1
|
Pazokitoroudi A, Liu Z, Dahl A, Zaitlen N, Rosset S, Sankararaman S. A scalable and robust variance components method reveals insights into the architecture of gene-environment interactions underlying complex traits. Am J Hum Genet 2024; 111:1462-1480. [PMID: 38866020 PMCID: PMC11267529 DOI: 10.1016/j.ajhg.2024.05.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 05/15/2024] [Accepted: 05/15/2024] [Indexed: 06/14/2024] Open
Abstract
Understanding the contribution of gene-environment interactions (GxE) to complex trait variation can provide insights into disease mechanisms, explain sources of heritability, and improve genetic risk prediction. While large biobanks with genetic and deep phenotypic data hold promise for obtaining novel insights into GxE, our understanding of GxE architecture in complex traits remains limited. We introduce a method to estimate the proportion of trait variance explained by GxE (GxE heritability) and additive genetic effects (additive heritability) across the genome and within specific genomic annotations. We show that our method is accurate in simulations and computationally efficient for biobank-scale datasets. We applied our method to common array SNPs (MAF ≥1%), fifty quantitative traits, and four environmental variables (smoking, sex, age, and statin usage) in unrelated white British individuals in the UK Biobank. We found 68 trait-E pairs with significant genome-wide GxE heritability (p<0.05/200) with a ratio of GxE to additive heritability of ≈6.8% on average. Analyzing ≈8 million imputed SNPs (MAF ≥0.1%), we documented an approximate 28% increase in genome-wide GxE heritability compared to array SNPs. We partitioned GxE heritability across minor allele frequency (MAF) and local linkage disequilibrium (LD) values, revealing that, like additive allelic effects, GxE allelic effects tend to increase with decreasing MAF and LD. Analyzing GxE heritability near genes highly expressed in specific tissues, we find significant brain-specific enrichment for body mass index (BMI) and basal metabolic rate in the context of smoking and adipose-specific enrichment for waist-hip ratio (WHR) in the context of sex.
Collapse
Affiliation(s)
- Ali Pazokitoroudi
- Department of Computer Science, UCLA, Los Angeles, CA, USA; Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Zhengtong Liu
- Department of Computer Science, UCLA, Los Angeles, CA, USA
| | - Andrew Dahl
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA
| | - Noah Zaitlen
- Department of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA; Department of Computational Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA; Department of Neurology, UCLA, Los Angeles, CA, USA
| | - Saharon Rosset
- Department of Statistics, Tel-Aviv University, Tel-Aviv, Israel
| | - Sriram Sankararaman
- Department of Computer Science, UCLA, Los Angeles, CA, USA; Department of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA; Department of Computational Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA.
| |
Collapse
|
2
|
Motsinger-Reif AA, Reif DM, Akhtari FS, House JS, Campbell CR, Messier KP, Fargo DC, Bowen TA, Nadadur SS, Schmitt CP, Pettibone KG, Balshaw DM, Lawler CP, Newton SA, Collman GW, Miller AK, Merrick BA, Cui Y, Anchang B, Harmon QE, McAllister KA, Woychik R. Gene-environment interactions within a precision environmental health framework. CELL GENOMICS 2024; 4:100591. [PMID: 38925123 PMCID: PMC11293590 DOI: 10.1016/j.xgen.2024.100591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 03/26/2024] [Accepted: 06/02/2024] [Indexed: 06/28/2024]
Abstract
Understanding the complex interplay of genetic and environmental factors in disease etiology and the role of gene-environment interactions (GEIs) across human development stages is important. We review the state of GEI research, including challenges in measuring environmental factors and advantages of GEI analysis in understanding disease mechanisms. We discuss the evolution of GEI studies from candidate gene-environment studies to genome-wide interaction studies (GWISs) and the role of multi-omics in mediating GEI effects. We review advancements in GEI analysis methods and the importance of large-scale datasets. We also address the translation of GEI findings into precision environmental health (PEH), showcasing real-world applications in healthcare and disease prevention. Additionally, we highlight societal considerations in GEI research, including environmental justice, the return of results to participants, and data privacy. Overall, we underscore the significance of GEI for disease prediction and prevention and advocate for integrating the exposome into PEH omics studies.
Collapse
Affiliation(s)
- Alison A Motsinger-Reif
- Biostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA.
| | - David M Reif
- Predictive Toxicology Branch, Division of Translational Toxicology, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Farida S Akhtari
- Biostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - John S House
- Biostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - C Ryan Campbell
- Biostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Kyle P Messier
- Biostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA; Predictive Toxicology Branch, Division of Translational Toxicology, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - David C Fargo
- Office of the Director, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Tiffany A Bowen
- Office of the Director, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Srikanth S Nadadur
- Exposure, Response, and Technology Branch, Division of Extramural Research and Training, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Charles P Schmitt
- Office of the Scientific Director, Office of Data Science, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Kristianna G Pettibone
- Program Analysis Branch, Division of Extramural Research and Training, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - David M Balshaw
- Office of the Director, National Institute of Environmental Health Sciences, Durham, NC, USA; Division of Extramural Research and Training, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Cindy P Lawler
- Genes, Environment, and Health Branch, Division of Extramural Research and Training, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Shelia A Newton
- Office of Scientific Coordination, Planning and Evaluation, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Gwen W Collman
- Office of the Director, National Institute of Environmental Health Sciences, Durham, NC, USA; Office of Scientific Coordination, Planning and Evaluation, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Aubrey K Miller
- Office of Scientific Coordination, Planning and Evaluation, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - B Alex Merrick
- Mechanistic Toxicology Branch, Division of Translational Toxicology, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Yuxia Cui
- Exposure, Response, and Technology Branch, Division of Extramural Research and Training, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Benedict Anchang
- Biostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Quaker E Harmon
- Epidemiology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Kimberly A McAllister
- Genes, Environment, and Health Branch, Division of Extramural Research and Training, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Rick Woychik
- Office of the Director, National Institute of Environmental Health Sciences, Durham, NC, USA
| |
Collapse
|
3
|
Herrera-Luis E, Benke K, Volk H, Ladd-Acosta C, Wojcik GL. Gene-environment interactions in human health. Nat Rev Genet 2024:10.1038/s41576-024-00731-z. [PMID: 38806721 DOI: 10.1038/s41576-024-00731-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/03/2024] [Indexed: 05/30/2024]
Abstract
Gene-environment interactions (G × E), the interplay of genetic variation with environmental factors, have a pivotal impact on human complex traits and diseases. Statistically, G × E can be assessed by determining the deviation from expectation of predictive models based solely on the phenotypic effects of genetics or environmental exposures. Despite the unprecedented, widespread and diverse use of G × E analytical frameworks, heterogeneity in their application and reporting hinders their applicability in public health. In this Review, we discuss study design considerations as well as G × E analytical frameworks to assess polygenic liability dependent on the environment, to identify specific genetic variants exhibiting G × E, and to characterize environmental context for these dynamics. We conclude with recommendations to address the most common challenges and pitfalls in the conceptualization, methodology and reporting of G × E studies, as well as future directions.
Collapse
Affiliation(s)
- Esther Herrera-Luis
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Kelly Benke
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Heather Volk
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Christine Ladd-Acosta
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Genevieve L Wojcik
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
| |
Collapse
|
4
|
Dong Z, Jiang W, Li H, DeWan AT, Zhao H. LDER-GE estimates phenotypic variance component of gene-environment interactions in human complex traits accurately with GE interaction summary statistics and full LD information. Brief Bioinform 2024; 25:bbae335. [PMID: 38980374 PMCID: PMC11232466 DOI: 10.1093/bib/bbae335] [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: 04/08/2024] [Revised: 06/05/2024] [Accepted: 06/26/2024] [Indexed: 07/10/2024] Open
Abstract
Gene-environment (GE) interactions are essential in understanding human complex traits. Identifying these interactions is necessary for deciphering the biological basis of such traits. In this study, we review state-of-art methods for estimating the proportion of phenotypic variance explained by genome-wide GE interactions and introduce a novel statistical method Linkage-Disequilibrium Eigenvalue Regression for Gene-Environment interactions (LDER-GE). LDER-GE improves the accuracy of estimating the phenotypic variance component explained by genome-wide GE interactions using large-scale biobank association summary statistics. LDER-GE leverages the complete Linkage Disequilibrium (LD) matrix, as opposed to only the diagonal squared LD matrix utilized by LDSC (Linkage Disequilibrium Score)-based methods. Our extensive simulation studies demonstrate that LDER-GE performs better than LDSC-based approaches by enhancing statistical efficiency by ~23%. This improvement is equivalent to a sample size increase of around 51%. Additionally, LDER-GE effectively controls type-I error rate and produces unbiased results. We conducted an analysis using UK Biobank data, comprising 307 259 unrelated European-Ancestry subjects and 966 766 variants, across 217 environmental covariate-phenotype (E-Y) pairs. LDER-GE identified 34 significant E-Y pairs while LDSC-based method only identified 23 significant E-Y pairs with 22 overlapped with LDER-GE. Furthermore, we employed LDER-GE to estimate the aggregated variance component attributed to multiple GE interactions, leading to an increase in the explained phenotypic variance with GE interactions compared to considering main genetic effects only. Our results suggest the importance of impacts of GE interactions on human complex traits.
Collapse
Affiliation(s)
- Zihan Dong
- Department of Biostatistics, Yale School of Public Health, 60 College Street, New Haven, CT 06510, United States
- Center for Perinatal, Pediatric and Environmental Epidemiology, 60 College Street, Yale School of Public Health, New Haven, CT 06510, United States
| | - Wei Jiang
- Department of Biostatistics, Yale School of Public Health, 60 College Street, New Haven, CT 06510, United States
| | - Hongyu Li
- Department of Biostatistics, Yale School of Public Health, 60 College Street, New Haven, CT 06510, United States
| | - Andrew T DeWan
- Center for Perinatal, Pediatric and Environmental Epidemiology, 60 College Street, Yale School of Public Health, New Haven, CT 06510, United States
- Department of Chronic Disease Epidemiology, Yale School of Public Health, 60 College Street, New Haven, CT 06510, United States
| | - Hongyu Zhao
- Department of Biostatistics, Yale School of Public Health, 60 College Street, New Haven, CT 06510, United States
| |
Collapse
|
5
|
Durvasula A, Price AL. Distinct explanations underlie gene-environment interactions in the UK Biobank. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.09.22.23295969. [PMID: 37790574 PMCID: PMC10543037 DOI: 10.1101/2023.09.22.23295969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
The role of gene-environment (GxE) interaction in disease and complex trait architectures is widely hypothesized, but currently unknown. Here, we apply three statistical approaches to quantify and distinguish three different types of GxE interaction for a given trait and E variable. First, we detect locus-specific GxE interaction by testing for genetic correlation r g < 1 across E bins. Second, we detect genome-wide effects of the E variable on genetic variance by leveraging polygenic risk scores (PRS) to test for significant PRSxE in a regression of phenotypes on PRS, E, and PRSxE, together with differences in SNP-heritability across E bins. Third, we detect genome-wide proportional amplification of genetic and environmental effects as a function of the E variable by testing for significant PRSxE with no differences in SNP-heritability across E bins. Simulations show that these approaches achieve high sensitivity and specificity in distinguishing these three GxE scenarios. We applied our framework to 33 UK Biobank traits (25 quantitative traits and 8 diseases; average N = 325 K ) and 10 E variables spanning lifestyle, diet, and other environmental exposures. First, we identified 19 trait-E pairs with r g significantly < 1 (FDR<5%) (average r g = 0.95 ); for example, white blood cell count had r g = 0.95 (s.e. 0.01) between smokers and non-smokers. Second, we identified 28 trait-E pairs with significant PRSxE and significant SNP-heritability differences across E bins; for example, BMI had a significant PRSxE for physical activity (P=4.6e-5) with 5% larger SNP-heritability in the largest versus smallest quintiles of physical activity (P=7e-4). Third, we identified 15 trait-E pairs with significant PRSxE with no SNP-heritability differences across E bins; for example, waist-hip ratio adjusted for BMI had a significant PRSxE effect for time spent watching television (P=5e-3) with no SNP-heritability differences. Across the three scenarios, 8 of the trait-E pairs involved disease traits, whose interpretation is complicated by scale effects. Analyses using biological sex as the E variable produced additional significant findings in each of the three scenarios. Overall, we infer a significant contribution of GxE and GxSex effects to complex trait and disease variance.
