers rare variant association analysis of large whole-genome sequencing studies at scale. - Li, Xihao; Li, Zilin; Zhou, Hufeng; Gaynor, Sheila M; Liu, Yaowu; Chen, Han; Sun, Ryan; Dey, Rounak; Arnett, Donna K; Aslibekyan, Stella; Ballantyne, Christie M; Bielak, Lawrence F; et al. (Nat Genet, 2020)
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 'annotation principal components', 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

Efficient Estimation and Applications of Cross-Validated Genetic Predictions to Polygenic Risk Scores and Linear Mixed Models. - Mefford, Joel; Park, Danny; Zheng, Zhili; Ko, Arthur; Ala-Korpela, Mika; Laakso, Markku; Pajukanta, Päivi; Yang, Jian; Witte, John; Zaitlen, Noah (J Comput Biol, 2020)
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.

Evidence for secondary-variant genetic burden and non-random distribution across biological modules in a recessive ciliopathy. - Kousi, Maria; Söylemez, Onuralp; Ozanturk, Aysegül; Mourtzi, Niki; Akle, Sebastian; Jungreis, Irwin; Muller, Jean; Cassa, Christopher A; Brand, Harrison; Mokry, Jill Anne; Wolf, Maxim Y; Sadeghpour, Azita; et al. (Nat Genet, 2020)
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

A fast and scalable framework for large-scale and ultrahigh-dimensional sparse regression with application to the UK Biobank. - Qian, Junyang; Tanigawa, Yosuke; Du, Wenfei; Aguirre, Matthew; Chang, Chris; Tibshirani, Robert; Rivas, Manuel A; Hastie, Trevor (PLoS Genet, 2020)
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

FasTag: Automatic text classification of unstructured medical narratives. - Venkataraman, Guhan Ram; Pineda, Arturo Lopez; Bear Don't Walk Iv, Oliver J; Zehnder, Ashley M; Ayyar, Sandeep; Page, Rodney L; Bustamante, Carlos D; Rivas, Manuel A (PLoS One, 2020)
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

Genetic diagnoses in epilepsy: The impact of dynamic exome analysis in a pediatric cohort. - Rochtus, Anne; Olson, Heather E; Smith, Lacey; Keith, Louisa G; El Achkar, Christelle; Taylor, Alan; Mahida, Sonal; Park, Meredith; Kelly, McKenna; Shain, Catherine; Rockowitz, Shira; Rosen Sheidley, Beth; et al. (Epilepsia, 2020)
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

Impact of admixture and ancestry on eQTL analysis and GWAS colocalization in GTEx. - Gay, Nicole R; Gloudemans, Michael; Antonio, Margaret L; Abell, Nathan S; Balliu, Brunilda; Park, YoSon; Martin, Alicia R; Musharoff, Shaila; Rao, Abhiram S; Aguet, Francois; Barbeira, Alvaro N; Bonazzola, Rodrigo; et al. (Genome Biol, 2020)
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.

Improving the trans-ancestry portability of polygenic risk scores by prioritizing variants in predicted cell-type-specific regulatory elements. - Amariuta, Tiffany; Ishigaki, Kazuyoshi; Sugishita, Hiroki; Ohta, Tazro; Koido, Masaru; Dey, Kushal K; Matsuda, Koichi; Murakami, Yoshinori; Price, Alkes L; Kawakami, Eiryo; Terao, Chikashi; Raychaudhuri, Soumya (Nat Genet, 2020)
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

Lung Function in African American Children with Asthma Is Associated with Novel Regulatory Variants of the KIT Ligand and Gene-By-Air-Pollution Interaction. - Mak, Angel C Y; Sajuthi, Satria; Joo, Jaehyun; Xiao, Shujie; Sleiman, Patrick M; White, Marquitta J; Lee, Eunice Y; Saef, Benjamin; Hu, Donglei; Gui, Hongsheng; Keys, Kevin L; Lurmann, Fred; et al. (Genetics, 2020)
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 × 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.

