@article {142, title = {Identification of rare and common regulatory variants in pluripotent cells using population-scale transcriptomics.}, journal = {Nat Genet}, volume = {53}, year = {2021}, month = {2021 03}, pages = {313-321}, abstract = {

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

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

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

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

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

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

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

}, issn = {1098-2272}, doi = {10.1002/gepi.22365}, author = {Goddard, Pag{\'e} C and Keys, Kevin L and Mak, Angel C Y and Lee, Eunice Y and Liu, Amy K and Samedy-Bates, Lesly-Anne and Risse-Adams, Oona and Contreras, Mar{\'\i}a G and Elhawary, Jennifer R and Hu, Donglei and Huntsman, Scott and Oh, Sam S and Salazar, Sandra and Eng, Celeste and Himes, Blanca E and White, Marquitta J and Burchard, Esteban G} } @article {81, title = {Identification of cancer driver genes based on nucleotide context.}, journal = {Nat Genet}, volume = {52}, year = {2020}, month = {2020 02}, pages = {208-218}, abstract = {

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

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

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

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

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

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

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

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

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

}, keywords = {Asian Continental Ancestry Group, Base Sequence, Computational Biology, Enhancer Elements, Genetic, European Continental Ancestry Group, Gene Expression Regulation, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Models, Genetic, Molecular Sequence Annotation, Multifactorial Inheritance, Polymorphism, Single Nucleotide}, issn = {1546-1718}, doi = {10.1038/s41588-020-00740-8}, author = {Amariuta, Tiffany and Ishigaki, Kazuyoshi and Sugishita, Hiroki and Ohta, Tazro and Koido, Masaru and Dey, Kushal K and Matsuda, Koichi and Murakami, Yoshinori and Price, Alkes L and Kawakami, Eiryo and Terao, Chikashi and Raychaudhuri, Soumya} } @article {63, title = {Identification of rare-disease genes using blood transcriptome sequencing and large control cohorts.}, journal = {Nat Med}, volume = {25}, year = {2019}, month = {2019 06}, pages = {911-919}, abstract = {

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

}, keywords = {Acid Ceramidase, Case-Control Studies, Child, Child, Preschool, Cohort Studies, Female, Genetic Variation, Humans, Male, Models, Genetic, Mutation, Oxidoreductases Acting on CH-CH Group Donors, Potassium Channels, Rare Diseases, RNA, RNA Splicing, Sequence Analysis, RNA, Whole Exome Sequencing}, issn = {1546-170X}, doi = {10.1038/s41591-019-0457-8}, author = {Fr{\'e}sard, Laure and Smail, Craig and Ferraro, Nicole M and Teran, Nicole A and Li, Xin and Smith, Kevin S and Bonner, Devon and Kernohan, Kristin D and Marwaha, Shruti and Zappala, Zachary and Balliu, Brunilda and Davis, Joe R and Liu, Boxiang and Prybol, Cameron J and Kohler, Jennefer N and Zastrow, Diane B and Reuter, Chloe M and Fisk, Dianna G and Grove, Megan E and Davidson, Jean M and Hartley, Taila and Joshi, Ruchi and Strober, Benjamin J and Utiramerur, Sowmithri and Lind, Lars and Ingelsson, Erik and Battle, Alexis and Bejerano, Gill and Bernstein, Jonathan A and Ashley, Euan A and Boycott, Kym M and Merker, Jason D and Wheeler, Matthew T and Montgomery, Stephen B} } @article {39, title = {Imputation-Aware Tag SNP Selection To Improve Power for Large-Scale, Multi-ethnic Association Studies.}, journal = {G3 (Bethesda)}, volume = {8}, year = {2018}, month = {2018 10 03}, pages = {3255-3267}, abstract = {

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

}, keywords = {Computational Biology, Databases, Nucleic Acid, Ethnic Groups, Genetic Association Studies, Genetics, Population, Genome-Wide Association Study, Humans, Linkage Disequilibrium, Models, Genetic, Polymorphism, Single Nucleotide, Reproducibility of Results, Selection, Genetic}, issn = {2160-1836}, doi = {10.1534/g3.118.200502}, author = {Wojcik, Genevieve L and Fuchsberger, Christian and Taliun, Daniel and Welch, Ryan and Martin, Alicia R and Shringarpure, Suyash and Carlson, Christopher S and Abecasis, Goncalo and Kang, Hyun Min and Boehnke, Michael and Bustamante, Carlos D and Gignoux, Christopher R and Kenny, Eimear E} } @article {30, title = {The impact of rare variation on gene expression across tissues.}, journal = {Nature}, volume = {550}, year = {2017}, month = {2017 10 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} }