Collapse
Affiliation(s)
- Arun Durvasula
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Genetics, Harvard Medical School, Cambridge, MA, USA
- Department of Human Evolutionary Biology, Harvard University, Cambridge, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Alkes L Price
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| |
Collapse
|
6
|
Timmins IR, Dudbridge F. Bayesian approach to assessing population differences in genetic risk of disease with application to prostate cancer. PLoS Genet 2024; 20:e1011212. [PMID: 38630784 PMCID: PMC11023298 DOI: 10.1371/journal.pgen.1011212] [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: 12/12/2022] [Accepted: 03/07/2024] [Indexed: 04/19/2024] Open
Abstract
Population differences in risk of disease are common, but the potential genetic basis for these differences is not well understood. A standard approach is to compare genetic risk across populations by testing for mean differences in polygenic scores, but existing studies that use this approach do not account for statistical noise in effect estimates (i.e., the GWAS betas) that arise due to the finite sample size of GWAS training data. Here, we show using Bayesian polygenic score methods that the level of uncertainty in estimates of genetic risk differences across populations is highly dependent on the GWAS training sample size, the polygenicity (number of causal variants), and genetic distance (FST) between the populations considered. We derive a Wald test for formally assessing the difference in genetic risk across populations, which we show to have calibrated type 1 error rates under a simplified assumption that all SNPs are independent, which we achieve in practise using linkage disequilibrium (LD) pruning. We further provide closed-form expressions for assessing the uncertainty in estimates of relative genetic risk across populations under the special case of an infinitesimal genetic architecture. We suggest that for many complex traits and diseases, particularly those with more polygenic architectures, current GWAS sample sizes are insufficient to detect moderate differences in genetic risk across populations, though more substantial differences in relative genetic risk (relative risk > 1.5) can be detected. We show that conventional approaches that do not account for sampling error from the training sample, such as using a simple t-test, have very high type 1 error rates. When applying our approach to prostate cancer, we demonstrate a higher genetic risk in African Ancestry men, with lower risk in men of European followed by East Asian ancestry.
Collapse
Affiliation(s)
- Iain R. Timmins
- Department of Population Health Sciences, University of Leicester, Leicester, United Kingdom
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, United Kingdom
- Statistical Innovation, AstraZeneca, Cambridge, United Kingdom
| | | | - Frank Dudbridge
- Department of Population Health Sciences, University of Leicester, Leicester, United Kingdom
| |
Collapse
|
7
|
Zhou H, McPeek MS. Overcoming the "feast or famine" effect: improved interaction testing in genome-wide association studies. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.13.580168. [PMID: 38405994 PMCID: PMC10888770 DOI: 10.1101/2024.02.13.580168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
In genetic association analysis of complex traits, detection of interaction (either GxG or GxE) can help to elucidate the genetic architecture and biological mechanisms underlying the trait. Detection of interaction in a genome-wide association study (GWAS) can be methodologically challenging for various reasons, including a high burden of multiple comparisons when testing for epistasis between all possible pairs of a set of genomewide variants, as well as heteroscedasticity effects occurring in the presence of GxG or GxE interaction. In this paper, we address the problem of an even more striking phenomenon that we call the "feast or famine" effect that occurs when testing interaction in a genomewide context. As we verify, even in a simplified setting in which there is no interaction at all (and so no heteroscedasticity), in a GWAS to detect GxG or GxE interaction with a fixed genetic variant or environmental factor, the distribution of the genome-wide p-values under the null hypothesis is not the i.i.d. uniform one that is commonly assumed. Using standard methods, even if all SNPs are independent, some GWASs will have systematically underinflated p-values ("feast"), and others will have systematically overinflated p-values ("famine"), which can lead to false detection of interaction, reduced power, inconsistent results across studies, and failure to replicate true signal. This startling phenomenon is specific to detection of interaction in a GWAS, and it may partly explain why such detection has so far proved challenging and difficult to replicate. We show theoretically that the key cause of this phenomenon is which variables are conditioned on in the analysis, and this suggests an approach to correct the problem by changing the way the conditioning is done. Using this insight, we have developed the TINGA method to adjust the interaction test statistics to make their p-values closer to uniform under the null hypothesis. In simulations we show that TINGA both controls type 1 error and improves power. TINGA allows for covariates and population structure through use of a linear mixed model and accounts for heteroscedasticity. We apply TINGA to detection of epistasis in a study of flowering time in Arabidopsis thaliana.
Collapse
Affiliation(s)
- Huanlin Zhou
- Department of Statistics, The University of Chicago, Chicago, Illinois, U.S.A
| | - Mary Sara McPeek
- Department of Statistics, The University of Chicago, Chicago, Illinois, U.S.A
- Department of Human Genetics, The University of Chicago, Chicago, Illinois, U.S.A
| |
Collapse
|
8
|
Miao J, Wu Y, Lu Q. Statistical methods for gene-environment interaction analysis. WILEY INTERDISCIPLINARY REVIEWS. COMPUTATIONAL STATISTICS 2024; 16:e1635. [PMID: 38699459 PMCID: PMC11064894 DOI: 10.1002/wics.1635] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 09/12/2023] [Indexed: 05/05/2024]
Abstract
Most human complex phenotypes result from multiple genetic and environmental factors and their interactions. Understanding the mechanisms by which genetic and environmental factors interact offers valuable insights into the genetic architecture of complex traits and holds great potential for advancing precision medicine. The emergence of large population biobanks has led to the development of numerous statistical methods aiming at identifying gene-environment interactions (G × E). In this review, we present state-of-the-art statistical methodologies for G × E analysis. We will survey a spectrum of approaches for single-variant G × E mapping, followed by various techniques for polygenic G × E analysis. We conclude this review with a discussion on the future directions and challenges in G × E research.
Collapse
Affiliation(s)
- Jiacheng Miao
- Department of Biostatistics and Medical Informatics, University of Wisconsin–Madison, Madison, Wisconsin, USA
| | - Yixuan Wu
- University of Wisconsin–Madison, Madison, Wisconsin, USA
| | - Qiongshi Lu
- Department of Biostatistics and Medical Informatics, University of Wisconsin–Madison, Madison, Wisconsin, USA
- Department of Statistics, University of Wisconsin–Madison, Madison, Wisconsin, USA
- Center for Demography of Health and Aging, University of Wisconsin–Madison, Madison, Wisconsin, USA
| |
Collapse
|
9
|
Penner-Goeke S, Bothe M, Rek N, Kreitmaier P, Pöhlchen D, Kühnel A, Glaser LV, Kaya E, Krontira AC, Röh S, Czamara D, Ködel M, Monteserin-Garcia J, Diener L, Wölfel B, Sauer S, Rummel C, Riesenberg S, Arloth-Knauer J, Ziller M, Labeur M, Meijsing S, Binder EB. High-throughput screening of glucocorticoid-induced enhancer activity reveals mechanisms of stress-related psychiatric disorders. Proc Natl Acad Sci U S A 2023; 120:e2305773120. [PMID: 38011552 DOI: 10.1073/pnas.2305773120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 09/01/2023] [Indexed: 11/29/2023] Open
Abstract
Exposure to stressful life events increases the risk for psychiatric disorders. Mechanistic insight into the genetic factors moderating the impact of stress can increase our understanding of disease processes. Here, we test 3,662 single nucleotide polymorphisms (SNPs) from preselected expression quantitative trait loci in massively parallel reporter assays to identify genetic variants that modulate the activity of regulatory elements sensitive to glucocorticoids, important mediators of the stress response. Of the tested SNP sequences, 547 were located in glucocorticoid-responsive regulatory elements of which 233 showed allele-dependent activity. Transcripts regulated by these functional variants were enriched for those differentially expressed in psychiatric disorders in the postmortem brain. Phenome-wide Mendelian randomization analysis in 4,439 phenotypes revealed potentially causal associations specifically in neurobehavioral traits, including major depression and other psychiatric disorders. Finally, a functional gene score derived from these variants was significantly associated with differences in the physiological stress response, suggesting that these variants may alter disease risk by moderating the individual set point of the stress response.
Collapse
Affiliation(s)
- Signe Penner-Goeke
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich 80804, Germany
- Graduate School of Systemic Neurosciences, Ludwig Maximilian University of Munich, Planegg 82152, Germany
| | - Melissa Bothe
- Department of Computational Molecular Biology, Max Planck Institute of Molecular Genetics, Berlin 14195, Germany
| | - Nils Rek
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich 80804, Germany
- International Max Planck Research School for Translational Psychiatry, Max Planck Institute of Psychiatry, Munich 80804, Germany
| | - Peter Kreitmaier
- Institute of Translational Genomics, Helmholtz Munich, Neuherberg 85764, Germany
| | - Dorothee Pöhlchen
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich 80804, Germany
- International Max Planck Research School for Translational Psychiatry, Max Planck Institute of Psychiatry, Munich 80804, Germany
| | - Anne Kühnel
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich 80804, Germany
- International Max Planck Research School for Translational Psychiatry, Max Planck Institute of Psychiatry, Munich 80804, Germany
| | - Laura V Glaser
- Department of Computational Molecular Biology, Max Planck Institute of Molecular Genetics, Berlin 14195, Germany
| | - Ezgi Kaya
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich 80804, Germany
- Graduate School of Systemic Neurosciences, Ludwig Maximilian University of Munich, Planegg 82152, Germany
| | - Anthi C Krontira
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich 80804, Germany
- International Max Planck Research School for Translational Psychiatry, Max Planck Institute of Psychiatry, Munich 80804, Germany
| | - Simone Röh
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich 80804, Germany
| | - Darina Czamara
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich 80804, Germany
| | - Maik Ködel
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich 80804, Germany
| | - Jose Monteserin-Garcia
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich 80804, Germany
| | - Laura Diener
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich 80804, Germany
| | - Barbara Wölfel
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich 80804, Germany
| | - Susann Sauer
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich 80804, Germany
| | - Christine Rummel
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich 80804, Germany
| | - Stephan Riesenberg
- Department of Evolutionary Genetics, Max-Planck-Institute for Evolutionary Anthropology, Leipzig 04103, Germany
| | - Janine Arloth-Knauer
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich 80804, Germany
| | - Michael Ziller
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich 80804, Germany
- Department of Psychiatry, University of Muenster, Muenster 48149, Germany
| | - Marta Labeur
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich 80804, Germany
| | - Sebastiaan Meijsing
- Department of Computational Molecular Biology, Max Planck Institute of Molecular Genetics, Berlin 14195, Germany
| | - Elisabeth B Binder
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich 80804, Germany
| |
Collapse
|
10
|
Dahl A, Thompson M, An U, Krebs M, Appadurai V, Border R, Bacanu SA, Werge T, Flint J, Schork AJ, Sankararaman S, Kendler KS, Cai N. Phenotype integration improves power and preserves specificity in biobank-based genetic studies of major depressive disorder. Nat Genet 2023; 55:2082-2093. [PMID: 37985818 PMCID: PMC10703686 DOI: 10.1038/s41588-023-01559-9] [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: 08/01/2022] [Accepted: 09/18/2023] [Indexed: 11/22/2023]
Abstract
Biobanks often contain several phenotypes relevant to diseases such as major depressive disorder (MDD), with partly distinct genetic architectures. Researchers face complex tradeoffs between shallow (large sample size, low specificity/sensitivity) and deep (small sample size, high specificity/sensitivity) phenotypes, and the optimal choices are often unclear. Here we propose to integrate these phenotypes to combine the benefits of each. We use phenotype imputation to integrate information across hundreds of MDD-relevant phenotypes, which significantly increases genome-wide association study (GWAS) power and polygenic risk score (PRS) prediction accuracy of the deepest available MDD phenotype in UK Biobank, LifetimeMDD. We demonstrate that imputation preserves specificity in its genetic architecture using a novel PRS-based pleiotropy metric. We further find that integration via summary statistics also enhances GWAS power and PRS predictions, but can introduce nonspecific genetic effects depending on input. Our work provides a simple and scalable approach to improve genetic studies in large biobanks by integrating shallow and deep phenotypes.
Collapse
Affiliation(s)
- Andrew Dahl
- Section of Genetic Medicine, University of Chicago, Chicago, IL, USA.
| | - Michael Thompson
- Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, USA
| | - Ulzee An
- Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, USA
| | - Morten Krebs
- Institute of Biological Psychiatry, Mental Health Center-Sct Hans, Copenhagen University Hospital-Mental Health Services CPH, Copenhagen, Denmark
| | - Vivek Appadurai
- Institute of Biological Psychiatry, Mental Health Center-Sct Hans, Copenhagen University Hospital-Mental Health Services CPH, Copenhagen, Denmark
| | - Richard Border
- Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Silviu-Alin Bacanu
- Virginia Institute for Psychiatric and Behavioral Genetics and Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA
| | - Thomas Werge
- Institute of Biological Psychiatry, Mental Health Center-Sct Hans, Copenhagen University Hospital-Mental Health Services CPH, Copenhagen, Denmark
- Lundbeck Foundation GeoGenetics Centre, Natural History Museum of Denmark, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jonathan Flint
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Andrew J Schork
- Institute of Biological Psychiatry, Mental Health Center-Sct Hans, Copenhagen University Hospital-Mental Health Services CPH, Copenhagen, Denmark
- Neurogenomics Division, The Translational Genomics Research Institute (TGEN), Phoenix, AZ, USA
- Section for Geogenetics, GLOBE Institute, Faculty of Health and Medical Sciences, Copenhagen University, Copenhagen, Denmark
| | - Sriram Sankararaman
- Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Kenneth S Kendler
- Virginia Institute for Psychiatric and Behavioral Genetics and Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA
| | - Na Cai
- Helmholtz Pioneer Campus, Helmholtz Zentrum München, Neuherberg, Germany.
