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Boye C, Nirmalan S, Ranjbaran A, Luca F. Genotype × environment interactions in gene regulation and complex traits. Nat Genet 2024; 56:1057-1068. [PMID: 38858456 DOI: 10.1038/s41588-024-01776-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 04/25/2024] [Indexed: 06/12/2024]
Abstract
Genotype × environment interactions (GxE) have long been recognized as a key mechanism underlying human phenotypic variation. Technological developments over the past 15 years have dramatically expanded our appreciation of the role of GxE in both gene regulation and complex traits. The richness and complexity of these datasets also required parallel efforts to develop robust and sensitive statistical and computational approaches. Although our understanding of the genetic architecture of molecular and complex traits has been maturing, a large proportion of complex trait heritability remains unexplained. Furthermore, there are increasing efforts to characterize the effect of environmental exposure on human health. We therefore review GxE in human gene regulation and complex traits, advocating for a comprehensive approach that jointly considers genetic and environmental factors in human health and disease.
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Affiliation(s)
- Carly Boye
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI, US
| | - Shreya Nirmalan
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI, US
| | - Ali Ranjbaran
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI, US
| | - Francesca Luca
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI, US.
- Department of Obstetrics and Gynecology, Wayne State University, Detroit, MI, US.
- Department of Biology, University of Rome "Tor Vergata", Rome, Italy.
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2
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Luo L, Mehrotra DV, Shen J, Tang ZZ. Multi-trait analysis of gene-by-environment interactions in large-scale genetic studies. Biostatistics 2024; 25:504-520. [PMID: 36897773 DOI: 10.1093/biostatistics/kxad004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 02/15/2023] [Accepted: 02/22/2023] [Indexed: 03/11/2023] Open
Abstract
Identifying genotype-by-environment interaction (GEI) is challenging because the GEI analysis generally has low power. Large-scale consortium-based studies are ultimately needed to achieve adequate power for identifying GEI. We introduce Multi-Trait Analysis of Gene-Environment Interactions (MTAGEI), a powerful, robust, and computationally efficient framework to test gene-environment interactions on multiple traits in large data sets, such as the UK Biobank (UKB). To facilitate the meta-analysis of GEI studies in a consortium, MTAGEI efficiently generates summary statistics of genetic associations for multiple traits under different environmental conditions and integrates the summary statistics for GEI analysis. MTAGEI enhances the power of GEI analysis by aggregating GEI signals across multiple traits and variants that would otherwise be difficult to detect individually. MTAGEI achieves robustness by combining complementary tests under a wide spectrum of genetic architectures. We demonstrate the advantages of MTAGEI over existing single-trait-based GEI tests through extensive simulation studies and the analysis of the whole exome sequencing data from the UKB.
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Affiliation(s)
- Lan Luo
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., Rahway, NJ 07065, USA
| | - Devan V Mehrotra
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., North Wales, PA 19454, USA
| | - Judong Shen
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., Rahway, NJ 07065, USA
| | - Zheng-Zheng Tang
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, 330 N Orchard St, Madison, WI 53715, USA
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3
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Alamin M, Sultana MH, Lou X, Jin W, Xu H. Dissecting Complex Traits Using Omics Data: A Review on the Linear Mixed Models and Their Application in GWAS. PLANTS (BASEL, SWITZERLAND) 2022; 11:3277. [PMID: 36501317 PMCID: PMC9739826 DOI: 10.3390/plants11233277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 11/23/2022] [Accepted: 11/25/2022] [Indexed: 06/17/2023]
Abstract
Genome-wide association study (GWAS) is the most popular approach to dissecting complex traits in plants, humans, and animals. Numerous methods and tools have been proposed to discover the causal variants for GWAS data analysis. Among them, linear mixed models (LMMs) are widely used statistical methods for regulating confounding factors, including population structure, resulting in increased computational proficiency and statistical power in GWAS studies. Recently more attention has been paid to pleiotropy, multi-trait, gene-gene interaction, gene-environment interaction, and multi-locus methods with the growing availability of large-scale GWAS data and relevant phenotype samples. In this review, we have demonstrated all possible LMMs-based methods available in the literature for GWAS. We briefly discuss the different LMM methods, software packages, and available open-source applications in GWAS. Then, we include the advantages and weaknesses of the LMMs in GWAS. Finally, we discuss the future perspective and conclusion. The present review paper would be helpful to the researchers for selecting appropriate LMM models and methods quickly for GWAS data analysis and would benefit the scientific society.