Modeling epistasis in mice and yeast using the proportion of two or more distinct genetic backgrounds: Evidence for "polygenic epistasis". - Rau, Christoph D; Gonzales, Natalia M; Bloom, Joshua S; Park, Danny; Ayroles, Julien; Palmer, Abraham A; Lusis, Aldons J; Zaitlen, Noah (PLoS Genet, 2020)
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

Multi-Ethnic Genome-Wide Association Study of Decomposed Cardioelectric Phenotypes Illustrates Strategies to Identify and Characterize Evidence of Shared Genetic Effects for Complex Traits. - Baldassari, Antoine R; Sitlani, Colleen M; Highland, Heather M; Arking, Dan E; Buyske, Steve; Darbar, Dawood; Gondalia, Rahul; Graff, Misa; Guo, Xiuqing; Heckbert, Susan R; Hindorff, Lucia A; Hodonsky, Chani J; et al. (Circ Genom Precis Med, 2020)
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 <5×10). 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.

Phenome-based approach identifies RIC1-linked Mendelian syndrome through zebrafish models, biobank associations and clinical studies. - Unlu, Gokhan; Qi, Xinzi; Gamazon, Eric R; Melville, David B; Patel, Nisha; Rushing, Amy R; Hashem, Mais; Al-Faifi, Abdullah; Chen, Rui; Li, Bingshan; Cox, Nancy J; Alkuraya, Fowzan S; et al. (Nat Med, 2020)
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

A phenome-wide association study of 26 mendelian genes reveals phenotypic expressivity of common and rare variants within the general population. - Tcheandjieu, Catherine; Aguirre, Matthew; Gustafsson, Stefan; Saha, Priyanka; Potiny, Praneetha; Haendel, Melissa; Ingelsson, Erik; Rivas, Manuel A; Priest, James R (PLoS Genet, 2020)
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

A positively selected FBN1 missense variant reduces height in Peruvian individuals. - Asgari, Samira; Luo, Yang; Akbari, Ali; Belbin, Gillian M; Li, Xinyi; Harris, Daniel N; Selig, Martin; Bartell, Eric; Calderon, Roger; Slowikowski, Kamil; Contreras, Carmen; Yataco, Rosa; et al. (Nature, 2020)
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

Transcriptomic signatures across human tissues identify functional rare genetic variation. - Ferraro, Nicole M; Strober, Benjamin J; Einson, Jonah; Abell, Nathan S; Aguet, Francois; Barbeira, Alvaro N; Brandt, Margot; Bucan, Maja; Castel, Stephane E; Davis, Joe R; Greenwald, Emily; Hess, Gaelen T; et al. (Science, 2020)
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

Type 2 and interferon inflammation regulate SARS-CoV-2 entry factor expression in the airway epithelium. - Sajuthi, Satria P; DeFord, Peter; Li, Yingchun; Jackson, Nathan D; Montgomery, Michael T; Everman, Jamie L; Rios, Cydney L; Pruesse, Elmar; Nolin, James D; Plender, Elizabeth G; Wechsler, Michael E; Mak, Angel C Y; et al. (Nat Commun, 2020)
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

Type 2 and interferon inflammation strongly regulate SARS-CoV-2 related gene expression in the airway epithelium. - Sajuthi, Satria P; DeFord, Peter; Jackson, Nathan D; Montgomery, Michael T; Everman, Jamie L; Rios, Cydney L; Pruesse, Elmar; Nolin, James D; Plender, Elizabeth G; Wechsler, Michael E; Mak, Angel Cy; Eng, Celeste; et al. (bioRxiv, 2020)
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.

Whole-Genome and RNA Sequencing Reveal Variation and Transcriptomic Coordination in the Developing Human Prefrontal Cortex. - Werling, Donna M; Pochareddy, Sirisha; Choi, Jinmyung; An, Joon-Yong; Sheppard, Brooke; Peng, Minshi; Li, Zhen; Dastmalchi, Claudia; Santpere, Gabriel; Sousa, André M M; Tebbenkamp, Andrew T N; Kaur, Navjot; et al. (Cell Rep, 2020)
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

Adjusting for Principal Components of Molecular Phenotypes Induces Replicating False Positives. - Dahl, Andy; Guillemot, Vincent; Mefford, Joel; Aschard, Hugues; Zaitlen, Noah (Genetics, 2019)
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

Components of genetic associations across 2,138 phenotypes in the UK Biobank highlight adipocyte biology. - Tanigawa, Yosuke; Li, Jiehan; Justesen, Johanne M; Horn, Heiko; Aguirre, Matthew; DeBoever, Christopher; Chang, Chris; Narasimhan, Balasubramanian; Lage, Kasper; Hastie, Trevor; Park, Chong Y; Bejerano, Gill; et al. (Nat Commun, 2019)
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.