- Computational Health Centre, Helmholtz Zentrum München, Neuherberg, Germany.
- School of Medicine, Technical University of Munich, Munich, Germany.
| |
Collapse
|
11
|
Di Scipio M, Khan M, Mao S, Chong M, Judge C, Pathan N, Perrot N, Nelson W, Lali R, Di S, Morton R, Petch J, Paré G. A versatile, fast and unbiased method for estimation of gene-by-environment interaction effects on biobank-scale datasets. Nat Commun 2023; 14:5196. [PMID: 37626057 PMCID: PMC10457310 DOI: 10.1038/s41467-023-40913-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 08/16/2023] [Indexed: 08/27/2023] Open
Abstract
Identification of gene-by-environment interactions (GxE) is crucial to understand the interplay of environmental effects on complex traits. However, current methods evaluating GxE on biobank-scale datasets have limitations. We introduce MonsterLM, a multiple linear regression method that does not rely on model specification and provides unbiased estimates of variance explained by GxE. We demonstrate robustness of MonsterLM through comprehensive genome-wide simulations using real genetic data from 325,989 individuals. We estimate GxE using waist-to-hip-ratio, smoking, and exercise as the environmental variables on 13 outcomes (N = 297,529-325,989) in the UK Biobank. GxE variance is significant for 8 environment-outcome pairs, ranging from 0.009 - 0.071. The majority of GxE variance involves SNPs without strong marginal or interaction associations. We observe modest improvements in polygenic score prediction when incorporating GxE. Our results imply a significant contribution of GxE to complex trait variance and we show MonsterLM to be well-purposed to handle this with biobank-scale data.
Collapse
Affiliation(s)
- Matteo Di Scipio
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
- Department of Medicine, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada
| | - Mohammad Khan
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
- Department of Medicine, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada
| | - Shihong Mao
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
| | - Michael Chong
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
- Thrombosis and Atherosclerosis Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, ON, Canada
- Department of Pathology and Molecular Medicine, McMaster University, Michael G. DeGroote School of Medicine, Hamilton, ON, Canada
| | - Conor Judge
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
| | - Nazia Pathan
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
- Department of Medicine, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada
| | - Nicolas Perrot
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
| | - Walter Nelson
- Centre for Data Science and Digital Health, Hamilton Health Sciences, Hamilton, ON, Canada
- Department of Statistical Sciences, University of Toronto, Toronto, ON, Canada
| | - Ricky Lali
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
| | - Shuang Di
- Centre for Data Science and Digital Health, Hamilton Health Sciences, Hamilton, ON, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Robert Morton
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
- Department of Pathology and Molecular Medicine, McMaster University, Michael G. DeGroote School of Medicine, Hamilton, ON, Canada
| | - Jeremy Petch
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
- Department of Medicine, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada
- Centre for Data Science and Digital Health, Hamilton Health Sciences, Hamilton, ON, Canada
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada
| | - Guillaume Paré
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada.
- Thrombosis and Atherosclerosis Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, ON, Canada.
- Department of Pathology and Molecular Medicine, McMaster University, Michael G. DeGroote School of Medicine, Hamilton, ON, Canada.
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada.
| |
Collapse
|
12
|
Flint J. The genetic basis of major depressive disorder. Mol Psychiatry 2023; 28:2254-2265. [PMID: 36702864 PMCID: PMC10611584 DOI: 10.1038/s41380-023-01957-9] [Citation(s) in RCA: 28] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Revised: 12/30/2022] [Accepted: 01/11/2023] [Indexed: 01/27/2023]
Abstract
The genetic dissection of major depressive disorder (MDD) ranks as one of the success stories of psychiatric genetics, with genome-wide association studies (GWAS) identifying 178 genetic risk loci and proposing more than 200 candidate genes. However, the GWAS results derive from the analysis of cohorts in which most cases are diagnosed by minimal phenotyping, a method that has low specificity. I review data indicating that there is a large genetic component unique to MDD that remains inaccessible to minimal phenotyping strategies and that the majority of genetic risk loci identified with minimal phenotyping approaches are unlikely to be MDD risk loci. I show that inventive uses of biobank data, novel imputation methods, combined with more interviewer diagnosed cases, can identify loci that contribute to the episodic severe shifts of mood, and neurovegetative and cognitive changes that are central to MDD. Furthermore, new theories about the nature and causes of MDD, drawing upon advances in neuroscience and psychology, can provide handles on how best to interpret and exploit genetic mapping results.
Collapse
Affiliation(s)
- Jonathan Flint
- Department of Psychiatry and Biobehavioral Sciences, Billy and Audrey Wilder Endowed Chair in Psychiatry and Neuroscience, Center for Neurobehavioral Genetics, 695 Charles E. Young Drive South, 3357B Gonda, Box 951761, Los Angeles, CA, 90095-1761, USA.
| |
Collapse
|
13
|
Westerman KE, Walker ME, Gaynor SM, Wessel J, DiCorpo D, Ma J, Alonso A, Aslibekyan S, Baldridge AS, Bertoni AG, Biggs ML, Brody JA, Chen YDI, Dupuis J, Goodarzi MO, Guo X, Hasbani NR, Heath A, Hidalgo B, Irvin MR, Johnson WC, Kalyani RR, Lange L, Lemaitre RN, Liu CT, Liu S, Moon JY, Nassir R, Pankow JS, Pettinger M, Raffield LM, Rasmussen-Torvik LJ, Selvin E, Senn MK, Shadyab AH, Smith AV, Smith NL, Steffen L, Talegakwar S, Taylor KD, de Vries PS, Wilson JG, Wood AC, Yanek LR, Yao J, Zheng Y, Boerwinkle E, Morrison AC, Fornage M, Russell TP, Psaty BM, Levy D, Heard-Costa NL, Ramachandran VS, Mathias RA, Arnett DK, Kaplan R, North KE, Correa A, Carson A, Rotter JI, Rich SS, Manson JE, Reiner AP, Kooperberg C, Florez JC, Meigs JB, Merino J, Tobias DK, Chen H, Manning AK. Investigating Gene-Diet Interactions Impacting the Association Between Macronutrient Intake and Glycemic Traits. Diabetes 2023; 72:653-665. [PMID: 36791419 PMCID: PMC10130485 DOI: 10.2337/db22-0851] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 02/02/2023] [Indexed: 02/17/2023]
Abstract
Few studies have demonstrated reproducible gene-diet interactions (GDIs) impacting metabolic disease risk factors, likely due in part to measurement error in dietary intake estimation and insufficient capture of rare genetic variation. We aimed to identify GDIs across the genetic frequency spectrum impacting the macronutrient-glycemia relationship in genetically and culturally diverse cohorts. We analyzed 33,187 participants free of diabetes from 10 National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine program cohorts with whole-genome sequencing, self-reported diet, and glycemic trait data. We fit cohort-specific, multivariable-adjusted linear mixed models for the effect of diet, modeled as an isocaloric substitution of carbohydrate for fat, and its interactions with common and rare variants genome-wide. In main effect meta-analyses, participants consuming more carbohydrate had modestly lower glycemic trait values (e.g., for glycated hemoglobin [HbA1c], -0.013% HbA1c/250 kcal substitution). In GDI meta-analyses, a common African ancestry-enriched variant (rs79762542) reached study-wide significance and replicated in the UK Biobank cohort, indicating a negative carbohydrate-HbA1c association among major allele homozygotes only. Simulations revealed that >150,000 samples may be necessary to identify similar macronutrient GDIs under realistic assumptions about effect size and measurement error. These results generate hypotheses for further exploration of modifiable metabolic disease risk in additional cohorts with African ancestry. ARTICLE HIGHLIGHTS We aimed to identify genetic modifiers of the dietary macronutrient-glycemia relationship using whole-genome sequence data from 10 Trans-Omics for Precision Medicine program cohorts. Substitution models indicated a modest reduction in glycemia associated with an increase in dietary carbohydrate at the expense of fat. Genome-wide interaction analysis identified one African ancestry-enriched variant near the FRAS1 gene that may interact with macronutrient intake to influence hemoglobin A1c. Simulation-based power calculations accounting for measurement error suggested that substantially larger sample sizes may be necessary to discover further gene-macronutrient interactions.
Collapse
Affiliation(s)
- Kenneth E. Westerman
- Clinical and Translational Epidemiology Unit, Mongan Institute, Massachusetts General Hospital, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Boston, MA
| | - Maura E. Walker
- Department of Medicine, Section of Preventive Medicine, Boston University School of Medicine, Boston, MA
- Department of Health Sciences, Sargent College of Health and Rehabilitation Sciences, Boston University, Boston, MA
| | - Sheila M. Gaynor
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Jennifer Wessel
- Department of Epidemiology, Richard M. Fairbanks School of Public Health, Indianapolis, IN
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN
- Diabetes Translational Research Center, Indiana University, Indianapolis, IN
| | - Daniel DiCorpo
- Department of Biostatistics, Boston University School of Public Health, Boston, MA
| | - Jiantao Ma
- Nutrition Epidemiology and Data Science, Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA
| | - Alvaro Alonso
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA
| | | | - Abigail S. Baldridge
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Alain G. Bertoni
- Department of Epidemiology and Prevention, Wake Forest School of Medicine, Winston-Salem, NC
| | - Mary L. Biggs
- Department of Biostatistics, University of Washington, Seattle, WA
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA
| | - Jennifer A. Brody
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA
- Department of Medicine, University of Washington, Seattle, WA
| | - Yii-Der Ida Chen
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA
| | - Joseé Dupuis
- Department of Biostatistics, Boston University School of Public Health, Boston, MA
| | - Mark O. Goodarzi
- Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA
| | - Natalie R. Hasbani
- Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX
| | - Adam Heath
- Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX
| | - Bertha Hidalgo
- School of Public Health, University of Alabama at Birmingham, Birmingham, AL
| | - Marguerite R. Irvin
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL
| | - W. Craig Johnson
- Department of Biostatistics, University of Washington, Seattle, WA
| | - Rita R. Kalyani
- GeneSTAR Research Program, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Leslie Lange
- Department of Medicine, Anschutz Medical Campus, University of Colorado, Aurora, CO
| | - Rozenn N. Lemaitre
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA
- Department of Internal Medicine, University of Washington, Seattle, WA
| | - Ching-Ti Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA
- National Heart, Lung, and Blood Institute and Boston University’s Framingham Heart Study, Framingham, MA
- Evans Department of Medicine, Section of Preventive Medicine and Epidemiology, Boston University School of Medicine, Boston, MA
- Evans Department of Medicine, Whitaker Cardiovascular Institute and Cardiology Section, Boston University School of Medicine, Boston, MA
| | - Simin Liu
- Center for Global Cardiometabolic Health, Boston, MA
| | - Jee-Young Moon
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY
| | - Rami Nassir
- Department of Pathology, School of Medicine, Umm Al-Qura University, Mecca, Saudi Arabia
| | - James S. Pankow
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN
| | - Mary Pettinger
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA
| | - Laura M. Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | | | - Elizabeth Selvin
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Mackenzie K. Senn
- USDA/ARS Children’s Nutrition Research Center, Baylor College of Medicine, Houston, TX
| | - Aladdin H. Shadyab
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla, CA
| | - Albert V. Smith
- Department of Biostatistics, University of Michigan, Ann Arbor, MI
| | - Nicholas L. Smith
- Department of Epidemiology, University of Washington, Seattle, WA
- Kaiser Permanente Washington Health Research Institute, Seattle, WA
- Department of Veterans Affairs Office of Research and Development, Seattle Epidemiologic Research and Information Center, Seattle, WA
| | - Lyn Steffen
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN
| | - Sameera Talegakwar
- Department of Exercise and Nutrition Sciences, Milken Institute School of Public Health, The George Washington University, Washington, DC
| | - Kent D. Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA
| | - Paul S. de Vries
- Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX
| | - James G. Wilson
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Alexis C. Wood
- USDA/ARS Children’s Nutrition Research Center, Baylor College of Medicine, Houston, TX
| | - Lisa R. Yanek
- GeneSTAR Research Program, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Jie Yao
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA
| | - Yinan Zheng
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Eric Boerwinkle
- Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX
| | - Alanna C. Morrison
- Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX
| | - Miriam Fornage
- Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX
| | - Tracy P. Russell
- Department of Pathology and Laboratory Medicine, University of Vermont Larner College of Medicine, Burlington, VT
| | - Bruce M. Psaty
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA
- Department of Medicine, University of Washington, Seattle, WA
- Department of Epidemiology, University of Washington, Seattle, WA
- Department of Health Systems and Population Health, University of Washington, Seattle, WA
| | - Daniel Levy
- National Heart, Lung, and Blood Institute and Boston University’s Framingham Heart Study, Framingham, MA
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, Bethesda, MD
| | - Nancy L. Heard-Costa
- National Heart, Lung, and Blood Institute and Boston University’s Framingham Heart Study, Framingham, MA
- Department of Neurology, Boston University School of Medicine, Boston, MA
| | - Vasan S. Ramachandran
- National Heart, Lung, and Blood Institute and Boston University’s Framingham Heart Study, Framingham, MA
- Evans Department of Medicine, Section of Preventive Medicine and Epidemiology, Boston University School of Medicine, Boston, MA
- Evans Department of Medicine, Whitaker Cardiovascular Institute and Cardiology Section, Boston University School of Medicine, Boston, MA
| | - Rasika A. Mathias
- GeneSTAR Research Program, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Donna K. Arnett
- College of Public Health, University of Kentucky, Lexington, KY
| | - Robert Kaplan
- Clinical Excellence Research Center, School of Medicine, Stanford University, Stanford, CA
| | - Kari E. North
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Adolfo Correa
- Department of Population Health Science, University of Mississippi Medical Center, Jackson, MS
| | - April Carson
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS
| | - Jerome I. Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA
| | - Stephen S. Rich
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA
| | | | | | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA
| | - Jose C. Florez
- Department of Medicine, Harvard Medical School, Boston, MA
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Boston, MA
- Diabetes Unit, Massachusetts General Hospital, Boston, MA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
| | - James B. Meigs
- Department of Medicine, Harvard Medical School, Boston, MA
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Boston, MA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA
| | - Jordi Merino
- Department of Medicine, Harvard Medical School, Boston, MA
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Boston, MA
- Diabetes Unit, Massachusetts General Hospital, Boston, MA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
| | - Deirdre K. Tobias
- Division of Preventive Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Han Chen
- Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX
- Center for Precision Health, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX
| | - Alisa K. Manning
- Clinical and Translational Epidemiology Unit, Mongan Institute, Massachusetts General Hospital, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Boston, MA
| |
Collapse
|
14
|
BROWN TYSONH, HOMAN PATRICIA. The Future of Social Determinants of Health: Looking Upstream to Structural Drivers. Milbank Q 2023; 101:36-60. [PMID: 37096627 PMCID: PMC10126983 DOI: 10.1111/1468-0009.12641] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 10/15/2022] [Accepted: 01/06/2023] [Indexed: 04/26/2023] Open
Abstract
Policy Points Policies that redress oppressive social, economic, and political conditions are essential for improving population health and achieving health equity. Efforts to remedy structural oppression and its deleterious effects should account for its multilevel, multifaceted, interconnected, systemic, and intersectional nature. The U.S. Department of Health and Human Services should facilitate the creation and maintenance of a national publicly available, user-friendly data infrastructure on contextual measures of structural oppression. Publicly funded research on social determinants of health should be mandated to (a) analyze health inequities in relation to relevant data on structural conditions and (b) deposit the data in the publicly available data repository.
