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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.
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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.
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Beyene MB, Visvanathan R, Ahmed M, Benyamin B, Beard JR, Amare AT. Development and validation of an intrinsic capacity score in the UK Biobank study. Maturitas 2024; 185:107976. [PMID: 38537388 DOI: 10.1016/j.maturitas.2024.107976] [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: 11/27/2023] [Revised: 02/06/2024] [Accepted: 03/14/2024] [Indexed: 06/03/2024]
Abstract
BACKGROUND In 2015, the World Health Organization introduced the concept of intrinsic capacity (IC) to define the individual-level characteristics that enable an older person to be and do the things they value. This study developed an intrinsic capacity score for UK Biobank study participants and validated its use as a tool for health outcome prediction, understanding healthy aging trajectories, and genetic research. METHODS Our analysis included data from 45,208 UK biobank participants who had a complete record of the ten variables included in the analysis. Factor adequacy was tested using Kaiser-Meyer-Olkin, Barthelt's, and the determinant of matrix tests, and the number of factors was determined by the parallel analysis method. Exploratory and confirmatory factor analyses were employed to determine the structure and dimensionality of indicators. Finally, the intrinsic capacity score was generated, and its construct and predictive validities as well as reliability were assessed. RESULTS The factor analysis identified a multidimensional construct comprising one general factor (intrinsic capacity) and five specific factors (locomotor, vitality, cognitive, psychological, and sensory). The bifactor structure showed a better fit (comparative fit index = 0.995, Tucker Lewis index = 0.976, root mean square error of approximation = 0.025, root mean square residual = 0.009) than the conventional five-factor structure. The intrinsic capacity score generated using the bifactor confirmatory factor analysis has good construct validity, as demonstrated by an inverse association with age (lower intrinsic capacity in older age; (β) =-0.035 (95%CI: -0.036, -0.034)), frailty (lower intrinsic capacity score in prefrail participants, β = -0.104 (95%CI: (-0.114, -0.094)) and frail participants, β = -0.227 (95%CI: -0.267, -0.186) than robust participants), and comorbidity (a lower intrinsic capacity score associated with increased Charlson's comorbidity index, β =-0.019 (95%CI: -0.022, -0.015)). The intrinsic capacity score also predicted comorbidity (a one-unit increase in baseline intrinsic capacity score led to a lower Charlson's comorbidity index, β = 0.147 (95%CI: -0.173, -0.121)) and mortality (a one-unit increase in baseline intrinsic capacity score led to 25 % lower risk of death, odds ratio = 0.75(95%CI: 0.663, 0.848)). CONCLUSION The bifactor structure showed a better fit in all goodness of fit tests. The intrinsic capacity construct has strong structural, construct, and predictive validities and is a promising tool for monitoring aging trajectories.
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Affiliation(s)
- Melkamu Bedimo Beyene
- Discipline of Psychiatry, School of Medicine, University of Adelaide, Adelaide, SA, Australia; Adelaide Geriatrics Training and Research with Aged Care Centre (GTRAC), Faculty of Health and Medical Sciences, University of Adelaide, Woodville, SA, 5011, Australia
| | - Renuka Visvanathan
- Adelaide Geriatrics Training and Research with Aged Care Centre (GTRAC), Faculty of Health and Medical Sciences, University of Adelaide, Woodville, SA, 5011, Australia; Aged and Extended Care Services, The Queen Elizabeth Hospital, Central Adelaide Local Health Network, Adelaide, SA, Australia
| | - Muktar Ahmed
- Discipline of Psychiatry, School of Medicine, University of Adelaide, Adelaide, SA, Australia
| | - Beben Benyamin
- Australian Centre for Precision Health, Allied Health and Human Performance, University of South Australia, Adelaide 5000, Australia; South Australian Health and Medical Research Institute, Adelaide 5000, Australia
| | - John R Beard
- International Longevity Centre USA, Columbia University Mailman School of Public Health, NY, USA
| | - Azmeraw T Amare
- Discipline of Psychiatry, School of Medicine, University of Adelaide, Adelaide, SA, Australia; Adelaide Geriatrics Training and Research with Aged Care Centre (GTRAC), Faculty of Health and Medical Sciences, University of Adelaide, Woodville, SA, 5011, Australia.