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Affiliation(s)
- Md. Alamin
- Institute of Bioinformatics, Zhejiang University, Hangzhou 310058, China
- Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
| | | | - Xiangyang Lou
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Wenfei Jin
- Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
| | - Haiming Xu
- Institute of Bioinformatics, Zhejiang University, Hangzhou 310058, China
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4
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Li M, Zhang YW, Zhang ZC, Xiang Y, Liu MH, Zhou YH, Zuo JF, Zhang HQ, Chen Y, Zhang YM. A compressed variance component mixed model for detecting QTNs and QTN-by-environment and QTN-by-QTN interactions in genome-wide association studies. MOLECULAR PLANT 2022; 15:630-650. [PMID: 35202864 DOI: 10.1016/j.molp.2022.02.012] [Citation(s) in RCA: 47] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2021] [Revised: 01/26/2022] [Accepted: 02/19/2022] [Indexed: 05/25/2023]
Abstract
Although genome-wide association studies are widely used to mine genes for quantitative traits, the effects to be estimated are confounded, and the methodologies for detecting interactions are imperfect. To address these issues, the mixed model proposed here first estimates the genotypic effects for AA, Aa, and aa, and the genotypic polygenic background replaces additive and dominance polygenic backgrounds. Then, the estimated genotypic effects are partitioned into additive and dominance effects using a one-way analysis of variance model. This strategy was further expanded to cover QTN-by-environment interactions (QEIs) and QTN-by-QTN interactions (QQIs) using the same mixed-model framework. Thus, a three-variance-component mixed model was integrated with our multi-locus random-SNP-effect mixed linear model (mrMLM) method to establish a new methodological framework, 3VmrMLM, that detects all types of loci and estimates their effects. In Monte Carlo studies, 3VmrMLM correctly detected all types of loci and almost unbiasedly estimated their effects, with high powers and accuracies and a low false positive rate. In re-analyses of 10 traits in 1439 rice hybrids, detection of 269 known genes, 45 known gene-by-environment interactions, and 20 known gene-by-gene interactions strongly validated 3VmrMLM. Further analyses of known genes showed more small (67.49%), minor-allele-frequency (35.52%), and pleiotropic (30.54%) genes, with higher repeatability across datasets (54.36%) and more dominance loci. In addition, a heteroscedasticity mixed model in multiple environments and dimension reduction methods in quite a number of environments were developed to detect QEIs, and variable selection under a polygenic background was proposed for QQI detection. This study provides a new approach for revealing the genetic architecture of quantitative traits.
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Affiliation(s)
- Mei Li
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Ya-Wen Zhang
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China; State Key Laboratory of Cotton Biology, Anyang 455000, China
| | - Ze-Chang Zhang
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Yu Xiang
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Ming-Hui Liu
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Ya-Hui Zhou
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Jian-Fang Zuo
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Han-Qing Zhang
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Ying Chen
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Yuan-Ming Zhang
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China.
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5
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Arnol D, Schapiro D, Bodenmiller B, Saez-Rodriguez J, Stegle O. Modeling Cell-Cell Interactions from Spatial Molecular Data with Spatial Variance Component Analysis. Cell Rep 2020; 29:202-211.e6. [PMID: 31577949 PMCID: PMC6899515 DOI: 10.1016/j.celrep.2019.08.077] [Citation(s) in RCA: 95] [Impact Index Per Article: 23.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2018] [Revised: 04/11/2019] [Accepted: 08/22/2019] [Indexed: 12/22/2022] Open
Abstract
Technological advances enable assaying multiplexed spatially resolved RNA and protein expression profiling of individual cells, thereby capturing molecular variations in physiological contexts. While these methods are increasingly accessible, computational approaches for studying the interplay of the spatial structure of tissues and cell-cell heterogeneity are only beginning to emerge. Here, we present spatial variance component analysis (SVCA), a computational framework for the analysis of spatial molecular data. SVCA enables quantifying different dimensions of spatial variation and in particular quantifies the effect of cell-cell interactions on gene expression. In a breast cancer Imaging Mass Cytometry dataset, our model yields interpretable spatial variance signatures, which reveal cell-cell interactions as a major driver of protein expression heterogeneity. Applied to high-dimensional imaging-derived RNA data, SVCA identifies plausible gene families that are linked to cell-cell interactions. SVCA is available as a free software tool that can be widely applied to spatial data from different technologies.