Collapse
|
15
|
Gorla A, Sankararaman S, Burchard E, Flint J, Zaitlen N, Rahmani E. Phenotypic subtyping via contrastive learning. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.05.522921. [PMID: 36711575 PMCID: PMC9881932 DOI: 10.1101/2023.01.05.522921] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Defining and accounting for subphenotypic structure has the potential to increase statistical power and provide a deeper understanding of the heterogeneity in the molecular basis of complex disease. Existing phenotype subtyping methods primarily rely on clinically observed heterogeneity or metadata clustering. However, they generally tend to capture the dominant sources of variation in the data, which often originate from variation that is not descriptive of the mechanistic heterogeneity of the phenotype of interest; in fact, such dominant sources of variation, such as population structure or technical variation, are, in general, expected to be independent of subphenotypic structure. We instead aim to find a subspace with signal that is unique to a group of samples for which we believe that subphenotypic variation exists (e.g., cases of a disease). To that end, we introduce Phenotype Aware Components Analysis (PACA), a contrastive learning approach leveraging canonical correlation analysis to robustly capture weak sources of subphenotypic variation. In the context of disease, PACA learns a gradient of variation unique to cases in a given dataset, while leveraging control samples for accounting for variation and imbalances of biological and technical confounders between cases and controls. We evaluated PACA using an extensive simulation study, as well as on various subtyping tasks using genotypes, transcriptomics, and DNA methylation data. Our results provide multiple strong evidence that PACA allows us to robustly capture weak unknown variation of interest while being calibrated and well-powered, far superseding the performance of alternative methods. This renders PACA as a state-of-the-art tool for defining de novo subtypes that are more likely to reflect molecular heterogeneity, especially in challenging cases where the phenotypic heterogeneity may be masked by a myriad of strong unrelated effects in the data.
Collapse
Affiliation(s)
- Aditya Gorla
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, USA
| | - Sriram Sankararaman
- Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
| | - Esteban Burchard
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA
| | - Jonathan Flint
- Department of Psychiatry and Behavioral Sciences, Brain Research Institute, University of California, Los Angeles, Los Angeles, CA, USA
| | - Noah Zaitlen
- Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Elior Rahmani
- Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| |
Collapse
|
16
|
Novembre J, Stein C, Asgari S, Gonzaga-Jauregui C, Landstrom A, Lemke A, Li J, Mighton C, Taylor M, Tishkoff S. Addressing the challenges of polygenic scores in human genetic research. Am J Hum Genet 2022; 109:2095-2100. [PMID: 36459976 PMCID: PMC9808501 DOI: 10.1016/j.ajhg.2022.10.012] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/04/2022] Open
Abstract
The genotyping of millions of human samples has made it possible to evaluate variants across the human genome for their possible association with risks for numerous diseases and other traits by using genome-wide association studies (GWASs). The associations between phenotype and genotype found in GWASs make possible the construction of polygenic scores (PGSs), which aim to predict a trait or disease outcome in an individual on the basis of their genotype (in the disease case, the term polygenic risk score [PRS] is often used). PGSs have shown promise for studying the biology of complex traits and as a tool for evaluating individual disease risks in clinical settings. Although the quantity and quality of data to compute PGSs are increasing, challenges remain in the technical aspects of developing PGSs and in the ethical and social issues that might arise from their use. This ASHG Guidance emphasizes three major themes for researchers working with or interested in the application of PGSs in their own research: (1) developing diverse research cohorts; (2) fostering robustness in the development, application, and interpretation of PGSs; and (3) improving the communication of PGS results and their implications to broad audiences.
Collapse
Affiliation(s)
- John Novembre
- Professional Practice and Social Implications Committee Polygenic Scores Guidance Writing Group, American Society of Human Genetics, Rockville MD, USA,Department of Human Genetics, University of Chicago, Chicago, IL, USA,Department of Ecology and Evolution, University of Chicago, Chicago, IL, USA,Corresponding author
| | - Catherine Stein
- Professional Practice and Social Implications Committee Polygenic Scores Guidance Writing Group, American Society of Human Genetics, Rockville MD, USA,Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA,Corresponding author
| | - Samira Asgari
- Professional Practice and Social Implications Committee Polygenic Scores Guidance Writing Group, American Society of Human Genetics, Rockville MD, USA,Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Claudia Gonzaga-Jauregui
- Professional Practice and Social Implications Committee Polygenic Scores Guidance Writing Group, American Society of Human Genetics, Rockville MD, USA,International Laboratory for Human Genome Research, Laboratorio Internacional de Investigación sobre el Genoma Humano, Universidad Nacional Autónoma de México, Juriquilla, Querétaro, México
| | - Andrew Landstrom
- Professional Practice and Social Implications Committee Polygenic Scores Guidance Writing Group, American Society of Human Genetics, Rockville MD, USA,Department of Pediatrics, Division of Cardiology, Duke University School of Medicine, Durham, NC, USA
| | - Amy Lemke
- Professional Practice and Social Implications Committee Polygenic Scores Guidance Writing Group, American Society of Human Genetics, Rockville MD, USA,Norton Children’s Research Institute, affiliated with the University of Louisville School of Medicine, Louisville, KY, USA
| | - Jun Li
- Professional Practice and Social Implications Committee Polygenic Scores Guidance Writing Group, American Society of Human Genetics, Rockville MD, USA,Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Chloe Mighton
- Professional Practice and Social Implications Committee Polygenic Scores Guidance Writing Group, American Society of Human Genetics, Rockville MD, USA,Genomics Health Services Research Program, St. Michael’s Hospital, Unity Health Toronto, Toronto, ON, Canada,Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Matthew Taylor
- Professional Practice and Social Implications Committee Polygenic Scores Guidance Writing Group, American Society of Human Genetics, Rockville MD, USA,Adult Medical Genetics Program, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Sarah Tishkoff
- Professional Practice and Social Implications Committee Polygenic Scores Guidance Writing Group, American Society of Human Genetics, Rockville MD, USA,Department of Genetics, Center for Global Genomics and Health Equity, University of Pennsylvania, Philadelphia, PA, USA,Department of Biology, Center for Global Genomics and Health Equity, University of Pennsylvania, Philadelphia, PA, USA
| |
Collapse
|
17
|
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.
Collapse
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
| |
Collapse
|
18
|
Hecker J, Prokopenko D, Moll M, Lee S, Kim W, Qiao D, Voorhies K, Kim W, Vansteelandt S, Hobbs BD, Cho MH, Silverman EK, Lutz SM, DeMeo DL, Weiss ST, Lange C. A robust and adaptive framework for interaction testing in quantitative traits between multiple genetic loci and exposure variables. PLoS Genet 2022; 18:e1010464. [PMID: 36383614 PMCID: PMC9668174 DOI: 10.1371/journal.pgen.1010464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 10/04/2022] [Indexed: 11/17/2022] Open
Abstract
The identification and understanding of gene-environment interactions can provide insights into the pathways and mechanisms underlying complex diseases. However, testing for gene-environment interaction remains a challenge since a.) statistical power is often limited and b.) modeling of environmental effects is nontrivial and such model misspecifications can lead to false positive interaction findings. To address the lack of statistical power, recent methods aim to identify interactions on an aggregated level using, for example, polygenic risk scores. While this strategy can increase the power to detect interactions, identifying contributing genes and pathways is difficult based on these relatively global results. Here, we propose RITSS (Robust Interaction Testing using Sample Splitting), a gene-environment interaction testing framework for quantitative traits that is based on sample splitting and robust test statistics. RITSS can incorporate sets of genetic variants and/or multiple environmental factors. Based on the user's choice of statistical/machine learning approaches, a screening step selects and combines potential interactions into scores with improved interpretability. In the testing step, the application of robust statistics minimizes the susceptibility to main effect misspecifications. Using extensive simulation studies, we demonstrate that RITSS controls the type 1 error rate in a wide range of scenarios, and we show how the screening strategy influences statistical power. In an application to lung function phenotypes and human height in the UK Biobank, RITSS identified highly significant interactions based on subcomponents of genetic risk scores. While the contributing single variant interaction signals are weak, our results indicate interaction patterns that result in strong aggregated effects, providing potential insights into underlying gene-environment interaction mechanisms.