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3
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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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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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Pérez-Gutiérrez AM, Carmona R, Loucera C, Cervilla JA, Gutiérrez B, Molina E, Lopez-Lopez D, Pérez-Florido J, Zarza-Rebollo JA, López-Isac E, Dopazo J, Martínez-González LJ, Rivera M. Mutational landscape of risk variants in comorbid depression and obesity: a next-generation sequencing approach. Mol Psychiatry 2024:10.1038/s41380-024-02609-2. [PMID: 38806690 DOI: 10.1038/s41380-024-02609-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 05/03/2024] [Accepted: 05/13/2024] [Indexed: 05/30/2024]
Abstract
Major depression (MD) and obesity are complex genetic disorders that are frequently comorbid. However, the study of both diseases concurrently remains poorly addressed and therefore the underlying genetic mechanisms involved in this comorbidity remain largely unknown. Here we examine the contribution of common and rare variants to this comorbidity through a next-generation sequencing (NGS) approach. Specific genomic regions of interest in MD and obesity were sequenced in a group of 654 individuals from the PISMA-ep epidemiological study. We obtained variants across the entire frequency spectrum and assessed their association with comorbid MD and obesity, both at variant and gene levels. We identified 55 independent common variants and a burden of rare variants in 4 genes (PARK2, FGF21, HIST1H3D and RSRC1) associated with the comorbid phenotype. Follow-up analyses revealed significantly enriched gene-sets associated with biological processes and pathways involved in metabolic dysregulation, hormone signaling and cell cycle regulation. Our results suggest that, while risk variants specific to the comorbid phenotype have been identified, the genes functionally impacted by the risk variants share cell biological processes and signaling pathways with MD and obesity phenotypes separately. To the best of our knowledge, this is the first study involving a targeted sequencing approach toward the study of the comorbid MD and obesity. The framework presented here allowed a deep characterization of the genetics of the co-occurring MD and obesity, revealing insights into the mutational and functional profile that underlies this comorbidity and contributing to a better understanding of the relationship between these two disabling disorders.
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Affiliation(s)
- Ana M Pérez-Gutiérrez
- Department of Biochemistry and Molecular Biology II, Faculty of Pharmacy, University of Granada, Granada, Spain
- Institute of Neurosciences "Federico Olóriz", Biomedical Research Center (CIBM), University of Granada, Granada, Spain
- Instituto de Investigación Biosanitaria, Ibs Granada, Granada, Spain
| | - Rosario Carmona
- Platform for Computational Medicine, Andalusian Public Foundation Progress and Health-FPS, Seville, Spain
- Computational Systems Medicine, Institute of Biomedicine of Seville (IBIS), Hospital Virgen del Rocío, Seville, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER-ISCIII), U715, Seville, Spain
| | - Carlos Loucera
- Platform for Computational Medicine, Andalusian Public Foundation Progress and Health-FPS, Seville, Spain
- Computational Systems Medicine, Institute of Biomedicine of Seville (IBIS), Hospital Virgen del Rocío, Seville, Spain
| | - Jorge A Cervilla
- Institute of Neurosciences "Federico Olóriz", Biomedical Research Center (CIBM), University of Granada, Granada, Spain
- Instituto de Investigación Biosanitaria, Ibs Granada, Granada, Spain
- Department of Psychiatry, Faculty of Medicine, University of Granada, Granada, Spain
| | - Blanca Gutiérrez
- Institute of Neurosciences "Federico Olóriz", Biomedical Research Center (CIBM), University of Granada, Granada, Spain
- Instituto de Investigación Biosanitaria, Ibs Granada, Granada, Spain
- Department of Psychiatry, Faculty of Medicine, University of Granada, Granada, Spain
| | - Esther Molina
- Institute of Neurosciences "Federico Olóriz", Biomedical Research Center (CIBM), University of Granada, Granada, Spain
- Instituto de Investigación Biosanitaria, Ibs Granada, Granada, Spain
- Department of Nursing, Faculty of Health Sciences, University of Granada, Granada, Spain
| | - Daniel Lopez-Lopez
- Platform for Computational Medicine, Andalusian Public Foundation Progress and Health-FPS, Seville, Spain
- Computational Systems Medicine, Institute of Biomedicine of Seville (IBIS), Hospital Virgen del Rocío, Seville, Spain
| | - Javier Pérez-Florido
- Platform for Computational Medicine, Andalusian Public Foundation Progress and Health-FPS, Seville, Spain
- Computational Systems Medicine, Institute of Biomedicine of Seville (IBIS), Hospital Virgen del Rocío, Seville, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER-ISCIII), U715, Seville, Spain
| | - Juan Antonio Zarza-Rebollo
- Department of Biochemistry and Molecular Biology II, Faculty of Pharmacy, University of Granada, Granada, Spain
- Institute of Neurosciences "Federico Olóriz", Biomedical Research Center (CIBM), University of Granada, Granada, Spain
- Instituto de Investigación Biosanitaria, Ibs Granada, Granada, Spain
| | - Elena López-Isac
- Department of Biochemistry and Molecular Biology II, