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Boye C, Nirmalan S, Ranjbaran A, Luca F. Genotype × environment interactions in gene regulation and complex traits. Nat Genet 2024; 56:1057-1068. [PMID: 38858456 DOI: 10.1038/s41588-024-01776-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 04/25/2024] [Indexed: 06/12/2024]
Abstract
Genotype × environment interactions (GxE) have long been recognized as a key mechanism underlying human phenotypic variation. Technological developments over the past 15 years have dramatically expanded our appreciation of the role of GxE in both gene regulation and complex traits. The richness and complexity of these datasets also required parallel efforts to develop robust and sensitive statistical and computational approaches. Although our understanding of the genetic architecture of molecular and complex traits has been maturing, a large proportion of complex trait heritability remains unexplained. Furthermore, there are increasing efforts to characterize the effect of environmental exposure on human health. We therefore review GxE in human gene regulation and complex traits, advocating for a comprehensive approach that jointly considers genetic and environmental factors in human health and disease.
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Affiliation(s)
- Carly Boye
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI, US
| | - Shreya Nirmalan
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI, US
| | - Ali Ranjbaran
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI, US
| | - Francesca Luca
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI, US.
- Department of Obstetrics and Gynecology, Wayne State University, Detroit, MI, US.
- Department of Biology, University of Rome "Tor Vergata", Rome, Italy.
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2
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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.
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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.
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3
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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.
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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
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Jayasinghe D, Momin MM, Beckmann K, Hyppönen E, Benyamin B, Lee SH. Mitigating type 1 error inflation and power loss in GxE PRS: Genotype-environment interaction in polygenic risk score models. Genet Epidemiol 2024; 48:85-100. [PMID: 38303123 DOI: 10.1002/gepi.22546] [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: 07/25/2023] [Revised: 01/03/2024] [Accepted: 01/08/2024] [Indexed: 02/03/2024]
Abstract
The use of polygenic risk score (PRS) models has transformed the field of genetics by enabling the prediction of complex traits and diseases based on an individual's genetic profile. However, the impact of genotype-environment interaction (GxE) on the performance and applicability of PRS models remains a crucial aspect to be explored. Currently, existing genotype-environment interaction polygenic risk score (GxE PRS) models are often inappropriately used, which can result in inflated type 1 error rates and compromised results. In this study, we propose novel GxE PRS models that jointly incorporate additive and interaction genetic effects although also including an additional quadratic term for nongenetic covariates, enhancing their robustness against model misspecification. Through extensive simulations, we demonstrate that our proposed models outperform existing models in terms of controlling type 1 error rates and enhancing statistical power. Furthermore, we apply the proposed models to real data, and report significant GxE effects. Specifically, we highlight the impact of our models on both quantitative and binary traits. For quantitative traits, we uncover the GxE modulation of genetic effects on body mass index by alcohol intake frequency. In the case of binary traits, we identify the GxE modulation of genetic effects on hypertension by waist-to-hip ratio. These findings underscore the importance of employing a robust model that effectively controls type 1 error rates, thus preventing the occurrence of spurious GxE signals. To facilitate the implementation of our approach, we have developed an innovative R software package called GxEprs, specifically designed to detect and estimate GxE effects. Overall, our study highlights the importance of accurate GxE modeling and its implications for genetic risk prediction, although providing a practical tool to support further research in this area.