Collapse
Affiliation(s)
- Julian Hecker
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
| | - Dmitry Prokopenko
- Harvard Medical School, Boston, Massachusetts, United States of America
- Genetics and Aging Unit and McCance Center for Brain Health, Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Matthew Moll
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
- Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
| | - Sanghun Lee
- Department of Medical Consilience, Division of Medicine, Graduate School, Dankook University, Yongin, South Korea
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Wonji Kim
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
| | - Dandi Qiao
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
| | - Kirsten Voorhies
- Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Population Medicine, PRecisiOn Medicine Translational Research (PROMoTeR) Center, Harvard Pilgrim Health Care, Boston, Massachusetts, United States of America
| | - Woori Kim
- Harvard Medical School, Boston, Massachusetts, United States of America
- Systems Biology and Computer Science Program, Ann Romney Center for Neurological Diseases, Department of Neurology, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
| | - Stijn Vansteelandt
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Gent, Belgium
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Brian D. Hobbs
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
- Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
| | - Michael H. Cho
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
- Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
| | - Edwin K. Silverman
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
- Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
| | - Sharon M. Lutz
- Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
- Department of Population Medicine, PRecisiOn Medicine Translational Research (PROMoTeR) Center, Harvard Pilgrim Health Care, Boston, Massachusetts, United States of America
| | - Dawn L. DeMeo
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
| | - Scott T. Weiss
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
| | - Christoph Lange
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
| |
Collapse
|
19
|
Lea AJ, Peng J, Ayroles JF. Diverse environmental perturbations reveal the evolution and context-dependency of genetic effects on gene expression levels. Genome Res 2022; 32:1826-1839. [PMID: 36229124 PMCID: PMC9712631 DOI: 10.1101/gr.276430.121] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 09/07/2022] [Indexed: 01/18/2023]
Abstract
There is increasing appreciation that, in addition to being shaped by an individual's genotype and environment, most complex traits are also determined by poorly understood interactions between these two factors. So-called "genotype × environment" (G×E) interactions remain difficult to map at the organismal level but can be uncovered using molecular phenotypes. To do so at large scale, we used TM3'seq to profile transcriptomes across 12 cellular environments in 544 immortalized B cell lines from the 1000 Genomes Project. We mapped the genetic basis of gene expression levels across environments and revealed a context-dependent genetic architecture: The average heritability of gene expression levels increased in treatment relative to control conditions, and on average, each treatment revealed new expression quantitative trait loci (eQTLs) at 11% of genes. Across our experiments, 22% of all identified eQTLs were context-dependent, and this group was enriched for trait- and disease-associated loci. Further, evolutionary analyses suggested that positive selection has shaped G×E loci involved in responding to immune challenges and hormones but not to man-made chemicals. We hypothesize that this reflects a reduced opportunity for selection to act on responses to molecules recently introduced into human environments. Together, our work highlights the importance of considering an exposure's evolutionary history when studying and interpreting G×E interactions, and provides new insight into the evolutionary mechanisms that maintain G×E loci in human populations.
Collapse
Affiliation(s)
- Amanda J. Lea
- Department of Ecology and Evolution, Princeton University, Princeton, New Jersey 08544, USA;,Lewis Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08544, USA
| | - Julie Peng
- Department of Ecology and Evolution, Princeton University, Princeton, New Jersey 08544, USA;,Lewis Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08544, USA
| | - Julien F. Ayroles
- Department of Ecology and Evolution, Princeton University, Princeton, New Jersey 08544, USA;,Lewis Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08544, USA
| |
Collapse
|
20
|
Genome-wide association study identifies a gene responsible for temperature-dependent rice germination. Nat Commun 2022; 13:5665. [PMID: 36175401 PMCID: PMC9523024 DOI: 10.1038/s41467-022-33318-5] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 09/13/2022] [Indexed: 11/08/2022] Open
Abstract
Environment is an important determinant of agricultural productivity; therefore, crops have been bred with traits adapted to their environment. It is assumed that the physiology of seed germination is optimised for various climatic conditions. Here, to understand the genetic basis underlying seed germination, we conduct a genome-wide association study considering genotype-by-environment interactions on the germination rate of Japanese rice cultivars under different temperature conditions. We find that a 4 bp InDel in one of the 14-3-3 family genes, GF14h, preferentially changes the germination rate of rice under optimum temperature conditions. The GF14h protein constitutes a transcriptional regulatory module with a bZIP-type transcription factor, OREB1, and a florigen-like protein, MOTHER OF FT AND TFL 2, to control the germination rate by regulating abscisic acid (ABA)-responsive genes. The GF14h loss-of-function allele enhances ABA signalling and reduces the germination rate. This allele is found in rice varieties grown in the northern area and in modern cultivars of Japan and China, suggesting that it contributes to the geographical adaptation of rice. This study demonstrates the complicated molecular system involved in the regulation of seed germination in response to temperature, which has allowed rice to be grown in various geographical locations.
Collapse
|
21
|
Hansen PB, Ruud AK, de los Campos G, Malinowska M, Nagy I, Svane SF, Thorup-Kristensen K, Jensen JD, Krusell L, Asp T. Integration of DNA Methylation and Transcriptome Data Improves Complex Trait Prediction in Hordeum vulgare. PLANTS 2022; 11:plants11172190. [PMID: 36079572 PMCID: PMC9459846 DOI: 10.3390/plants11172190] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 08/19/2022] [Accepted: 08/21/2022] [Indexed: 11/30/2022]
Abstract
Whole-genome multi-omics profiles contain valuable information for the characterization and prediction of complex traits in plants. In this study, we evaluate multi-omics models to predict four complex traits in barley (Hordeum vulgare); grain yield, thousand kernel weight, protein content, and nitrogen uptake. Genomic, transcriptomic, and DNA methylation data were obtained from 75 spring barley lines tested in the RadiMax semi-field phenomics facility under control and water-scarce treatment. By integrating multi-omics data at genomic, transcriptomic, and DNA methylation regulatory levels, a higher proportion of phenotypic variance was explained (0.72–0.91) than with genomic models alone (0.55–0.86). The correlation between predictions and phenotypes varied from 0.17–0.28 for control plants and 0.23–0.37 for water-scarce plants, and the increase in accuracy was significant for nitrogen uptake and protein content compared to models using genomic information alone. Adding transcriptomic and DNA methylation information to the prediction models explained more of the phenotypic variance attributed to the environment in grain yield and nitrogen uptake. It furthermore explained more of the non-additive genetic effects for thousand kernel weight and protein content. Our results show the feasibility of multi-omics prediction for complex traits in barley.
Collapse
Affiliation(s)
- Pernille Bjarup Hansen
- Center for Quantitative Genetics and Genomics, Aarhus University, 4200 Slagelse, Denmark
- Correspondence: (P.B.H.); (T.A.); Tel.: +45-87158243 (T.A.)
| | - Anja Karine Ruud
- Center for Quantitative Genetics and Genomics, Aarhus University, 4200 Slagelse, Denmark
| | - Gustavo de los Campos
- Departments of Epidemiology & Biostatistics and Statistics & Probability, Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI 48824, USA
| | - Marta Malinowska
- Center for Quantitative Genetics and Genomics, Aarhus University, 4200 Slagelse, Denmark
| | - Istvan Nagy
- Center for Quantitative Genetics and Genomics, Aarhus University, 4200 Slagelse, Denmark
| | - Simon Fiil Svane
- Section for Crop Sciences, Department of Plant and Environmental Sciences, Copenhagen University, 2630 Taastrup, Denmark
| | - Kristian Thorup-Kristensen
- Section for Crop Sciences, Department of Plant and Environmental Sciences, Copenhagen University, 2630 Taastrup, Denmark
| | | | - Lene Krusell
- Sejet Plant Breeding, Nørremarksvej 67, 8700 Horsens, Denmark
| | - Torben Asp
- Center for Quantitative Genetics and Genomics, Aarhus University, 4200 Slagelse, Denmark
- Correspondence: (P.B.H.); (T.A.); Tel.: +45-87158243 (T.A.)
| |
Collapse
|
22
|
Wright KM, Deighan AG, Di Francesco A, Freund A, Jojic V, Churchill GA, Raj A. Age and diet shape the genetic architecture of body weight in diversity outbred mice. eLife 2022; 11:64329. [PMID: 35838135 PMCID: PMC9286741 DOI: 10.7554/elife.64329] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Accepted: 05/20/2022] [Indexed: 12/26/2022] Open
Abstract
Understanding how genetic variation shapes a complex trait relies on accurately quantifying both the additive genetic and genotype–environment interaction effects in an age-dependent manner. We used a linear mixed model to quantify diet-dependent genetic contributions to body weight measured through adulthood in diversity outbred female mice under five diets. We observed that heritability of body weight declined with age under all diets, except the 40% calorie restriction diet. We identified 14 loci with age-dependent associations and 19 loci with age- and diet-dependent associations, with many diet-dependent loci previously linked to neurological function and behavior in mice or humans. We found their allelic effects to be dynamic with respect to genomic background, age, and diet, identifying several loci where distinct alleles affect body weight at different ages. These results enable us to more fully understand and predict the effectiveness of dietary intervention on overall health throughout age in distinct genetic backgrounds. Body weight is one trait influenced by genes, age and environmental factors. Both internal and external environmental pressures are known to affect genetic variation over time. However, it is largely unknown how all factors – including age – interact to shape metabolism and bodyweight. Wright et al. set out to quantify the interactions between genes and diet in ageing mice and found that the effect of genetics on mouse body weight changes with age. In the experiments, Wright et al. weighed 960 female mice with diverse genetic backgrounds, starting at two months of age into adulthood. The animals were randomized to different diets at six months of age. Some mice had unlimited food access, others received 20% or 40% less calories than a typical mouse diet, and some fasted one or two days per week. Variations in their genetic background explained about 80% of differences in mice’s weight, but the influence of genetics relative to non-genetic factors decreased as they aged. Mice on the 40% calorie restriction diet were an exception to this rule and genetics accounted for 80% of their weight throughout adulthood, likely due to reduced influence from diet and reduced interactions between diet and genes. Several genes involved in metabolism, neurological function, or behavior, were associated with mouse weight. The experiments highlight the importance of considering interactions between genetics, environment, and age in determining complex traits like body weight. The results and the approaches used by Wright et al. may help other scientists learn more about how the genetic predisposition to disease changes with environmental stimuli and age.
Collapse
Affiliation(s)
- Kevin M Wright
- Calico Life Sciences LLC, South San Francisco, United States
| | | | | | - Adam Freund
- Calico Life Sciences LLC, South San Francisco, United States
| | - Vladimir Jojic
- Calico Life Sciences LLC, South San Francisco, United States
| | | | - Anil Raj
- Calico Life Sciences LLC, South San Francisco, United States
| |
Collapse
|
23
|
Tonnelé H, Baud A. Accounting for diet and age. eLife 2022; 11:80890. [PMID: 35838133 PMCID: PMC9286735 DOI: 10.7554/elife.80890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
The diet and age of mice can modulate how different genetic variants impact body weight, demonstrating the need to take context into account when performing genetic studies.
Collapse
Affiliation(s)
- Hélène Tonnelé
- Centre for Genomic Regulation, Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Amelie Baud
- Centre for Genomic Regulation, Barcelona Institute of Science and Technology, Barcelona, Spain.,Universitat Pompeu Fabra, Barcelona, Spain
| |
Collapse
|
24
|
Yousefi H, Delavar MR, Piroozian F, Baghi M, Nguyen K, Cheng T, Vittori C, Worthylake D, Alahari SK. Hippo signaling pathway: A comprehensive gene expression profile analysis in breast cancer. Biomed Pharmacother 2022; 151:113144. [PMID: 35623167 DOI: 10.1016/j.biopha.2022.113144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 05/10/2022] [Accepted: 05/15/2022] [Indexed: 11/30/2022] Open
Abstract
Breast cancer (BC) is the most frequently diagnosed malignancy in women and a major public health concern. The Hippo pathway is an evolutionarily conserved signaling pathway that serves as a key regulator for a wide variety of biological processes. Hippo signaling has been shown to have both oncogenic and tumor-suppressive functions in various cancers. Core components of the Hippo pathway consist of various kinases and downstream effectors such as YAP/TAZ. In the current report, differential expression of Hippo pathway elements as well as the correlation of Hippo pathway mRNAs with various clinicopathologic characteristics, including molecular subtypes, receptor status, and methylation status, has been investigated in BC using METABRIC and TCGA datasets. In this review, we note deregulation of several Hippo signaling elements in BC patients. Moreover, we see examples of negative correlations between methylation of Hippo genes and mRNA expression. The expression of Hippo genes significantly varies between different receptor subgroups. Because of the clear associations between mRNA expression and methylation status, DNA methylation may be one of the mechanisms that regulate the Hippo pathway in BC cells. Differential expression of Hippo genes among various BC molecular subtypes suggests that Hippo signaling may function differently in different subtypes of BC. Our data also highlights an interesting link between Hippo components' transcription and ER negativity in BC. In conclusion, substantial deregulation of Hippo signaling components suggests an important role of these genes in breast cancer.
