@article {117, title = {A deoxyribonuclease 1-like 3 genetic variant associates with asthma exacerbations.}, journal = {J Allergy Clin Immunol}, volume = {147}, year = {2021}, month = {2021 Mar}, pages = {1095-1097.e10}, issn = {1097-6825}, doi = {10.1016/j.jaci.2020.09.027}, author = {Herrera-Luis, Esther and Lorenzo-Diaz, Fabian and Samedy-Bates, Lesly-Anne and Eng, Celeste and Villar, Jes{\'u}s and Rodriguez-Santana, Jose R and Burchard, Esteban G and Pino-Yanes, Maria} } @article {98, title = {Genome-wide association study reveals a novel locus for asthma with severe exacerbations in diverse populations.}, journal = {Pediatr Allergy Immunol}, volume = {32}, year = {2021}, month = {2021 Jan}, pages = {106-115}, abstract = {

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

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

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

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

}, issn = {1399-3038}, doi = {10.1111/pai.13337}, author = {Herrera-Luis, Esther and Espuela-Ortiz, Antonio and Lorenzo-Diaz, Fabian and Keys, Kevin L and Mak, Angel C Y and Eng, Celeste and Huntsman, Scott and Villar, Jes{\'u}s and Rodriguez-Santana, Jose R and Burchard, Esteban G and Pino-Yanes, Maria} } @article {92, title = {Integrative genomic analysis in African American children with asthma finds three novel loci associated with lung function.}, journal = {Genet Epidemiol}, volume = {45}, year = {2021}, month = {2021 Mar}, pages = {190-208}, abstract = {

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

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

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

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

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

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

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

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

}, issn = {1945-0826}, doi = {10.18865/ed.31.1.77}, author = {Contreras, Mar{\'\i}a G and Keys, Kevin and Maga{\~n}a, Joaquin and Goddard, Pag{\'e} C and Risse-Adams, Oona and Zeiger, Andrew M and Mak, Angel C Y and Samedy-Bates, Lesly-Anne and Neophytou, Andreas M and Lee, Eunice and Thakur, Neeta and Elhawary, Jennifer R and Hu, Donglei and Huntsman, Scott and Eng, Celeste and Hu, Ting and Burchard, Esteban G and White, Marquitta J} } @article {145, title = {Population sequencing data reveal a compendium of mutational processes in the human germ line.}, journal = {Science}, volume = {373}, year = {2021}, month = {2021 08 27}, pages = {1030-1035}, abstract = {

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

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

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

}, keywords = {Coronary Artery Disease, Diabetes Mellitus, Type 1, Diabetes Mellitus, Type 2, Disease, Gene Expression Profiling, Genetic Variation, Humans, Inflammatory Bowel Diseases, Multifactorial Inheritance, Organ Specificity, Population, Quantitative Trait Loci, RNA, Long Noncoding, Transcriptome}, issn = {1097-4172}, doi = {10.1016/j.cell.2021.03.050}, author = {de Goede, Olivia M and Nachun, Daniel C and Ferraro, Nicole M and Gloudemans, Michael J and Rao, Abhiram S and Smail, Craig and Eulalio, Tiffany Y and Aguet, Francois and Ng, Bernard and Xu, Jishu and Barbeira, Alvaro N and Castel, Stephane E and Kim-Hellmuth, Sarah and Park, YoSon and Scott, Alexandra J and Strober, Benjamin J and Brown, Christopher D and Wen, Xiaoquan and Hall, Ira M and Battle, Alexis and Lappalainen, Tuuli and Im, Hae Kyung and Ardlie, Kristin G and Mostafavi, Sara and Quertermous, Thomas and Kirkegaard, Karla and Montgomery, Stephen B} } @article {127, title = {Rapid response to the alpha-1 adrenergic agent phenylephrine in the perioperative period is impacted by genomics and ancestry.}, journal = {Pharmacogenomics J}, volume = {21}, year = {2021}, month = {2021 Apr}, pages = {174-189}, abstract = {