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Affiliation(s)
- Dovini Jayasinghe
- Australian Centre for Precision Health, University of South Australia, Adelaide, South Australia, Australia
- UniSA Allied Health and Human Performance, University of South Australia, Adelaide, South Australia, Australia
- South Australian Health and Medical Research Institute (SAHMRI), University of South Australia, Adelaide, South Australia, Australia
| | - Md Moksedul Momin
- Australian Centre for Precision Health, University of South Australia, Adelaide, South Australia, Australia
- UniSA Allied Health and Human Performance, University of South Australia, Adelaide, South Australia, Australia
- South Australian Health and Medical Research Institute (SAHMRI), University of South Australia, Adelaide, South Australia, Australia
- Department of Genetics and Animal Breeding, Faculty of Veterinary Medicine, Chattogram Veterinary and Animal Sciences University (CVASU), Khulshi, Chattogram, Bangladesh
| | - Kerri Beckmann
- UniSA Allied Health and Human Performance, University of South Australia, Adelaide, South Australia, Australia
| | - Elina Hyppönen
- Australian Centre for Precision Health, University of South Australia, Adelaide, South Australia, Australia
- South Australian Health and Medical Research Institute (SAHMRI), University of South Australia, Adelaide, South Australia, Australia
- UniSA Clinical and Health Sciences, University of South Australia, Adelaide, South Australia, Australia
| | - Beben Benyamin
- Australian Centre for Precision Health, University of South Australia, Adelaide, South Australia, Australia
- UniSA Allied Health and Human Performance, University of South Australia, Adelaide, South Australia, Australia
- South Australian Health and Medical Research Institute (SAHMRI), University of South Australia, Adelaide, South Australia, Australia
| | - S Hong Lee
- Australian Centre for Precision Health, University of South Australia, Adelaide, South Australia, Australia
- UniSA Allied Health and Human Performance, University of South Australia, Adelaide, South Australia, Australia
- South Australian Health and Medical Research Institute (SAHMRI), University of South Australia, Adelaide, South Australia, Australia
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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.
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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
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Pham DT, Westerman KE, Pan C, Chen L, Srinivasan S, Isganaitis E, Vajravelu ME, Bacha F, Chernausek S, Gubitosi-Klug R, Divers J, Pihoker C, Marcovina SM, Manning AK, Chen H. Re-analysis and meta-analysis of summary statistics from gene-environment interaction studies. Bioinformatics 2023; 39:btad730. [PMID: 38039147 PMCID: PMC10724851 DOI: 10.1093/bioinformatics/btad730] [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: 08/16/2023] [Revised: 10/26/2023] [Accepted: 11/30/2023] [Indexed: 12/03/2023] Open
Abstract
MOTIVATION statistics from genome-wide association studies enable many valuable downstream analyses that are more efficient than individual-level data analysis while also reducing privacy concerns. As growing sample sizes enable better-powered analysis of gene-environment interactions, there is a need for gene-environment interaction-specific methods that manipulate and use summary statistics. RESULTS We introduce two tools to facilitate such analysis, with a focus on statistical models containing multiple gene-exposure and/or gene-covariate interaction terms. REGEM (RE-analysis of GEM summary statistics) uses summary statistics from a single, multi-exposure genome-wide interaction study to derive analogous sets of summary statistics with arbitrary sets of exposures and interaction covariate adjustments. METAGEM (META-analysis of GEM summary statistics) extends current fixed-effects meta-analysis models to incorporate multiple exposures from multiple studies. We demonstrate the value and efficiency of these tools by exploring alternative methods of accounting for ancestry-related population stratification in genome-wide interaction study in the UK Biobank as well as by conducting a multi-exposure genome-wide interaction study meta-analysis in cohorts from the diabetes-focused ProDiGY consortium. These programs help to maximize the value of summary statistics from diverse and complex gene-environment interaction studies. AVAILABILITY AND IMPLEMENTATION REGEM and METAGEM are open-source projects freely available at https://github.com/large-scale-gxe-methods/REGEM and https://github.com/large-scale-gxe-methods/METAGEM.