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Affiliation(s)
- Damien Arnol
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Denis Schapiro
- Institute of Molecular Life Sciences, University of Zurich, Zurich, Switzerland; Life Science Zurich Graduate School, ETH Zurich and University of Zurich, Zurich, Switzerland
| | - Bernd Bodenmiller
- Institute of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
| | - Julio Saez-Rodriguez
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK; Joint Research Center for Computational Biomedicine, RWTH Aachen University, Faculty of Medicine, Pauwelsstrasse 19, 52074 Aachen, Germany; Institute for Computational Biomedicine, Heidelberg University, Faculty of Medicine, Bioquant, 69120 Heidelberg.
| | - Oliver Stegle
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK; European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany; Division of Computational Genomics and Systems Genetics, German Cancer Research Center, 69120 Heidelberg, Germany.
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6
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He Y, Chhetri SB, Arvanitis M, Srinivasan K, Aguet F, Ardlie KG, Barbeira AN, Bonazzola R, Im HK, Brown CD, Battle A. sn-spMF: matrix factorization informs tissue-specific genetic regulation of gene expression. Genome Biol 2020; 21:235. [PMID: 32912314 PMCID: PMC7488540 DOI: 10.1186/s13059-020-02129-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Accepted: 08/04/2020] [Indexed: 01/09/2023] Open
Abstract
Genetic regulation of gene expression, revealed by expression quantitative trait loci (eQTLs), exhibits complex patterns of tissue-specific effects. Characterization of these patterns may allow us to better understand mechanisms of gene regulation and disease etiology. We develop a constrained matrix factorization model, sn-spMF, to learn patterns of tissue-sharing and apply it to 49 human tissues from the Genotype-Tissue Expression (GTEx) project. The learned factors reflect tissues with known biological similarity and identify transcription factors that may mediate tissue-specific effects. sn-spMF, available at https://github.com/heyuan7676/ts_eQTLs , can be applied to learn biologically interpretable patterns of eQTL tissue-specificity and generate testable mechanistic hypotheses.
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Affiliation(s)
- Yuan He
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, 21218, MD, USA
| | - Surya B Chhetri
- HudsonAlpha Institute for Biotechnology, Huntsville, 35806, AL, USA
- Current Address: Department of Biomedical Engineering, Johns Hopkins University, Baltimore, 21218, MD, USA
| | - Marios Arvanitis
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, 21218, MD, USA
- Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, 21287, MD, USA
| | - Kaushik Srinivasan
- Department of Computer Science, Johns Hopkins University, Baltimore, 21218, MD, USA
| | - François Aguet
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Alvaro N Barbeira
- Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, IL, USA
| | - Rodrigo Bonazzola
- Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, IL, USA
| | - Hae Kyung Im
- Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, IL, USA
| | - Christopher D Brown
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, 19104, PA, USA.
| | - Alexis Battle
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, 21218, MD, USA.
- Department of Computer Science, Johns Hopkins University, Baltimore, 21218, MD, USA.