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

}, issn = {1473-1150}, doi = {10.1038/s41397-020-00194-5}, author = {Wenric, Stephane and Jeff, Janina M and Joseph, Thomas and Yee, Muh-Ching and Belbin, Gillian M and Owusu Obeng, Aniwaa and Ellis, Stephen B and Bottinger, Erwin P and Gottesman, Omri and Levin, Matthew A and Kenny, Eimear E} } @article {146, title = {Sub-genic intolerance, ClinVar, and the epilepsies: A whole-exome sequencing study of 29,165 individuals.}, journal = {Am J Hum Genet}, volume = {108}, year = {2021}, month = {2021 06 03}, pages = {965-982}, abstract = {

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

}, keywords = {Case-Control Studies, Cohort Studies, Epilepsy, Exome, Genetic Markers, Genetic Predisposition to Disease, Genetic Testing, Genetic Variation, Humans, Phenotype, Whole Exome Sequencing}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2021.04.009} } @article {100, title = {On the cross-population generalizability of gene expression prediction models.}, journal = {PLoS Genet}, volume = {16}, year = {2020}, month = {2020 08}, pages = {e1008927}, abstract = {

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

}, keywords = {African Americans, Gene Expression Profiling, Genome-Wide Association Study, Humans, Models, Genetic, Quantitative Trait Loci, Reference Standards, RNA-Seq, Transcriptome}, issn = {1553-7404}, doi = {10.1371/journal.pgen.1008927}, author = {Keys, Kevin L and Mak, Angel C Y and White, Marquitta J and Eckalbar, Walter L and Dahl, Andrew W and Mefford, Joel and Mikhaylova, Anna V and Contreras, Mar{\'\i}a G and Elhawary, Jennifer R and Eng, Celeste and Hu, Donglei and Huntsman, Scott and Oh, Sam S and Salazar, Sandra and LeNoir, Michael A and Ye, Jimmie C and Thornton, Timothy A and Zaitlen, Noah and Burchard, Esteban G and Gignoux, Christopher R} } @article {82, title = {Electronic health record phenotypes associated with genetically regulated expression of CFTR and application to cystic fibrosis.}, journal = {Genet Med}, volume = {22}, year = {2020}, month = {2020 07}, pages = {1191-1200}, abstract = {

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

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

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

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

}, keywords = {Adult, Cystic Fibrosis, Cystic Fibrosis Transmembrane Conductance Regulator, Electronic Health Records, Humans, Mutation, Phenotype}, issn = {1530-0366}, doi = {10.1038/s41436-020-0786-5}, author = {Zhong, Xue and Yin, Zhijun and Jia, Gengjie and Zhou, Dan and Wei, Qiang and Faucon, Annika and Evans, Patrick and Gamazon, Eric R and Li, Bingshan and Tao, Ran and Rzhetsky, Andrey and Bastarache, Lisa and Cox, Nancy J} } @article {107, title = {Genome-Wide Analysis Reveals Mucociliary Remodeling of the Nasal Airway Epithelium Induced by Urban PM.}, journal = {Am J Respir Cell Mol Biol}, volume = {63}, year = {2020}, month = {2020 08}, pages = {172-184}, abstract = {

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

}, issn = {1535-4989}, doi = {10.1165/rcmb.2019-0454OC}, author = {Montgomery, Michael T and Sajuthi, Satria P and Cho, Seung-Hyun and Everman, Jamie L and Rios, Cydney L and Goldfarbmuren, Katherine C and Jackson, Nathan D and Saef, Benjamin and Cromie, Meghan and Eng, Celeste and Medina, Vivian and Elhawary, Jennifer R and Oh, Sam S and Rodriguez-Santana, Jose and Vladar, Eszter K and Burchard, Esteban G and Seibold, Max A} } @article {105, title = {Lung Function in African American Children with Asthma Is Associated with Novel Regulatory Variants of the KIT Ligand and Gene-By-Air-Pollution Interaction.}, journal = {Genetics}, volume = {215}, year = {2020}, month = {2020 07}, pages = {869-886}, abstract = {