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Affiliation(s)
- Duy T Pham
- Human Genetics Center, Department of Epidemiology, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, United States
| | - Kenneth E Westerman
- Department of Medicine, Clinical and Translational Epidemiology Unit, Mongan Institute, Massachusetts General Hospital, Boston, MA 02114, United States
- Metabolism Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, United States
- Department of Medicine, Harvard Medical School, Boston, MA 02115, United States
| | - Cong Pan
- Human Genetics Center, Department of Epidemiology, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, United States
| | - Ling Chen
- Department of Medicine, Clinical and Translational Epidemiology Unit, Mongan Institute, Massachusetts General Hospital, Boston, MA 02114, United States
- Metabolism Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, United States
| | - Shylaja Srinivasan
- Department of Pediatrics, University of California, San Francisco, CA 94158, United States
| | - Elvira Isganaitis
- Research Division, Joslin Diabetes Center, Boston, MA 02115, United States
| | - Mary Ellen Vajravelu
- Department of Pediatrics, University of Pittsburgh School of Medicine, Pittsburgh, PA 15224, United States
| | - Fida Bacha
- Department of Pediatrics, Baylor College of Medicine, Houston, TX 77030, United States
| | - Steve Chernausek
- Department of Pediatrics, The University of Oklahoma College of Medicine, Oklahoma City, OK 73117, United States
| | - Rose Gubitosi-Klug
- Department of Pediatrics, Case Western Reserve University, Cleveland, OH 44106, United States
| | - Jasmin Divers
- Department of Foundations of Medicine, New York University, New York, NY 10016, United States
| | - Catherine Pihoker
- Department of Pediatrics, University of Washington School of Medicine, Seattle, WA 98105, United States
| | - Santica M Marcovina
- Northwest Lipid Metabolism and Diabetes Research Laboratories, Department of Medicine, University of Washington, Seattle, WA 98105, United States
| | - Alisa K Manning
- Department of Medicine, Clinical and Translational Epidemiology Unit, Mongan Institute, Massachusetts General Hospital, Boston, MA 02114, United States
- Metabolism Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, United States
- Department of Medicine, Harvard Medical School, Boston, MA 02115, United States
| | - Han Chen
- Human Genetics Center, Department of Epidemiology, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, United States
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Zhang C, Shestopaloff K, Hollis B, Kwok CH, Hon C, Hartmann N, Tian C, Wozniak M, Santos L, West D, Gardiner S, Mallon AM, Readie A, Martin R, Nichols T, Beste MT, Zierer J, Ferrero E, Vandemeulebroecke M, Jostins-Dean L. Response to anti-IL17 therapy in inflammatory disease is not strongly impacted by genetic background. Am J Hum Genet 2023; 110:1817-1824. [PMID: 37659414 PMCID: PMC10577077 DOI: 10.1016/j.ajhg.2023.08.010] [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: 03/23/2023] [Revised: 08/15/2023] [Accepted: 08/15/2023] [Indexed: 09/04/2023] Open
Abstract
Response to the anti-IL17 monoclonal antibody secukinumab is heterogeneous, and not all participants respond to treatment. Understanding whether this heterogeneity is driven by genetic variation is a key aim of pharmacogenetics and could influence precision medicine approaches in inflammatory diseases. Using changes in disease activity scores across 5,218 genotyped individuals from 19 clinical trials across four indications (psoriatic arthritis, psoriasis, ankylosing spondylitis, and rheumatoid arthritis), we tested whether genetics predicted response to secukinumab. We did not find any evidence of association between treatment response and common variants, imputed HLA alleles, polygenic risk scores of disease susceptibility, or cross-disease components of shared genetic risk. This suggests that anti-IL17 therapy is equally effective regardless of an individual's genetic background, a finding that has important implications for future genetic studies of biological therapy response in inflammatory diseases.
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Affiliation(s)
- Cong Zhang
- China Novartis Institutes for Bio-medical Research CO., Shanghai, China
| | - Konstantin Shestopaloff
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK; Department of Statistics, University of Oxford, Oxford, UK
| | - Benjamin Hollis
- Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK
| | - Chun Hei Kwok
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Claudia Hon
- Novartis Institutes for BioMedical Research, 220 Massachusetts Avenue, Cambridge, MA 02139, USA
| | | | - Chengeng Tian
- China Novartis Institutes for Bio-medical Research CO., Shanghai, China
| | | | | | - Dominique West
- Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Stephen Gardiner
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | | | - Aimee Readie
- Novartis Pharmaceuticals Corporation, East Hanover, NJ, USA
| | - Ruvie Martin
- Novartis Pharmaceuticals Corporation, East Hanover, NJ, USA
| | - Thomas Nichols
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Michael T Beste
- Novartis Institutes for BioMedical Research, 220 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Jonas Zierer
- Novartis Institutes for BioMedical Research, Basel, CH, Switzerland
| | - Enrico Ferrero
- Novartis Institutes for BioMedical Research, Basel, CH, Switzerland
| | | | - Luke Jostins-Dean
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK; Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK.
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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.
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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.