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7
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Gonçalves E, Segura‐Cabrera A, Pacini C, Picco G, Behan FM, Jaaks P, Coker EA, van der Meer D, Barthorpe A, Lightfoot H, Mironenko T, Beck A, Richardson L, Yang W, Lleshi E, Hall J, Tolley C, Hall C, Mali I, Thomas F, Morris J, Leach AR, Lynch JT, Sidders B, Crafter C, Iorio F, Fawell S, Garnett MJ. Drug mechanism-of-action discovery through the integration of pharmacological and CRISPR screens. Mol Syst Biol 2020; 16:e9405. [PMID: 32627965 PMCID: PMC7336273 DOI: 10.15252/msb.20199405] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Revised: 05/14/2020] [Accepted: 05/21/2020] [Indexed: 12/26/2022] Open
Abstract
Low success rates during drug development are due, in part, to the difficulty of defining drug mechanism-of-action and molecular markers of therapeutic activity. Here, we integrated 199,219 drug sensitivity measurements for 397 unique anti-cancer drugs with genome-wide CRISPR loss-of-function screens in 484 cell lines to systematically investigate cellular drug mechanism-of-action. We observed an enrichment for positive associations between the profile of drug sensitivity and knockout of a drug's nominal target, and by leveraging protein-protein networks, we identified pathways underpinning drug sensitivity. This revealed an unappreciated positive association between mitochondrial E3 ubiquitin-protein ligase MARCH5 dependency and sensitivity to MCL1 inhibitors in breast cancer cell lines. We also estimated drug on-target and off-target activity, informing on specificity, potency and toxicity. Linking drug and gene dependency together with genomic data sets uncovered contexts in which molecular networks when perturbed mediate cancer cell loss-of-fitness and thereby provide independent and orthogonal evidence of biomarkers for drug development. This study illustrates how integrating cell line drug sensitivity with CRISPR loss-of-function screens can elucidate mechanism-of-action to advance drug development.
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Affiliation(s)
| | - Aldo Segura‐Cabrera
- European Molecular Biology LaboratoryEuropean Bioinformatics InstituteHinxtonUK
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Andrew R Leach
- European Molecular Biology LaboratoryEuropean Bioinformatics InstituteHinxtonUK
| | - James T Lynch
- Research and Early DevelopmentOncology R&DAstraZenecaCambridgeUK
| | - Ben Sidders
- Research and Early DevelopmentOncology R&DAstraZenecaCambridgeUK
| | - Claire Crafter
- Research and Early DevelopmentOncology R&DAstraZenecaCambridgeUK
| | - Francesco Iorio
- Wellcome Sanger InstituteHinxtonUK
- Human TechnopoleMilanoItaly
| | - Stephen Fawell
- Research and Early DevelopmentOncology R&DAstraZenecaWalthamMAUSA
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8
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Dahl A, Nguyen K, Cai N, Gandal MJ, Flint J, Zaitlen N. A Robust Method Uncovers Significant Context-Specific Heritability in Diverse Complex Traits. Am J Hum Genet 2020; 106:71-91. [PMID: 31901249 PMCID: PMC7042488 DOI: 10.1016/j.ajhg.2019.11.015] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2019] [Accepted: 11/26/2019] [Indexed: 02/08/2023] Open
Abstract
Gene-environment interactions (GxE) can be fundamental in applications ranging from functional genomics to precision medicine and is a conjectured source of substantial heritability. However, unbiased methods to profile GxE genome-wide are nascent and, as we show, cannot accommodate general environment variables, modest sample sizes, heterogeneous noise, and binary traits. To address this gap, we propose a simple, unifying mixed model for gene-environment interaction (GxEMM). In simulations and theory, we show that GxEMM can dramatically improve estimates and eliminate false positives when the assumptions of existing methods fail. We apply GxEMM to a range of human and model organism datasets and find broad evidence of context-specific genetic effects, including GxSex, GxAdversity, and GxDisease interactions across thousands of clinical and molecular phenotypes. Overall, GxEMM is broadly applicable for testing and quantifying polygenic interactions, which can be useful for explaining heritability and invaluable for determining biologically relevant environments.
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Affiliation(s)
- Andy Dahl
- Department of Neurology, University of California Los Angeles, Los Angeles, CA 90095, USA; Department of Medicine, University of California San Francisco, San Francisco, CA 94158, USA.
| | - Khiem Nguyen
- Department of Medicine, University of California San Francisco, San Francisco, CA 94158, USA
| | - Na Cai
- Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK; European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Michael J Gandal
- Department of Psychiatry, Semel Institute, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Jonathan Flint
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Noah Zaitlen
- Department of Neurology, University of California Los Angeles, Los Angeles, CA 90095, USA; Department of Medicine, University of California San Francisco, San Francisco, CA 94158, USA.