Collapse
Affiliation(s)
- Hassan Yousefi
- Louisiana State University Health Science Center (LSUHSC), Biochemistry & Molecular Biology, New Orleans, LA, USA
| | - Mahsa Rostamian Delavar
- Department of Cell and Molecular Biology and Microbiology, Faculty of Biological Science and Technology, University of Isfahan, Isfahan, Iran
| | | | - Masoud Baghi
- Department of Cell and Molecular Biology and Microbiology, Faculty of Biological Science and Technology, University of Isfahan, Isfahan, Iran
| | - Khoa Nguyen
- Department of Medicine, Tulane University School of Medicine, New Orleans, LA, USA
| | - Thomas Cheng
- Department of Medicine, Tulane University School of Medicine, New Orleans, LA, USA
| | - Cecilia Vittori
- Louisiana State University Health Sciences Center and Stanley S. Scott Cancer Center, New Orleans, LA, USA
| | - David Worthylake
- Louisiana State University Health Science Center (LSUHSC), Biochemistry & Molecular Biology, New Orleans, LA, USA
| | - Suresh K Alahari
- Louisiana State University Health Science Center (LSUHSC), Biochemistry & Molecular Biology, New Orleans, LA, USA.
| |
Collapse
|
25
|
Gillett AC, Jermy BS, Lee SH, Pain O, Howard DM, Hagenaars SP, Hanscombe KB, Coleman JRI, Lewis CM. Exploring polygenic-environment and residual-environment interactions for depressive symptoms within the UK Biobank. Genet Epidemiol 2022; 46:219-233. [PMID: 35438196 PMCID: PMC9541465 DOI: 10.1002/gepi.22449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 02/04/2022] [Accepted: 03/15/2022] [Indexed: 11/10/2022]
Abstract
Substantial advances have been made in identifying genetic contributions to depression, but little is known about how the effect of genes can be modulated by the environment, creating a gene-environment interaction. Using multivariate reaction norm models (MRNMs) within the UK Biobank (N = 61294-91644), we investigate whether the polygenic and residual variance components of depressive symptoms are modulated by 17 a priori selected covariate traits-12 environmental variables and 5 biomarkers. MRNMs, a mixed-effects modelling approach, provide unbiased polygenic-covariate interaction estimates for a quantitative trait by controlling for outcome-covariate correlations and residual-covariate interactions. A continuous depressive symptom variable was the outcome in 17 MRNMs-one for each covariate trait. Each MRNM had a fixed-effects model (fixed effects included the covariate trait, demographic variables, and principal components) and a random effects model (where polygenic-covariate and residual-covariate interactions are modelled). Of the 17 selected covariates, 11 significantly modulate deviations in depressive symptoms through the modelled interactions, but no single interaction explains a large proportion of phenotypic variation. Results are dominated by residual-covariate interactions, suggesting that covariate traits (including neuroticism, childhood trauma, and BMI) typically interact with unmodelled variables, rather than a genome-wide polygenic component, to influence depressive symptoms. Only average sleep duration has a polygenic-covariate interaction explaining a demonstrably nonzero proportion of the variability in depressive symptoms. This effect is small, accounting for only 1.22% (95% confidence interval: [0.54, 1.89]) of variation. The presence of an interaction highlights a specific focus for intervention, but the negative results here indicate a limited contribution from polygenic-environment interactions.
Collapse
Affiliation(s)
- Alexandra C Gillett
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Trust, London, UK
| | - Bradley S Jermy
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Trust, London, UK
| | - Sang Hong Lee
- Australian Centre for Precision Health, University of South Australia, SA, Adelaide, Australia.,UniSA Allied Health and Human Performance, University of South Australia, Adelaide, SA, Australia
| | - Oliver Pain
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Trust, London, UK
| | - David M Howard
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
| | - Saskia P Hagenaars
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Ken B Hanscombe
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Jonathan R I Coleman
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Trust, London, UK
| | - Cathryn M Lewis
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Trust, London, UK.,Department of Medical and Molecular Genetics, Faculty of Life Sciences and Medicine, King's College London, London, UK
| |
Collapse
|
26
|
Fernández-Santiago R, Sharma M. What have we learned from genome-wide association studies (GWAS) in Parkinson's disease? Ageing Res Rev 2022; 79:101648. [PMID: 35595184 DOI: 10.1016/j.arr.2022.101648] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Revised: 04/11/2022] [Accepted: 05/11/2022] [Indexed: 11/01/2022]
Abstract
After fifteen years of genome-wide association studies (GWAS) in Parkinson's disease (PD), what have we learned? Addressing this question will help catalogue the progress made towards elucidating disease mechanisms, improving the clinical utility of the identified loci, and envisioning how we can harness the strides to develop translational GWAS strategies. Here we review the advances of PD GWAS made to date while critically addressing the challenges and opportunities for next-generation GWAS. Thus, deciphering the missing heritability in underrepresented populations is currently at the reach of hand for a truly comprehensive understanding of the genetics of PD across the different ethnicities. Moreover, state-of-the-art GWAS designs hold a true potential for enhancing the clinical applicability of genetic findings, for instance, by improving disease prediction (PD risk and progression). Lastly, advanced PD GWAS findings, alone or in combination with clinical and environmental parameters, are expected to have the capacity for defining patient enriched cohorts stratified by genetic risk profiles and readily available for neuroprotective clinical trials. Overall, envisioning future strategies for advanced GWAS is currently timely and can be instrumental in providing novel genetic readouts essential for a true clinical translatability of PD genetic findings.
Collapse
|
27
|
Ding Y, Hou K, Burch KS, Lapinska S, Privé F, Vilhjálmsson B, Sankararaman S, Pasaniuc B. Large uncertainty in individual polygenic risk score estimation impacts PRS-based risk stratification. Nat Genet 2022; 54:30-39. [PMID: 34931067 PMCID: PMC8758557 DOI: 10.1038/s41588-021-00961-5] [Citation(s) in RCA: 57] [Impact Index Per Article: 28.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2020] [Accepted: 09/29/2021] [Indexed: 01/05/2023]
Abstract
Although the cohort-level accuracy of polygenic risk scores (PRSs)-estimates of genetic value at the individual level-has been widely assessed, uncertainty in PRSs remains underexplored. In the present study, we show that Bayesian PRS methods can estimate the variance of an individual's PRS and can yield well-calibrated credible intervals via posterior sampling. For 13 real traits in the UK Biobank (n = 291,273 unrelated 'white British'), we observe large variances in individual PRS estimates which impact interpretation of PRS-based stratification; averaging across traits, only 0.8% (s.d. = 1.6%) of individuals with PRS point estimates in the top decile have corresponding 95% credible intervals fully contained in the top decile. We provide an analytical estimator for the expectation of individual PRS variance as a function of SNP heritability, number of causal SNPs and sample size. Our results showcase the importance of incorporating uncertainty in individual PRS estimates into subsequent analyses.
Collapse
Affiliation(s)
- Yi Ding
- Bioinformatics Interdepartmental Program, University of California, Los Angeles (UCLA), Los Angeles, CA, USA.
| | - Kangcheng Hou
- Bioinformatics Interdepartmental Program, University of California, Los Angeles (UCLA), Los Angeles, CA, USA.
| | - Kathryn S Burch
- Bioinformatics Interdepartmental Program, University of California, Los Angeles (UCLA), Los Angeles, CA, USA
| | - Sandra Lapinska
- Bioinformatics Interdepartmental Program, University of California, Los Angeles (UCLA), Los Angeles, CA, USA
| | - Florian Privé
- Department of Economics and Business Economics, National Centre for Register-Based Research, Aarhus University, Aarhus, Denmark
| | - Bjarni Vilhjálmsson
- Department of Economics and Business Economics, National Centre for Register-Based Research, Aarhus University, Aarhus, Denmark
| | - Sriram Sankararaman
- Bioinformatics Interdepartmental Program, University of California, Los Angeles (UCLA), Los Angeles, CA, USA
- Department of Computer Science, UCLA, Los Angeles, CA, USA
- Department of Computational Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Bogdan Pasaniuc
- Bioinformatics Interdepartmental Program, University of California, Los Angeles (UCLA), Los Angeles, CA, USA.
- Department of Computational Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
| |
Collapse
|
28
|
Kernel-based gene-environment interaction tests for rare variants with multiple quantitative phenotypes. PLoS One 2022; 17:e0275929. [PMID: 36223383 PMCID: PMC9555665 DOI: 10.1371/journal.pone.0275929] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2022] [Accepted: 09/26/2022] [Indexed: 11/19/2022] Open
Abstract
Previous studies have suggested that gene-environment interactions (GEIs) between a common variant and an environmental factor can influence multiple correlated phenotypes simultaneously, that is, GEI pleiotropy, and that analyzing multiple phenotypes jointly is more powerful than analyzing phenotypes separately by using single-phenotype GEI tests. Methods to test the GEI for rare variants with multiple phenotypes are, however, lacking. In our work, we model the correlation among the GEI effects of a variant on multiple quantitative phenotypes through four kernels and propose four multiphenotype GEI tests for rare variants, which are a test with a homogeneous kernel (Hom-GEI), a test with a heterogeneous kernel (Het-GEI), a test with a projection phenotype kernel (PPK-GEI) and a test with a linear phenotype kernel (LPK-GEI). Through numerical simulations, we show that correlation among phenotypes can enhance the statistical power except for LPK-GEI, which simply combines statistics from single-phenotype GEI tests and ignores the phenotypic correlations. Among almost all considered scenarios, Het-GEI and PPK-GEI are more powerful than Hom-GEI and LPK-GEI. We apply Het-GEI and PPK-GEI in the genome-wide GEI analysis of systolic blood pressure (SBP) and diastolic blood pressure (DBP) in the UK Biobank. We analyze 18,101 genes and find that LEUTX is associated with SBP and DBP (p = 2.20×10-6) through its interaction with hemoglobin. The single-phenotype GEI test and our multiphenotype GEI tests Het-GEI and PPK-GEI are also used to evaluate the gene-hemoglobin interactions for 22 genes that were previously reported to be associated with SBP or DBP in a meta-analysis of genetic main effects. MYO1C shows nominal significance (p < 0.05) by the Het-GEI test. NOS3 shows nominal significance in DBP and MYO1C in both SBP and DBP by the single-phenotype GEI test.
Collapse
|
29
|
Zhou X, Lee SH. An integrative analysis of genomic and exposomic data for complex traits and phenotypic prediction. Sci Rep 2021; 11:21495. [PMID: 34728654 PMCID: PMC8564528 DOI: 10.1038/s41598-021-00427-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Accepted: 10/12/2021] [Indexed: 12/18/2022] Open
Abstract
Complementary to the genome, the concept of exposome has been proposed to capture the totality of human environmental exposures. While there has been some recent progress on the construction of the exposome, few tools exist that can integrate the genome and exposome for complex trait analyses. Here we propose a linear mixed model approach to bridge this gap, which jointly models the random effects of the two omics layers on phenotypes of complex traits. We illustrate our approach using traits from the UK Biobank (e.g., BMI and height for N ~ 35,000) with a small fraction of the exposome that comprises 28 lifestyle factors. The joint model of the genome and exposome explains substantially more phenotypic variance and significantly improves phenotypic prediction accuracy, compared to the model based on the genome alone. The additional phenotypic variance captured by the exposome includes its additive effects as well as non-additive effects such as genome-exposome (gxe) and exposome-exposome (exe) interactions. For example, 19% of variation in BMI is explained by additive effects of the genome, while additional 7.2% by additive effects of the exposome, 1.9% by exe interactions and 4.5% by gxe interactions. Correspondingly, the prediction accuracy for BMI, computed using Pearson's correlation between the observed and predicted phenotypes, improves from 0.15 (based on the genome alone) to 0.35 (based on the genome and exposome). We also show, using established theories, that integrating genomic and exposomic data can be an effective way of attaining a clinically meaningful level of prediction accuracy for disease traits. In conclusion, the genomic and exposomic effects can contribute to phenotypic variation via their latent relationships, i.e. genome-exposome correlation, and gxe and exe interactions, and modelling these effects has a potential to improve phenotypic prediction accuracy and thus holds a great promise for future clinical practice.
Collapse
Affiliation(s)
- Xuan Zhou
- Australian Centre for Precision Health, University of South Australia, Adelaide, SA, 5000, Australia
- UniSA Allied Health and Human Performance, University of South Australia, Adelaide, SA, 5000, Australia
- South Australian Health and Medical Research Institute, Adelaide, SA, 5000, Australia
| | - S Hong Lee
- Australian Centre for Precision Health, University of South Australia, Adelaide, SA, 5000, Australia.
- UniSA Allied Health and Human Performance, University of South Australia, Adelaide, SA, 5000, Australia.