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Kuznetsov IA, Tsepilov YA, Freidin MB, Williams FMK, Suri P, Aulchenko YS. Genotype-by-environment interactions in chronic back pain. Spine J 2023; 23:1108-1114. [PMID: 37080360 DOI: 10.1016/j.spinee.2023.04.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 04/06/2023] [Accepted: 04/11/2023] [Indexed: 04/22/2023]
Abstract
BACKGROUND CONTEXT Chronic back pain (CBP) is a common debilitating condition with substantial societal impact. While understanding genotype-by-environment (GxE) interactions may be crucial to achieving the goals of personalized medicine, there are few large-scale studies investigating this topic for CBP. None of them systematically explore multiple CBP risk factors. PURPOSE To estimate the extent to which genetic effects on CBP are modified by known demographic and clinical risk factors. RESEARCH DESIGN Case-control study, genome-wide GxE interaction study. PATIENT SAMPLE Data on up to 331,610 unrelated participants (57,881 CBP cases and 273,729 controls) from the UK Biobank cohort were used. UK Biobank is a prospective cohort with collected deep genetic and phenotypic data on approximately 500,000 individuals across the UK. OUTCOME MEASURES Self-reported chronic back pain. METHODS We applied a whole-genome approach to estimate the proportion of phenotypic variance explained by interactions between genotype and 12 known risk factors. We also analyzed if effects of common single-nucleotide polymorphisms on CBP are changed in presence of known risk factors. RESULTS The results indicate a modest, if any, modification of genetic effects by examined risk factors in CBP. Our estimates suggest that detecting such weak effects would require a sample size of millions of individuals. CONCLUSIONS The GxE interactions with examined common risk factors for CBP are either weak or absent. Interactions of such magnitude are unlikely to have the potential to inform and influence treatment strategies. Risk estimation models may use common genetic variation and the considered risk factors as independent predictors, without accounting for GxE.
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Affiliation(s)
- Ivan A Kuznetsov
- Center of Life Sciences, Skolkovo Institute of Science and Technology, 30 bld.1 Bolshoy Boulevard, Moscow 121205, Russia
| | - Yakov A Tsepilov
- Laboratory of Recombination and Segregation Analysis, Institute of Cytology and Genetics, 10 Lavrentiev Ave, Novosibirsk, 630090, Russia; Laboratory of Theoretical and Applied Functional Genomics, Novosibirsk State University, 1 Pirogova St, Novosibirsk, 630090, Russia; Kurchatov genomics center of the Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 10 Lavrentiev Ave, Novosibirsk, 630090, Russia
| | - Maxim B Freidin
- Department of Biology, School of Biological and Behavioural Sciences, Queen Mary University of London, Mile End Rd, Bethnal Green, London E1 4DQ, UK
| | - Frances M K Williams
- Department of Twin Research and Genetic Epidemiology, King's College London, Westminster Bridge Rd, London SE1 7EH, UK
| | - Pradeep Suri
- Seattle Epidemiologic Research and Information Center, VA Puget Sound Health Care System, 1660 S. Columbian Way, Seattle, WA 98108, USA; Division of Rehabilitation Care Services, 1660 S. Columbian Way, Seattle, WA 98108, USA; Clinical Learning, Evidence, and Research Center, University of Washington, 325 Ninth AvBox 359612, Seattle, WA 98104, USA
| | - Yurii S Aulchenko
- Laboratory of Recombination and Segregation Analysis, Institute of Cytology and Genetics, 10 Lavrentiev Ave, Novosibirsk, 630090, Russia; PolyOmica, Het Vlaggeschip 61, 's-Hertogenbosch, PA 5237, The Netherlands.
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10
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San-Cristobal R, de Toro-Martín J, Vohl MC. Heuristic Approach Uncovering Biological Significance of Gene-Lifestyle Interactions in Cardiometabolic Traits. Lifestyle Genom 2023; 16:106-112. [PMID: 37339607 DOI: 10.1159/000531181] [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: 03/01/2023] [Accepted: 05/10/2023] [Indexed: 06/22/2023] Open
Abstract
BACKGROUND Gene-lifestyle interaction studies using genome-wide association studies (GWAS) data contribute to a better understanding of individual responses to environmental exposures. OBJECTIVES Herein, we aimed at assessing the biological significance of overlapping genes reported in gene-lifestyle interaction studies in cardiometabolic health. METHOD A heuristic analysis of genes reporting significant interactions related to cardiometabolic traits was performed to determine the biological pathways common to the different traits. RESULTS A total of 873 genes were analyzed. Fine and condensed phenotypic solutions were obtained from overlapping genes common to more than one trait. CONCLUSIONS This study revealed significant metabolic pathways associated with the impact of gene-environment interactions on cardiometabolic risk. Graphical Abstract: Publicly available data in cloud-based repositories were used to perform enrichment analyses of genes previously described in GWAS studies that showed interaction with lifestyles. From the enriched pathways, cluster analysis was performed to group enriched metabolic disorders.