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Kim J, Ziyatdinov A, Laville V, Hu FB, Rimm E, Kraft P, Aschard H. Joint Analysis of Multiple Interaction Parameters in Genetic Association Studies. Genetics 2019; 211:483-494. [PMID: 30578273 PMCID: PMC6366922 DOI: 10.1534/genetics.118.301394] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2018] [Accepted: 12/10/2018] [Indexed: 01/24/2023] Open
Abstract
With growing human genetic and epidemiologic data, there has been increased interest for the study of gene-by-environment (G-E) interaction effects. Still, major questions remain on how to test jointly a large number of interactions between multiple SNPs and multiple exposures. In this study, we first compared the relative performance of four fixed-effect joint analysis approaches using simulated data, considering up to 10 exposures and 300 SNPs: (1) omnibus test, (2) multi-exposure and genetic risk score (GRS) test, (3) multi-SNP and environmental risk score (ERS) test, and (4) GRS-ERS test. Our simulations explored both linear and logistic regression while considering three statistics: the Wald test, the Score test, and the likelihood ratio test (LRT). We further applied the approaches to three large sets of human cohort data (n = 37,664), focusing on type 2 diabetes (T2D), obesity, hypertension, and coronary heart disease with smoking, physical activity, diets, and total energy intake. Overall, GRS-based approaches were the most robust, and had the highest power, especially when the G-E interaction effects were correlated with the marginal genetic and environmental effects. We also observed severe miscalibration of joint statistics in logistic models when the number of events per variable was too low when using either the Wald test or LRT test. Finally, our real data application detected nominally significant interaction effects for three outcomes (T2D, obesity, and hypertension), mainly from the GRS-ERS approach. In conclusion, this study provides guidelines for testing multiple interaction parameters in modern human cohorts including extensive genetic and environmental data.
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Affiliation(s)
- Jihye Kim
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115
| | - Andrey Ziyatdinov
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115
| | - Vincent Laville
- Centre de Bioinformatique, Biostatistique et Biologie Intégrative (C3BI), Institut Pasteur, 75724 Paris, France
| | - Frank B Hu
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115
| | - Eric Rimm
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115
| | - Peter Kraft
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115
| | - Hugues Aschard
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115
- Centre de Bioinformatique, Biostatistique et Biologie Intégrative (C3BI), Institut Pasteur, 75724 Paris, France
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10
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A linear mixed-model approach to study multivariate gene-environment interactions. Nat Genet 2018; 51:180-186. [PMID: 30478441 DOI: 10.1038/s41588-018-0271-0] [Citation(s) in RCA: 85] [Impact Index Per Article: 14.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2018] [Accepted: 10/04/2018] [Indexed: 12/27/2022]
Abstract
Different exposures, including diet, physical activity, or external conditions can contribute to genotype-environment interactions (G×E). Although high-dimensional environmental data are increasingly available and multiple exposures have been implicated with G×E at the same loci, multi-environment tests for G×E are not established. Here, we propose the structured linear mixed model (StructLMM), a computationally efficient method to identify and characterize loci that interact with one or more environments. After validating our model using simulations, we applied StructLMM to body mass index in the UK Biobank, where our model yields previously known and novel G×E signals. Finally, in an application to a large blood eQTL dataset, we demonstrate that StructLMM can be used to study interactions with hundreds of environmental variables.