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

}, issn = {1943-2631}, doi = {10.1534/genetics.120.303231}, author = {Mak, Angel C Y and Sajuthi, Satria and Joo, Jaehyun and Xiao, Shujie and Sleiman, Patrick M and White, Marquitta J and Lee, Eunice Y and Saef, Benjamin and Hu, Donglei and Gui, Hongsheng and Keys, Kevin L and Lurmann, Fred and Jain, Deepti and Abecasis, Goncalo and Kang, Hyun Min and Nickerson, Deborah A and Germer, Soren and Zody, Michael C and Winterkorn, Lara and Reeves, Catherine and Huntsman, Scott and Eng, Celeste and Salazar, Sandra and Oh, Sam S and Gilliland, Frank D and Chen, Zhanghua and Kumar, Rajesh and Mart{\'\i}nez, Fernando D and Wu, Ann Chen and Ziv, Elad and Hakonarson, Hakon and Himes, Blanca E and Williams, L Keoki and Seibold, Max A and Burchard, Esteban G} } @article {94, title = {Transcriptomic signatures across human tissues identify functional rare genetic variation.}, journal = {Science}, volume = {369}, year = {2020}, month = {2020 09 11}, abstract = {

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

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

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

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

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

}, doi = {10.1101/2020.04.09.034454}, author = {Sajuthi, Satria P and DeFord, Peter and Jackson, Nathan D and Montgomery, Michael T and Everman, Jamie L and Rios, Cydney L and Pruesse, Elmar and Nolin, James D and Plender, Elizabeth G and Wechsler, Michael E and Mak, Angel Cy and Eng, Celeste and Salazar, Sandra and Medina, Vivian and Wohlford, Eric M and Huntsman, Scott and Nickerson, Deborah A and Germer, Soren and Zody, Michael C and Abecasis, Goncalo and Kang, Hyun Min and Rice, Kenneth M and Kumar, Rajesh and Oh, Sam and Rodriguez-Santana, Jose and Burchard, Esteban G and Seibold, Max A} } @article {55, title = {Methods for the Analysis and Interpretation for Rare Variants Associated with Complex Traits.}, journal = {Curr Protoc Hum Genet}, volume = {101}, year = {2019}, month = {2019 04}, pages = {e83}, abstract = {

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

}, keywords = {Algorithms, Genetic Predisposition to Disease, Genome, Human, Genome-Wide Association Study, Genotype, High-Throughput Nucleotide Sequencing, Humans, Multifactorial Inheritance, Phenotype, Polymorphism, Single Nucleotide, Whole Exome Sequencing, Whole Genome Sequencing}, issn = {1934-8258}, doi = {10.1002/cphg.83}, author = {Weissenkampen, J Dylan and Jiang, Yu and Eckert, Scott and Jiang, Bibo and Li, Bingshan and Liu, Dajiang J} } @article {57, title = {Opportunities and challenges for transcriptome-wide association studies.}, journal = {Nat Genet}, volume = {51}, year = {2019}, month = {2019 04}, pages = {592-599}, abstract = {

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

}, keywords = {Crohn Disease, Genetic Predisposition to Disease, Genetic Variation, Genome-Wide Association Study, Humans, Lipoproteins, LDL, Quantitative Trait Loci, Schizophrenia, Transcriptome}, issn = {1546-1718}, doi = {10.1038/s41588-019-0385-z}, author = {Wainberg, Michael and Sinnott-Armstrong, Nasa and Mancuso, Nicholas and Barbeira, Alvaro N and Knowles, David A and Golan, David and Ermel, Raili and Ruusalepp, Arno and Quertermous, Thomas and Hao, Ke and Bj{\"o}rkegren, Johan L M and Im, Hae Kyung and Pasaniuc, Bogdan and Rivas, Manuel A and Kundaje, Anshul} } @article {69, title = {Ultra-Rare Genetic Variation in the Epilepsies: A Whole-Exome Sequencing Study of 17,606 Individuals.}, journal = {Am J Hum Genet}, volume = {105}, year = {2019}, month = {2019 Aug 01}, pages = {267-282}, abstract = {