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Affiliation(s)
- Rodrigo San-Cristobal
- Centre Nutrition, Santé et société (NUTRISS), Institut sur la Nutrition et les Aliments Fonctionnels (INAF), Université Laval, Québec, Québec, Canada
- School of Nutrition, Université Laval, Québec, Québec, Canada
| | - Juan de Toro-Martín
- Centre Nutrition, Santé et société (NUTRISS), Institut sur la Nutrition et les Aliments Fonctionnels (INAF), Université Laval, Québec, Québec, Canada
- School of Nutrition, Université Laval, Québec, Québec, Canada
| | - Marie-Claude Vohl
- Centre Nutrition, Santé et société (NUTRISS), Institut sur la Nutrition et les Aliments Fonctionnels (INAF), Université Laval, Québec, Québec, Canada
- School of Nutrition, Université Laval, Québec, Québec, Canada
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11
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Khramtsova EA, Wilson MA, Martin J, Winham SJ, He KY, Davis LK, Stranger BE. Quality control and analytic best practices for testing genetic models of sex differences in large populations. Cell 2023; 186:2044-2061. [PMID: 37172561 PMCID: PMC10266536 DOI: 10.1016/j.cell.2023.04.014] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 01/31/2023] [Accepted: 04/07/2023] [Indexed: 05/15/2023]
Abstract
Phenotypic sex-based differences exist for many complex traits. In other cases, phenotypes may be similar, but underlying biology may vary. Thus, sex-aware genetic analyses are becoming increasingly important for understanding the mechanisms driving these differences. To this end, we provide a guide outlining the current best practices for testing various models of sex-dependent genetic effects in complex traits and disease conditions, noting that this is an evolving field. Insights from sex-aware analyses will not only teach us about the biology of complex traits but also aid in achieving the goals of precision medicine and health equity for all.
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Affiliation(s)
- Ekaterina A Khramtsova
- Population Analytics and Insights, Data Science Analytics & Insights, Janssen R&D, Lower Gwynedd Township, PA, USA.
| | - Melissa A Wilson
- School of Life Sciences, Center for Evolution and Medicine, Biodesign Center for Mechanisms of Evolution, Arizona State University, Tempe, AZ 85282, USA
| | - Joanna Martin
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Stacey J Winham
- Department of Quantitative Health Sciences, Division of Computational Biology, Mayo Clinic, Rochester, MN, USA
| | - Karen Y He
- Population Analytics and Insights, Data Science Analytics & Insights, Janssen R&D, Lower Gwynedd Township, PA, USA
| | - Lea K Davis
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Barbara E Stranger
- Center for Genetic Medicine, Department of Pharmacology, Northwestern University, Chicago, IL, USA.
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12
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Jung HU, Kim DJ, Baek EJ, Chung JY, Ha TW, Kim HK, Kang JO, Lim JE, Oh B. Gene-environment interaction explains a part of missing heritability in human body mass index. Commun Biol 2023; 6:324. [PMID: 36966243 PMCID: PMC10039928 DOI: 10.1038/s42003-023-04679-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 03/07/2023] [Indexed: 03/27/2023] Open
Abstract
Gene-environment (G×E) interaction could partially explain missing heritability in traits; however, the magnitudes of G×E interaction effects remain unclear. Here, we estimate the heritability of G×E interaction for body mass index (BMI) by subjecting genome-wide interaction study data of 331,282 participants in the UK Biobank to linkage disequilibrium score regression (LDSC) and linkage disequilibrium adjusted kinships-software for estimating SNP heritability from summary statistics (LDAK-SumHer) analyses. Among 14 obesity-related lifestyle factors, MET score, pack years of smoking, and alcohol intake frequency significantly interact with genetic factors in both analyses, accounting for the partial variance of BMI. The G×E interaction heritability (%) and standard error of these factors by LDSC and LDAK-SumHer are as follows: MET score, 0.45% (0.12) and 0.65% (0.24); pack years of smoking, 0.52% (0.13) and 0.93% (0.26); and alcohol intake frequency, 0.32% (0.10) and 0.80% (0.17), respectively. Moreover, these three factors are partially validated for their interactions with genetic factors in other obesity-related traits, including waist circumference, hip circumference, waist-to-hip ratio adjusted with BMI, and body fat percentage. Our results suggest that G×E interaction may partly explain the missing heritability in BMI, and two G×E interaction loci identified could help in understanding the genetic architecture of obesity.