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11
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Mason VC, Schaefer RJ, McCue ME, Leeb T, Gerber V. eQTL discovery and their association with severe equine asthma in European Warmblood horses. BMC Genomics 2018; 19:581. [PMID: 30071827 PMCID: PMC6090848 DOI: 10.1186/s12864-018-4938-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2018] [Accepted: 07/11/2018] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND Severe equine asthma, also known as recurrent airway obstruction (RAO), is a debilitating, performance limiting, obstructive respiratory condition in horses that is phenotypically similar to human asthma. Past genome wide association studies (GWAS) have not discovered coding variants associated with RAO, leading to the hypothesis that causative variant(s) underlying the signals are likely non-coding, regulatory variant(s). Regions of the genome containing variants that influence the number of expressed RNA molecules are expression quantitative trait loci (eQTLs). Variation associated with RAO that also regulates a gene's expression in a disease relevant tissue could help identify candidate genes that influence RAO if that gene's expression is also associated with RAO disease status. RESULTS We searched for eQTLs by analyzing peripheral blood mononuclear cells (PBMCs) from two half-sib families and one unrelated cohort of 82 European Warmblood horses that were previously treated in vitro with: no stimulation (MCK), lipopolysaccharides (LPS), recombinant cyathostomin antigen (RCA), and hay-dust extract (HDE). We identified high confidence eQTLs that did not violate linear modeling assumptions and were not significant due to single outlier individuals. We identified a mean of 4347 high confidence eQTLs in four treatments of PBMCs, and discovered two trans regulatory hotspots regulating genes involved in related biological pathways. We corroborated previous RAO associated single nucleotide polymorphisms (SNPs), and increased the resolution of past GWAS by analyzing 1,056,195 SNPs in 361 individuals. We identified four RAO-associated SNPs that only regulate gene expression of dexamethasone-induced protein (DEXI), however we found no significant association between DEXI gene expression and presence of RAO. CONCLUSIONS Thousands of genetic variants regulate gene expression in PBMCs of European Warmblood horses in cis and trans. Most high confidence eSNPs are significantly enriched near the transcription start sites of their target genes. Two trans regulatory hotspots on chromosome 11 and 13 regulate many genes involved in transmembrane cell signaling and neurological development respectively when PBMCs are treated with HDE. None of the top fifteen RAO associated SNPs strongly influence disease status through gene expression regulation.
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Affiliation(s)
- Victor C. Mason
- Department of Clinical Veterinary Medicine, Swiss Institute of Equine Medicine, Vetsuisse Faculty, University of Bern, and Agroscope, Länggassstrasse 124, 3012 Bern, Switzerland
| | - Robert J. Schaefer
- Department of Veterinary Population Medicine, University of Minnesota, 1365 Gortner Ave, Saint Paul, MN 55108 USA
| | - Molly E. McCue
- Department of Veterinary Population Medicine, University of Minnesota, 1365 Gortner Ave, Saint Paul, MN 55108 USA
| | - Tosso Leeb
- Department of Clinical Research and Veterinary Public Health, Institute of Genetics, Vetsuisse Faculty, University of Bern, Bremgartenstrasse 109A, 3012 Bern, Switzerland
| | - Vinzenz Gerber
- Department of Clinical Veterinary Medicine, Swiss Institute of Equine Medicine, Vetsuisse Faculty, University of Bern, and Agroscope, Länggassstrasse 124, 3012 Bern, Switzerland
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12
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Josephs EB. Determining the evolutionary forces shaping G × E. THE NEW PHYTOLOGIST 2018; 219:31-36. [PMID: 29574919 DOI: 10.1111/nph.15103] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2017] [Accepted: 02/09/2018] [Indexed: 06/08/2023]
Abstract
Contents Summary 31 I. Introduction 31 II. The maintenance of genetic variation for plasticity 32 III. Why is there environmental variation for genetic effects? 33 IV. Conclusions 35 Acknowledgements 35 References 35 SUMMARY: Phenotypic plasticity is common in nature, yet we lack a comprehensive understanding of the evolutionary forces that shape genetic variation for plasticity. This endeavor is especially important because variation for plasticity will result in genotype-by-environment interactions (G × E), a crucial component of variation in quantitative traits. Here, I review our understanding of the evolutionary forces shaping G × E, focusing specifically on: what evolutionary forces maintain variation for plasticity; and what forces maintain different genetic architectures across environments. My specific goal is to show that genomic data can be leveraged to explain the maintenance of G × E by contrasting patterns of genetic variation for plasticity with neutral expectations.
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Affiliation(s)
- Emily B Josephs
- Department of Evolution and Ecology, University of California, Davis, CA, 95616, USA
- Center for Population Biology, University of California, Davis, CA, 95616, USA
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