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

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

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

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

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

}, keywords = {Black or African American, Black People, DNA Methylation, Epistasis, Genetic, Gene-Environment Interaction, Hispanic or Latino, Humans, Models, Genetic, Phenotype, White People}, issn = {1098-2272}, doi = {10.1002/gepi.22087}, author = {Park, Danny S and Eskin, Itamar and Kang, Eun Yong and Gamazon, Eric R and Eng, Celeste and Gignoux, Christopher R and Galanter, Joshua M and Burchard, Esteban and Ye, Chun J and Aschard, Hugues and Eskin, Eleazar and Halperin, Eran and Zaitlen, Noah} } @article {44, title = {Fine-mapping and functional studies highlight potential causal variants for rheumatoid arthritis and type 1 diabetes.}, journal = {Nat Genet}, volume = {50}, year = {2018}, month = {2018 Oct}, pages = {1366-1374}, abstract = {

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

}, keywords = {Alleles, Arthritis, Rheumatoid, Case-Control Studies, CD28 Antigens, Chromosome Mapping, CTLA-4 Antigen, Diabetes Mellitus, Type 1, Gene Frequency, Genetic Loci, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Jurkat Cells, Mutation, Polymorphism, Single Nucleotide, Quantitative Trait Loci, RNA, Long Noncoding, Tumor Necrosis Factor alpha-Induced Protein 3}, issn = {1546-1718}, doi = {10.1038/s41588-018-0216-7}, author = {Westra, Harm-Jan and Mart{\'\i}nez-Bonet, Marta and Onengut-Gumuscu, Suna and Lee, Annette and Luo, Yang and Teslovich, Nikola and Worthington, Jane and Martin, Javier and Huizinga, Tom and Klareskog, Lars and Rantapaa-Dahlqvist, Solbritt and Chen, Wei-Min and Quinlan, Aaron and Todd, John A and Eyre, Steve and Nigrovic, Peter A and Gregersen, Peter K and Rich, Stephen S and Raychaudhuri, Soumya} } @article {43, title = {Functional equivalence of genome sequencing analysis pipelines enables harmonized variant calling across human genetics projects.}, journal = {Nat Commun}, volume = {9}, year = {2018}, month = {2018 Oct 02}, pages = {4038}, abstract = {

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

}, keywords = {Genome, Human, Human Genetics, Humans, Whole Genome Sequencing}, issn = {2041-1723}, doi = {10.1038/s41467-018-06159-4}, author = {Regier, Allison A and Farjoun, Yossi and Larson, David E and Krasheninina, Olga and Kang, Hyun Min and Howrigan, Daniel P and Chen, Bo-Juen and Kher, Manisha and Banks, Eric and Ames, Darren C and English, Adam C and Li, Heng and Xing, Jinchuan and Zhang, Yeting and Matise, Tara and Abecasis, Gon{\c c}alo R and Salerno, Will and Zody, Michael C and Neale, Benjamin M and Hall, Ira M} } @article {29, title = {Phenotype risk scores identify patients with unrecognized Mendelian disease patterns.}, journal = {Science}, volume = {359}, year = {2018}, month = {2018 Mar 16}, pages = {1233-1239}, abstract = {

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

}, keywords = {Databases, Genetic, DNA Mutational Analysis, Electronic Health Records, Exome, Genetic Association Studies, Genetic Diseases, Inborn, Genetic Predisposition to Disease, Genetic Variation, Humans, Phenotype, Risk Factors}, issn = {1095-9203}, doi = {10.1126/science.aal4043}, author = {Bastarache, Lisa and Hughey, Jacob J and Hebbring, Scott and Marlo, Joy and Zhao, Wanke and Ho, Wanting T and Van Driest, Sara L and McGregor, Tracy L and Mosley, Jonathan D and Wells, Quinn S and Temple, Michael and Ramirez, Andrea H and Carroll, Robert and Osterman, Travis and Edwards, Todd and Ruderfer, Douglas and Velez Edwards, Digna R and Hamid, Rizwan and Cogan, Joy and Glazer, Andrew and Wei, Wei-Qi and Feng, QiPing and Brilliant, Murray and Zhao, Zhizhuang J and Cox, Nancy J and Roden, Dan M and Denny, Joshua C} } @article {7, title = {The Effects of Migration and Assortative Mating on Admixture Linkage Disequilibrium.}, journal = {Genetics}, volume = {205}, year = {2017}, month = {2017 Jan}, pages = {375-383}, abstract = {