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Affiliation(s)
- Hae-Un Jung
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, South Korea
| | - Dong Jun Kim
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, South Korea
| | - Eun Ju Baek
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, South Korea
| | - Ju Yeon Chung
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, South Korea
| | - Tae Woong Ha
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul, South Korea
| | - Han-Kyul Kim
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul, South Korea
| | - Ji-One Kang
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul, South Korea
| | - Ji Eun Lim
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul, South Korea.
| | - Bermseok Oh
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, South Korea.
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul, South Korea.
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13
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Page ML, Vance EL, Cloward ME, Ringger E, Dayton L, Ebbert MTW, Miller JB, Kauwe JSK. The Polygenic Risk Score Knowledge Base offers a centralized online repository for calculating and contextualizing polygenic risk scores. Commun Biol 2022; 5:899. [PMID: 36056235 PMCID: PMC9438378 DOI: 10.1038/s42003-022-03795-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 08/03/2022] [Indexed: 11/20/2022] Open
Abstract
The process of identifying suitable genome-wide association (GWA) studies and formatting the data to calculate multiple polygenic risk scores on a single genome can be laborious. Here, we present a centralized polygenic risk score calculator currently containing over 250,000 genetic variant associations from the NHGRI-EBI GWAS Catalog for users to easily calculate sample-specific polygenic risk scores with comparable results to other available tools. Polygenic risk scores are calculated either online through the Polygenic Risk Score Knowledge Base (PRSKB; https://prs.byu.edu ) or via a command-line interface. We report study-specific polygenic risk scores across the UK Biobank, 1000 Genomes, and the Alzheimer's Disease Neuroimaging Initiative (ADNI), contextualize computed scores, and identify potentially confounding genetic risk factors in ADNI. We introduce a streamlined analysis tool and web interface to calculate and contextualize polygenic risk scores across various studies, which we anticipate will facilitate a wider adaptation of polygenic risk scores in future disease research.
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Affiliation(s)
- Madeline L Page
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY, USA
| | - Elizabeth L Vance
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY, USA
| | | | - Ed Ringger
- Department of Biology, Brigham Young University, Provo, UT, USA
| | - Louisa Dayton
- Department of Biology, Brigham Young University, Provo, UT, USA
| | - Mark T W Ebbert
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY, USA.,Division of Biomedical Informatics, Department of Internal Medicine, University of Kentucky, Lexington, KY, USA.,Department of Neuroscience, University of Kentucky, Lexington, KY, USA
| | | | - Justin B Miller
- Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY, USA.,Division of Biomedical Informatics, Department of Internal Medicine, University of Kentucky, Lexington, KY, USA.,Department of Pathology and Laboratory Medicine, University of Kentucky, Lexington, KY, USA
| | - John S K Kauwe
- Department of Biology, Brigham Young University, Provo, UT, USA.
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14
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Ahmed M, Mäkinen VP, Mulugeta A, Shin J, Boyle T, Hyppönen E, Lee SH. Considering hormone-sensitive cancers as a single disease in the UK biobank reveals shared aetiology. Commun Biol 2022; 5:614. [PMID: 35729236 PMCID: PMC9213416 DOI: 10.1038/s42003-022-03554-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Accepted: 06/02/2022] [Indexed: 11/09/2022] Open
Abstract
Hormone-related cancers, including cancers of the breast, prostate, ovaries, uterine, and thyroid, globally contribute to the majority of cancer incidence. We hypothesize that hormone-sensitive cancers share common genetic risk factors that have rarely been investigated by previous genomic studies of site-specific cancers. Here, we show that considering hormone-sensitive cancers as a single disease in the UK Biobank reveals shared genetic aetiology. We observe that a significant proportion of variance in disease liability is explained by the genome-wide single nucleotide polymorphisms (SNPs), i.e., SNP-based heritability on the liability scale is estimated as 10.06% (SE 0.70%). Moreover, we find 55 genome-wide significant SNPs for the disease, using a genome-wide association study. Pair-wise analysis also estimates positive genetic correlations between some pairs of hormone-sensitive cancers although they are not statistically significant. Our finding suggests that heritable genetic factors may be a key driver in the mechanism of carcinogenesis shared by hormone-sensitive cancers.