Error-prone bypass of DNA lesions during lagging-strand replication is a common source of germline and cancer mutations. - Seplyarskiy, Vladimir B; Akkuratov, Evgeny E; Akkuratova, Natalia; Andrianova, Maria A; Nikolaev, Sergey I; Bazykin, Georgii A; Adameyko, Igor; Sunyaev, Shamil R (Nat Genet, 2019)
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

Exome sequencing reveals a high prevalence of BRCA1 and BRCA2 founder variants in a diverse population-based biobank. - Abul-Husn, Noura S; Soper, Emily R; Odgis, Jacqueline A; Cullina, Sinead; Bobo, Dean; Moscati, Arden; Rodriguez, Jessica E; Loos, Ruth J F; Cho, Judy H; Belbin, Gillian M; Suckiel, Sabrina A; Kenny, Eimear E (Genome Med, 2019)
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.

Genome sequencing analysis identifies Epstein-Barr virus subtypes associated with high risk of nasopharyngeal carcinoma. - Xu, Miao; Yao, Youyuan; Chen, Hui; Zhang, Shanshan; Cao, Su-Mei; Zhang, Zhe; Luo, Bing; Liu, Zhiwei; Li, Zilin; Xiang, Tong; He, Guiping; Feng, Qi-Sheng; et al. (Nat Genet, 2019)
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 × 10 for SNP 162476_C; OR = 6.14, P = 2.40 × 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.

Global Biobank Engine: enabling genotype-phenotype browsing for biobank summary statistics. - McInnes, Gregory; Tanigawa, Yosuke; DeBoever, Chris; Lavertu, Adam; Olivieri, Julia Eve; Aguirre, Matthew; Rivas, Manuel A (Bioinformatics, 2019)
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

Identification of rare-disease genes using blood transcriptome sequencing and large control cohorts. - Frésard, Laure; Smail, Craig; Ferraro, Nicole M; Teran, Nicole A; Li, Xin; Smith, Kevin S; Bonner, Devon; Kernohan, Kristin D; Marwaha, Shruti; Zappala, Zachary; Balliu, Brunilda; Davis, Joe R; et al. (Nat Med, 2019)
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

Personalized Medicine and the Power of Electronic Health Records. - Abul-Husn, Noura S; Kenny, Eimear E (Cell, 2019)
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.

Phenome-wide Burden of Copy-Number Variation in the UK Biobank. - Aguirre, Matthew; Rivas, Manuel A; Priest, James (Am J Hum Genet, 2019)
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.

An ancestry-based approach for detecting interactions. - Park, Danny S; Eskin, Itamar; Kang, Eun Yong; Gamazon, Eric R; Eng, Celeste; Gignoux, Christopher R; Galanter, Joshua M; Burchard, Esteban; Ye, Chun J; Aschard, Hugues; Eskin, Eleazar; Halperin, Eran; et al. (Genet Epidemiol, 2018)
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<5×10-8. We show that two of the interactions in methylation data replicate, and the remaining six are significantly enriched for low P-values (P<1.8×10-6). 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

A common loss-of-function variant is associated with lower vitamin B concentration in African Americans. - Hu, Yao; Raffield, Laura M; Polfus, Linda M; Moscati, Arden; Nadkarni, Girish; Preuss, Michael H; Zhong, Xue; Wei, Qiang; Rich, Stephen S; Li, Yun; Wilson, James G; Correa, Adolfo; et al. (Blood, 2018)
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

Functional equivalence of genome sequencing analysis pipelines enables harmonized variant calling across human genetics projects. - Regier, Allison A; Farjoun, Yossi; Larson, David E; Krasheninina, Olga; Kang, Hyun Min; Howrigan, Daniel P; Chen, Bo-Juen; Kher, Manisha; Banks, Eric; Ames, Darren C; English, Adam C; Li, Heng; et al. (Nat Commun, 2018)
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

Imputation-Aware Tag SNP Selection To Improve Power for Large-Scale, Multi-ethnic Association Studies. - Wojcik, Genevieve L; Fuchsberger, Christian; Taliun, Daniel; Welch, Ryan; Martin, Alicia R; Shringarpure, Suyash; Carlson, Christopher S; Abecasis, Goncalo; Kang, Hyun Min; Boehnke, Michael; Bustamante, Carlos D; Gignoux, Christopher R; et al. (G3 (Bethesda), 2018)
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