- South Australian Health and Medical Research Institute, Adelaide, SA, 5000, Australia.
| |
Collapse
|
30
|
Adjangba C, Border R, Romero Villela PN, Ehringer MA, Evans LM. Little Evidence of Modified Genetic Effect of rs16969968 on Heavy Smoking Based on Age of Onset of Smoking. Nicotine Tob Res 2021; 23:1055-1063. [PMID: 33165565 DOI: 10.1093/ntr/ntaa229] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Accepted: 11/03/2020] [Indexed: 12/12/2022]
Abstract
INTRODUCTION Tobacco smoking is the leading cause of preventable death globally. Smoking quantity, measured in cigarettes per day, is influenced both by the age of onset of regular smoking (AOS) and by genetic factors, including a strong effect of the nonsynonymous single-nucleotide polymorphism rs16969968. A previous study by Hartz et al. reported an interaction between these two factors, whereby rs16969968 risk allele carriers who started smoking earlier showed increased risk for heavy smoking compared with those who started later. This finding has yet to be replicated in a large, independent sample. METHODS We performed a preregistered, direct replication attempt of the rs16969968 × AOS interaction on smoking quantity in 128 383 unrelated individuals from the UK Biobank, meta-analyzed across ancestry groups. We fit statistical association models mirroring the original publication as well as formal interaction tests on multiple phenotypic and analytical scales. RESULTS We replicated the main effects of rs16969968 and AOS on cigarettes per day but failed to replicate the interaction using previous methods. Nominal significance of the rs16969968 × AOS interaction term depended strongly on the scale of analysis and the particular phenotype, as did associations stratified by early/late AOS. No interaction tests passed genome-wide correction (α = 5e-8), and all estimated interaction effect sizes were much smaller in magnitude than previous estimates. CONCLUSIONS We failed to replicate the strong rs16969968 × AOS interaction effect previously reported. If such gene-moderator interactions influence complex traits, they likely depend on scale of measurement, and current biobanks lack the power to detect significant genome-wide associations given the minute effect sizes expected. IMPLICATIONS We failed to replicate the strong rs16969968 × AOS interaction effect on smoking quantity previously reported. If such gene-moderator interactions influence complex traits, current biobanks lack the power to detect significant genome-wide associations given the minute effect sizes expected. Furthermore, many potential interaction effects are likely to depend on the scale of measurement employed.
Collapse
Affiliation(s)
- Christine Adjangba
- Institute for Behavioral Genetics, University of Colorado-Boulder, Boulder, CO
| | - Richard Border
- Institute for Behavioral Genetics, University of Colorado-Boulder, Boulder, CO.,Department of Applied Mathematics, University of Colorado-Boulder, Boulder, CO
| | - Pamela N Romero Villela
- Institute for Behavioral Genetics, University of Colorado-Boulder, Boulder, CO.,Department of Psychology and Neuroscience, University of Colorado-Boulder, Boulder, CO
| | - Marissa A Ehringer
- Institute for Behavioral Genetics, University of Colorado-Boulder, Boulder, CO.,Department of Integrative Physiology, University of Colorado-Boulder, Boulder, CO
| | - Luke M Evans
- Institute for Behavioral Genetics, University of Colorado-Boulder, Boulder, CO.,Department of Ecology and Evolutionary Biology, University of Colorado-Boulder, Boulder, CO
| |
Collapse
|
31
|
Abstract
Disease classification, or nosology, was historically driven by careful examination of clinical features of patients. As technologies to measure and understand human phenotypes advanced, so too did classifications of disease, and the advent of genetic data has led to a surge in genetic subtyping in the past decades. Although the fundamental process of refining disease definitions and subtypes is shared across diverse fields, each field is driven by its own goals and technological expertise, leading to inconsistent and conflicting definitions of disease subtypes. Here, we review several classical and recent subtypes and subtyping approaches and provide concrete definitions to delineate subtypes. In particular, we focus on subtypes with distinct causal disease biology, which are of primary interest to scientists, and subtypes with pragmatic medical benefits, which are of primary interest to physicians. We propose genetic heterogeneity as a gold standard for establishing biologically distinct subtypes of complex polygenic disease. We focus especially on methods to find and validate genetic subtypes, emphasizing common pitfalls and how to avoid them.
Collapse
Affiliation(s)
- Andy Dahl
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, Illinois 60637, USA; .,Department of Neurology, University of California, Los Angeles, California 90024, USA; .,Department of Computational Medicine, University of California, Los Angeles, California 90095, USA
| | - Noah Zaitlen
- Department of Neurology, University of California, Los Angeles, California 90024, USA; .,Department of Computational Medicine, University of California, Los Angeles, California 90095, USA
| |
Collapse
|
32
|
Lin M, Park DS, Zaitlen NA, Henn BM, Gignoux CR. Admixed Populations Improve Power for Variant Discovery and Portability in Genome-Wide Association Studies. Front Genet 2021; 12:673167. [PMID: 34108994 PMCID: PMC8181458 DOI: 10.3389/fgene.2021.673167] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Accepted: 04/27/2021] [Indexed: 11/13/2022] Open
Abstract
Genome-wide association studies (GWAS) are primarily conducted in single-ancestry settings. The low transferability of results has limited our understanding of human genetic architecture across a range of complex traits. In contrast to homogeneous populations, admixed populations provide an opportunity to capture genetic architecture contributed from multiple source populations and thus improve statistical power. Here, we provide a mechanistic simulation framework to investigate the statistical power and transferability of GWAS under directional polygenic selection or varying divergence. We focus on a two-way admixed population and show that GWAS in admixed populations can be enriched for power in discovery by up to 2-fold compared to the ancestral populations under similar sample size. Moreover, higher accuracy of cross-population polygenic score estimates is also observed if variants and weights are trained in the admixed group rather than in the ancestral groups. Common variant associations are also more likely to replicate if first discovered in the admixed group and then transferred to an ancestral population, than the other way around (across 50 iterations with 1,000 causal SNPs, training on 10,000 individuals, testing on 1,000 in each population, p = 3.78e-6, 6.19e-101, ∼0 for FST = 0.2, 0.5, 0.8, respectively). While some of these FST values may appear extreme, we demonstrate that they are found across the entire phenome in the GWAS catalog. This framework demonstrates that investigation of admixed populations harbors significant advantages over GWAS in single-ancestry cohorts for uncovering the genetic architecture of traits and will improve downstream applications such as personalized medicine across diverse populations.
Collapse
Affiliation(s)
- Meng Lin
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - Danny S Park
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, San Francisco, CA, United States
| | - Noah A Zaitlen
- Department of Neurology and Computational Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Brenna M Henn
- Department of Anthropology, Center for Population Biology and the Genome Center, University of California, Davis, Davis, CA, United States
| | - Christopher R Gignoux
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| |
Collapse
|
33
|
Yamamoto E, Matsunaga H. Exploring Efficient Linear Mixed Models to Detect Quantitative Trait Locus-by-Environment Interactions. G3-GENES GENOMES GENETICS 2021; 11:6237888. [PMID: 33871575 PMCID: PMC8496289 DOI: 10.1093/g3journal/jkab119] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 04/06/2021] [Indexed: 11/29/2022]
Abstract
Genotype-by-environment (G × E) interactions are important for understanding genotype–phenotype relationships. To date, various statistical models have been proposed to account for G × E effects, especially in genomic selection (GS) studies. Generally, GS does not focus on the detection of each quantitative trait locus (QTL), while the genome-wide association study (GWAS) was designed for QTL detection. G × E modeling methods in GS can be included as covariates in GWAS using unified linear mixed models (LMMs). However, the efficacy of G × E modeling methods in GS studies has not been evaluated for GWAS. In this study, we performed a comprehensive comparison of LMMs that integrate the G × E modeling methods to detect both QTL and QTL-by-environment (Q × E) interaction effects. Model efficacy was evaluated using simulation experiments. For the fixed effect terms representing Q × E effects, simultaneous scoring of specific and nonspecific environmental effects was recommended because of the higher recall and improved genomic inflation factor value. For random effects, it was necessary to account for both G × E and genotype-by-trial (G × T) effects to control genomic inflation factor value. Thus, the recommended LMM includes fixed QTL effect terms that simultaneously score specific and nonspecific environmental effects and random effects accounting for both G × E and G × T. The LMM was applied to real tomato phenotype data obtained from two different cropping seasons. We detected not only QTLs with persistent effects across the cropping seasons but also QTLs with Q × E effects. The optimal LMM identified in this study successfully detected more QTLs with Q × E effects.
Collapse
Affiliation(s)
- Eiji Yamamoto
- Graduate School of Agriculture, Meiji University, Kawasaki 214-8571, Japan.,PRESTO, Japan Science and Technology Agency, Kawaguchi 332-0012, Japan
| | - Hiroshi Matsunaga
- Institute of Vegetable and Floriculture Science, National Agriculture and Food Research Organization, Ano, Tsu 514-2392, Japan
| |
Collapse
|
34
|
Sheppard B, Rappoport N, Loh PR, Sanders SJ, Zaitlen N, Dahl A. A model and test for coordinated polygenic epistasis in complex traits. Proc Natl Acad Sci U S A 2021; 118:e1922305118. [PMID: 33833052 PMCID: PMC8053945 DOI: 10.1073/pnas.1922305118] [Citation(s) in RCA: 5] [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] [Indexed: 12/11/2022] Open
Abstract
Interactions between genetic variants-epistasis-is pervasive in model systems and can profoundly impact evolutionary adaption, population disease dynamics, genetic mapping, and precision medicine efforts. In this work, we develop a model for structured polygenic epistasis, called coordinated epistasis (CE), and prove that several recent theories of genetic architecture fall under the formal umbrella of CE. Unlike standard epistasis models that assume epistasis and main effects are independent, CE captures systematic correlations between epistasis and main effects that result from pathway-level epistasis, on balance skewing the penetrance of genetic effects. To test for the existence of CE, we propose the even-odd (EO) test and prove it is calibrated in a range of realistic biological models. Applying the EO test in the UK Biobank, we find evidence of CE in 18 of 26 traits spanning disease, anthropometric, and blood categories. Finally, we extend the EO test to tissue-specific enrichment and identify several plausible tissue-trait pairs. Overall, CE is a dimension of genetic architecture that can capture structured, systemic forms of epistasis in complex human traits.
Collapse
Affiliation(s)
- Brooke Sheppard
- Department of Psychiatry and Behavioral Sciences, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94143
| | - Nadav Rappoport
- Department of Psychiatry and Behavioral Sciences, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94143
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA 94143
| | - Po-Ru Loh
- Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115
| | - Stephan J Sanders
- Department of Psychiatry and Behavioral Sciences, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94143
| | - Noah Zaitlen
- Department of Psychiatry and Behavioral Sciences, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94143;
- Department of Neurology, University of California Los Angeles, Los Angeles, CA 90095
- Department of Computational Medicine, University of California Los Angeles, Los Angeles, CA 90095
| | - Andy Dahl
- Department of Neurology, University of California Los Angeles, Los Angeles, CA 90095;
- Department of Computational Medicine, University of California Los Angeles, Los Angeles, CA 90095
- Section of Genetic Medicine, University of Chicago, Chicago, IL 60637
| |
Collapse
|
35
|
Lyall K, Ames JL, Pearl M, Traglia M, Weiss LA, Windham GC, Kharrazi M, Yoshida CK, Yolken R, Volk HE, Ashwood P, Van de Water J, Croen LA. A profile and review of findings from the Early Markers for Autism study: unique contributions from a population-based case-control study in California. Mol Autism 2021; 12:24. [PMID: 33736683 PMCID: PMC7977191 DOI: 10.1186/s13229-021-00429-7] [Citation(s) in RCA: 5] [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: 11/30/2020] [Accepted: 02/23/2021] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND The Early Markers for Autism (EMA) study is a population-based case-control study designed to learn more about early biologic processes involved in ASD. METHODS Participants were drawn from Southern California births from 2000 to 2003 with archived prenatal and neonatal screening specimens. Across two phases, children with ASD (n = 629) and intellectual disability without ASD (ID, n = 230) were ascertained from the California Department of Developmental Services (DDS), with diagnoses confirmed according to DSM-IV-TR criteria based on expert clinical review of abstracted records. General population controls (GP, n = 599) were randomly sampled from birth certificate files and matched to ASD cases by sex, birth month and year after excluding individuals with DDS records. EMA has published over 20 papers examining immune markers, endogenous hormones, environmental chemicals, and genetic factors in association with ASD and ID. This review summarizes the results across these studies, as well as the EMA study design and future directions. RESULTS EMA enabled several key contributions to the literature, including the examination of biomarker levels in biospecimens prospectively collected during critical windows of neurodevelopment. Key findings from EMA include demonstration of elevated cytokine and chemokine levels in maternal mid-pregnancy serum samples in association with ASD, as well as aberrations in other immune marker levels; suggestions of increased odds of ASD with prenatal exposure to certain endocrine disrupting chemicals, though not in mixture analyses; and demonstration of maternal and fetal genetic influence on prenatal chemical, and maternal and neonatal immune marker and vitamin D levels. We also observed an overall lack of association with ASD and measured maternal and neonatal vitamin D, mercury, and brain-derived neurotrophic factor (BDNF) levels. LIMITATIONS Covariate and outcome data were limited to information in Vital Statistics and DDS records. As a study based in Southern California, generalizability for certain environmental exposures may be reduced. CONCLUSIONS Results across EMA studies support the importance of the prenatal and neonatal periods in ASD etiology, and provide evidence for the role of the maternal immune response during pregnancy. Future directions for EMA, and the field of ASD in general, include interrogation of mechanistic pathways and examination of combined effects of exposures.