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Affiliation(s)
- Muktar Ahmed
- Australian Centre for Precision Health, University of South Australia, Adelaide, SA, Australia. .,Department of Epidemiology, Faculty of Public Health, Jimma University Institute of Health, Jimma, Ethiopia. .,UniSA Clinical and Health Sciences, University of South Australia, Adelaide, SA, Australia. .,South Australian Health and Medical Research Institute, Adelaide, SA, Australia.
| | - Ville-Petteri Mäkinen
- Australian Centre for Precision Health, University of South Australia, Adelaide, SA, Australia.,Computational Systems Biology Program, Precision Medicine Theme, South Australian Health and Medical Research Institute, Adelaide, SA, Australia
| | - Anwar Mulugeta
- Australian Centre for Precision Health, University of South Australia, Adelaide, SA, Australia.,UniSA Clinical and Health Sciences, University of South Australia, Adelaide, SA, Australia.,South Australian Health and Medical Research Institute, Adelaide, SA, Australia
| | - Jisu Shin
- Australian Centre for Precision Health, University of South Australia, Adelaide, SA, Australia.,UniSA Allied Health & Human Performance, University of South Australia, Adelaide, SA, Australia
| | - Terry Boyle
- Australian Centre for Precision Health, University of South Australia, Adelaide, SA, Australia.,South Australian Health and Medical Research Institute, Adelaide, SA, Australia.,UniSA Allied Health & Human Performance, University of South Australia, Adelaide, SA, Australia
| | - Elina Hyppönen
- Australian Centre for Precision Health, University of South Australia, Adelaide, SA, Australia.,UniSA Clinical and Health Sciences, University of South Australia, Adelaide, SA, Australia.,South Australian Health and Medical Research Institute, Adelaide, SA, Australia
| | - Sang Hong Lee
- Australian Centre for Precision Health, University of South Australia, Adelaide, SA, Australia. .,South Australian Health and Medical Research Institute, Adelaide, SA, Australia. .,UniSA Allied Health & Human Performance, University of South Australia, Adelaide, SA, Australia.
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15
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Martins J, Yusupov N, Binder EB, Brückl TM, Czamara D. Early adversity as the prototype gene × environment interaction in mental disorders? Pharmacol Biochem Behav 2022; 215:173371. [PMID: 35271857 DOI: 10.1016/j.pbb.2022.173371] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 02/03/2022] [Accepted: 02/28/2022] [Indexed: 10/18/2022]
Abstract
Childhood adversity (CA) as a significant stressor has consistently been associated with the development of mental disorders. The interaction between CA and genetic variants has been proposed to play a substantial role in disease etiology. In this review, we focus on the gene by environment (GxE) paradigm, its background and interpretation and stress the necessity of its implementation in psychiatric research. Further, we discuss the findings supporting GxCA interactions, ranging from candidate gene studies to polygenic and genome-wide approaches, their strengths and limitations. To illustrate potential underlying epigenetic mechanisms by which GxE effects are translated, we focus on results from FKBP5 × CA studies and discuss how molecular evidence can supplement previous GxE findings. In conclusion, while GxE studies constitute a valuable line of investigation, more harmonized GxE studies in large, deep-phenotyped, longitudinal cohorts, and across different developmental stages are necessary to further substantiate and understand reported GxE findings.
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Affiliation(s)
- Jade Martins
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich 80804, Germany.
| | - Natan Yusupov
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich 80804, Germany; International Max Planck Research School for Translational Psychiatry (IMPRS-TP), Munich, Germany
| | - Elisabeth B Binder
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich 80804, Germany; Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA 30329, USA
| | - Tanja M Brückl
- 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
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16
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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.
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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.
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