Medical relevance of protein-truncating variants across 337,205 individuals in the UK Biobank study. - DeBoever, Christopher; Tanigawa, Yosuke; Lindholm, Malene E; McInnes, Greg; Lavertu, Adam; Ingelsson, Erik; Chang, Chris; Ashley, Euan A; Bustamante, Carlos D; Daly, Mark J; Rivas, Manuel A (Nat Commun, 2018)
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

Quantifying the Polygenic Contribution to Cutaneous Squamous Cell Carcinoma Risk. - Sordillo, Joanne E; Kraft, Peter; Wu, Ann Chen; Asgari, Maryam M (J Invest Dermatol, 2018)
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

Cohort-specific imputation of gene expression improves prediction of warfarin dose for African Americans. - Gottlieb, Assaf; Daneshjou, Roxana; DeGorter, Marianne; Bourgeois, Stephane; Svensson, Peter J; Wadelius, Mia; Deloukas, Panos; Montgomery, Stephen B; Altman, Russ B (Genome Med, 2017)
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

Covariate selection for association screening in multiphenotype genetic studies. - Aschard, Hugues; Guillemot, Vincent; Vilhjalmsson, Bjarni; Patel, Chirag J; Skurnik, David; Ye, Chun J; Wolpin, Brian; Kraft, Peter; Zaitlen, Noah (Nat Genet, 2017)
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

Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases. - McAllister, Kimberly; Mechanic, Leah E; Amos, Christopher; Aschard, Hugues; Blair, Ian A; Chatterjee, Nilanjan; Conti, David; Gauderman, W James; Hsu, Li; Hutter, Carolyn M; Jankowska, Marta M; Kerr, Jacqueline; et al. (Am J Epidemiol, 2017)
Recently, many new approaches, study designs, and statistical and analytical methods have emerged for studying gene-environment interactions (G×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×E discovery. This paper highlights current and critical issues and themes in G×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

Genetic identification of a common collagen disease in puerto ricans via identity-by-descent mapping in a health system. - Belbin, Gillian Morven; Odgis, Jacqueline; Sorokin, Elena P; Yee, Muh-Ching; Kohli, Sumita; Glicksberg, Benjamin S; Gignoux, Christopher R; Wojcik, Genevieve L; Van Vleck, Tielman; Jeff, Janina M; Linderman, Michael; Schurmann, Claudia; et al. (Elife, 2017)
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

A genetic stochastic process model for genome-wide joint analysis of biomarker dynamics and disease susceptibility with longitudinal data. - He, Liang; Zhbannikov, Ilya; Arbeev, Konstantin G; Yashin, Anatoliy I; Kulminski, Alexander M (Genet Epidemiol, 2017)
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 × 10 ). We also found a SNP rs1551133 located on 2q14.2 that reversed the effect of BMI on T2D (P = 6.70 × 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

Incorporation of Biological Knowledge Into the Study of Gene-Environment Interactions. - Ritchie, Marylyn D; Davis, Joe R; Aschard, Hugues; Battle, Alexis; Conti, David; Du, Mengmeng; Eskin, Eleazar; Fallin, M Daniele; Hsu, Li; Kraft, Peter; Moore, Jason H; Pierce, Brandon L; et al. (Am J Epidemiol, 2017)
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

Negative selection in humans and fruit flies involves synergistic epistasis. - Sohail, Mashaal; Vakhrusheva, Olga A; Sul, Jae Hoon; Pulit, Sara L; Francioli, Laurent C; van den Berg, Leonard H; Veldink, Jan H; de Bakker, Paul I W; Bazykin, Georgii A; Kondrashov, Alexey S; Sunyaev, Shamil R (Science, 2017)
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

Population- and individual-specific regulatory variation in Sardinia. - Pala, Mauro; Zappala, Zachary; Marongiu, Mara; Li, Xin; Davis, Joe R; Cusano, Roberto; Crobu, Francesca; Kukurba, Kimberly R; Gloudemans, Michael J; Reinier, Frederic; Berutti, Riccardo; Piras, Maria G; et al. (Nat Genet, 2017)
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

VEXOR: an integrative environment for prioritization of functional variants in fine-mapping analysis. - Lemaçon, Audrey; Joly Beauparlant, Charles; Soucy, Penny; Allen, Jamie; Easton, Douglas; Kraft, Peter; Simard, Jacques; Droit, Arnaud (Bioinformatics, 2017)
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