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

}, keywords = {Alleles, Black or African American, Datasets as Topic, Gene Frequency, Genetics, Population, Genomics, Human Migration, Humans, Linkage Disequilibrium, Models, Genetic, Models, Statistical, Polymorphism, Single Nucleotide}, issn = {1943-2631}, doi = {10.1534/genetics.116.192138}, author = {Zaitlen, Noah and Huntsman, Scott and Hu, Donglei and Spear, Melissa and Eng, Celeste and Oh, Sam S and White, Marquitta J and Mak, Angel and Davis, Adam and Meade, Kelly and Brigino-Buenaventura, Emerita and LeNoir, Michael A and Bibbins-Domingo, Kirsten and Burchard, Esteban G and Halperin, Eran} } @article {18, title = {Genetic effects on gene expression across human tissues.}, journal = {Nature}, volume = {550}, year = {2017}, month = {2017 Oct 11}, pages = {204-213}, abstract = {

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

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

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

}, keywords = {Adolescent, Adult, Aged, Child, Collagen Diseases, Female, Fibrillar Collagens, Genotype, Heterozygote, Hispanic or Latino, Homozygote, Humans, Male, Middle Aged, Molecular Epidemiology, Multigene Family, Musculoskeletal Diseases, New York City, Pedigree, Whole Genome Sequencing, Young Adult}, issn = {2050-084X}, doi = {10.7554/eLife.25060}, author = {Belbin, Gillian Morven and Odgis, Jacqueline and Sorokin, Elena P and Yee, Muh-Ching and Kohli, Sumita and Glicksberg, Benjamin S and Gignoux, Christopher R and Wojcik, Genevieve L and Van Vleck, Tielman and Jeff, Janina M and Linderman, Michael and Schurmann, Claudia and Ruderfer, Douglas and Cai, Xiaoqiang and Merkelson, Amanda and Justice, Anne E and Young, Kristin L and Graff, Misa and North, Kari E and Peters, Ulrike and James, Regina and Hindorff, Lucia and Kornreich, Ruth and Edelmann, Lisa and Gottesman, Omri and Stahl, Eli Ea and Cho, Judy H and Loos, Ruth Jf and Bottinger, Erwin P and Nadkarni, Girish N and Abul-Husn, Noura S and Kenny, Eimear E} } @article {30, title = {The impact of rare variation on gene expression across tissues.}, journal = {Nature}, volume = {550}, year = {2017}, month = {2017 Oct 11}, pages = {239-243}, abstract = {

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

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

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

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

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

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

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

}, keywords = {Animals, Disease, Gene-Environment Interaction, Genome-Wide Association Study, Genomics, Humans, Models, Animal, Sequence Analysis, RNA}, issn = {1476-6256}, doi = {10.1093/aje/kwx229}, author = {Ritchie, Marylyn D and Davis, Joe R and Aschard, Hugues and Battle, Alexis and Conti, David and Du, Mengmeng and Eskin, Eleazar and Fallin, M Daniele and Hsu, Li and Kraft, Peter and Moore, Jason H and Pierce, Brandon L and Bien, Stephanie A and Thomas, Duncan C and Wei, Peng and Montgomery, Stephen B} } @article {25, title = {VEXOR: an integrative environment for prioritization of functional variants in fine-mapping analysis.}, journal = {Bioinformatics}, volume = {33}, year = {2017}, month = {2017 May 01}, pages = {1389-1391}, abstract = {

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

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

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

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

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

}, keywords = {Genome, Human, Genome-Wide Association Study, Genomics, Humans, Polymorphism, Genetic, Sequence Analysis, DNA, Software}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btw826}, author = {Lema{\c c}on, Audrey and Joly Beauparlant, Charles and Soucy, Penny and Allen, Jamie and Easton, Douglas and Kraft, Peter and Simard, Jacques and Droit, Arnaud} }