Collapse
Affiliation(s)
- Kristen Lyall
- A.J. Drexel Autism Institute, Drexel University, Suite 560, 3020 Market St, Philadelphia, PA, 19104, USA.
| | - Jennifer L Ames
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Michelle Pearl
- Environmental Health Investigations Branch, California Department of Public Health, Richmond, CA, USA
| | - Michela Traglia
- University of California, San Francisco, San Francisco, CA, USA
| | - Lauren A Weiss
- University of California, San Francisco, San Francisco, CA, USA
| | - Gayle C Windham
- Environmental Health Investigations Branch, California Department of Public Health, Richmond, CA, USA
| | - Martin Kharrazi
- Environmental Health Investigations Branch, California Department of Public Health, Richmond, CA, USA
| | - Cathleen K Yoshida
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Robert Yolken
- School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Heather E Volk
- Department of Mental Health, Johns Hopkins University, Baltimore, MD, USA
| | - Paul Ashwood
- UC Davis MIND Institute, University of California, Davis, Davis, CA, USA
| | - Judy Van de Water
- UC Davis MIND Institute, University of California, Davis, Davis, CA, USA
| | - Lisa A Croen
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| |
Collapse
|
36
|
Stern AJ, Speidel L, Zaitlen NA, Nielsen R. Disentangling selection on genetically correlated polygenic traits via whole-genome genealogies. Am J Hum Genet 2021; 108:219-239. [PMID: 33440170 PMCID: PMC7895848 DOI: 10.1016/j.ajhg.2020.12.005] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Accepted: 12/07/2020] [Indexed: 12/17/2022] Open
Abstract
We present a full-likelihood method to infer polygenic adaptation from DNA sequence variation and GWAS summary statistics to quantify recent transient directional selection acting on a complex trait. Through simulations of polygenic trait architecture evolution and GWASs, we show the method substantially improves power over current methods. We examine the robustness of the method under stratification, uncertainty and bias in marginal effects, uncertainty in the causal SNPs, allelic heterogeneity, negative selection, and low GWAS sample size. The method can quantify selection acting on correlated traits, controlling for pleiotropy even among traits with strong genetic correlation (|rg|=80%) while retaining high power to attribute selection to the causal trait. When the causal trait is excluded from analysis, selection is attributed to its closest proxy. We discuss limitations of the method, cautioning against strongly causal interpretations of the results, and the possibility of undetectable gene-by-environment (GxE) interactions. We apply the method to 56 human polygenic traits, revealing signals of directional selection on pigmentation, life history, glycated hemoglobin (HbA1c), and other traits. We also conduct joint testing of 137 pairs of genetically correlated traits, revealing widespread correlated response acting on these traits (2.6-fold enrichment, p = 1.5 × 10-7). Signs of selection on some traits previously reported as adaptive (e.g., educational attainment and hair color) are largely attributable to correlated response (p = 2.9 × 10-6 and 1.7 × 10-4, respectively). Lastly, our joint test shows antagonistic selection has increased type 2 diabetes risk and decrease HbA1c (p = 1.5 × 10-5).
Collapse
Affiliation(s)
- Aaron J Stern
- Graduate Group in Computational Biology, UC Berkeley, Berkeley, CA 94703, USA.
| | - Leo Speidel
- Department of Statistics, University of Oxford, Oxford, UK
| | - Noah A Zaitlen
- David Geffen School of Medicine, UC Los Angeles, Los Angeles, CA 90095, USA
| | - Rasmus Nielsen
- Department of Integrative Biology, UC Berkeley, Berkeley, CA 94703, USA; Department of Statistics, UC Berkeley, Berkeley, CA 94703, USA
| |
Collapse
|
37
|
Majumdar A, Burch KS, Haldar T, Sankararaman S, Pasaniuc B, Gauderman WJ, Witte JS. A two-step approach to testing overall effect of gene-environment interaction for multiple phenotypes. Bioinformatics 2021; 36:5640-5648. [PMID: 33453114 DOI: 10.1093/bioinformatics/btaa1083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Revised: 12/09/2020] [Accepted: 12/17/2020] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION While gene-environment (GxE) interactions contribute importantly to many different phenotypes, detecting such interactions requires well-powered studies and has proven difficult. To address this, we combine two approaches to improve GxE power: simultaneously evaluating multiple phenotypes and using a two-step analysis approach. Previous work shows that the power to identify a main genetic effect can be improved by simultaneously analyzing multiple related phenotypes. For a univariate phenotype, two-step methods produce higher power for detecting a GxE interaction compared to single step analysis. Therefore, we propose a two-step approach to test for an overall GxE effect for multiple phenotypes. RESULTS Using simulations we demonstrate that, when more than one phenotype has GxE effect (i.e., GxE pleiotropy), our approach offers substantial gain in power (18%-43%) to detect an aggregate-level GxE effect for a multivariate phenotype compared to an analogous two-step method to identify GxE effect for a univariate phenotype. We applied the proposed approach to simultaneously analyze three lipids, LDL, HDL and Triglyceride with the frequency of alcohol consumption as environmental factor in the UK Biobank. The method identified two loci with an overall GxE effect on the vector of lipids, one of which was missed by the competing approaches. AVAILABILITY We provide an R package MPGE implementing the proposed approach which is available from CRAN: https://cran.r-project.org/web/packages/MPGE/index.html.
Collapse
Affiliation(s)
- Arunabha Majumdar
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.,Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
| | - Kathryn S Burch
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, USA
| | | | - Sriram Sankararaman
- Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, USA
| | - Bogdan Pasaniuc
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.,Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, USA
| | - W James Gauderman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - John S Witte
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
| |
Collapse
|
38
|
Cai N, Choi KW, Fried EI. Reviewing the genetics of heterogeneity in depression: operationalizations, manifestations and etiologies. Hum Mol Genet 2020; 29:R10-R18. [PMID: 32568380 PMCID: PMC7530517 DOI: 10.1093/hmg/ddaa115] [Citation(s) in RCA: 74] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Revised: 06/05/2020] [Accepted: 06/08/2020] [Indexed: 02/06/2023] Open
Abstract
With progress in genome-wide association studies of depression, from identifying zero hits in ~16 000 individuals in 2013 to 223 hits in more than a million individuals in 2020, understanding the genetic architecture of this debilitating condition no longer appears to be an impossible task. The pressing question now is whether recently discovered variants describe the etiology of a single disease entity. There are a myriad of ways to measure and operationalize depression severity, and major depressive disorder as defined in the Diagnostic and Statistical Manual of Mental Disorders-5 can manifest in more than 10 000 ways based on symptom profiles alone. Variations in developmental timing, comorbidity and environmental contexts across individuals and samples further add to the heterogeneity. With big data increasingly enabling genomic discovery in psychiatry, it is more timely than ever to explicitly disentangle genetic contributions to what is likely 'depressions' rather than depression. Here, we introduce three sources of heterogeneity: operationalization, manifestation and etiology. We review recent efforts to identify depression subtypes using clinical and data-driven approaches, examine differences in genetic architecture of depression across contexts, and argue that heterogeneity in operationalizations of depression is likely a considerable source of inconsistency. Finally, we offer recommendations and considerations for the field going forward.
Collapse
Affiliation(s)
- Na Cai
- Helmholtz Pioneer Campus, Helmholtz Zentrum München, Neuherberg 85764, Germany
| | - Karmel W Choi
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA 02114, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research, Broad Institute, Boston, MA 02142, USA
| | - Eiko I Fried
- Department of Psychology, Leiden University, Leiden 2333 AK, Netherlands
| |
Collapse
|
39
|
Awany D, Chimusa ER. Heritability jointly explained by host genotype and microbiome: will improve traits prediction? Brief Bioinform 2020; 22:5893981. [PMID: 32810866 DOI: 10.1093/bib/bbaa175] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 07/09/2020] [Accepted: 07/10/2020] [Indexed: 11/14/2022] Open
Abstract
As we observe the $70$th anniversary of the publication by Robertson that formalized the notion of 'heritability', geneticists remain puzzled by the problem of missing/hidden heritability, where heritability estimates from genome-wide association studies (GWASs) fall short of that from twin-based studies. Many possible explanations have been offered for this discrepancy, including existence of genetic variants poorly captured by existing arrays, dominance, epistasis and unaccounted-for environmental factors; albeit these remain controversial. We believe a substantial part of this problem could be solved or better understood by incorporating the host's microbiota information in the GWAS model for heritability estimation and may also increase human traits prediction for clinical utility. This is because, despite empirical observations such as (i) the intimate role of the microbiome in many complex human phenotypes, (ii) the overlap between genetic variants associated with both microbiome attributes and complex diseases and (iii) the existence of heritable bacterial taxa, current GWAS models for heritability estimate do not take into account the contributory role of the microbiome. Furthermore, heritability estimate from twin-based studies does not discern microbiome component of the observed total phenotypic variance. Here, we summarize the concept of heritability in GWAS and microbiome-wide association studies, focusing on its estimation, from a statistical genetics perspective. We then discuss a possible statistical method to incorporate the microbiome in the estimation of heritability in host GWAS.
Collapse
Affiliation(s)
- Denis Awany
- Division of Human Genetics, Department of Pathology, University of Cape Town, Cape Town, South Africa
| | - Emile R Chimusa
- Computational Biology Division, Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| |
Collapse
|
40
|
Zhou X, van der Werf J, Carson-Chahhoud K, Ni G, McGrath J, Hyppönen E, Lee SH. Whole-Genome Approach Discovers Novel Genetic and Nongenetic Variance Components Modulated by Lifestyle for Cardiovascular Health. J Am Heart Assoc 2020; 9:e015661. [PMID: 32308100 PMCID: PMC7428517 DOI: 10.1161/jaha.119.015661] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Background Both genetic and nongenetic factors can predispose individuals to cardiovascular risk. Finding ways to alter these predispositions is important for cardiovascular disease prevention. Methods and Results We used a novel whole‐genome approach to estimate the genetic and nongenetic effects on—and hence their predispositions to—cardiovascular risk and determined whether they vary with respect to lifestyle factors such as physical activity, smoking, alcohol consumption, and dietary intake. We performed analyses on the ARIC (Atherosclerosis Risk in Communities) Study (N=6896–7180) and validated findings using the UKBB (UK Biobank, N=14 076–34 538). Lifestyle modulation was evident for many cardiovascular traits such as body mass index and resting heart rate. For example, alcohol consumption modulated both genetic and nongenetic effects on body mass index, whereas smoking modulated nongenetic effects on heart rate, pulse pressure, and white blood cell count. We also stratified individuals according to estimated genetic and nongenetic effects that are modulated by lifestyle factors and showed distinct phenotype–lifestyle relationships across the stratified groups. Finally, we showed that neglecting lifestyle modulations of cardiovascular traits would on average reduce single nucleotide polymorphism heritability estimates of these traits by a small yet significant amount, primarily owing to the overestimation of residual variance. Conclusions Lifestyle changes are relevant to cardiovascular disease prevention. Individual differences in the genetic and nongenetic effects that are modulated by lifestyle factors, as shown by the stratified group analyses, implies a need for personalized lifestyle interventions. In addition, single nucleotide polymorphism–based heritability of cardiovascular traits without accounting for lifestyle modulations could be underestimated.
Collapse
Affiliation(s)
- Xuan Zhou
- Australian Centre for Precision Health University of South Australia Adelaide South Australia Australia.,South Australian Health and Medical Research Institute Adelaide South Australia Australia
| | - Julius van der Werf
- School of Environmental and Rural Science University of New England Armidale New South Wales Australia
| | - Kristin Carson-Chahhoud
- Australian Centre for Precision Health University of South Australia Adelaide South Australia Australia
| | - Guiyan Ni
- School of Environmental and Rural Science University of New England Armidale New South Wales Australia.,Institute for Molecular Bioscience University of Queensland Brisbane Queensland Australia
| | - John McGrath
- Queensland Brain Institute University of Queensland Brisbane Queensland Australia.,Queensland Centre for Mental Health Research The Park Centre for Mental Health Wacol Queensland Australia
| | - Elina Hyppönen
- Australian Centre for Precision Health University of South Australia Adelaide South Australia Australia.,South Australian Health and Medical Research Institute Adelaide South Australia Australia
| | - S Hong Lee
- Australian Centre for Precision Health University of South Australia Adelaide South Australia Australia.,South Australian Health and Medical Research Institute Adelaide South Australia Australia
| |
Collapse
|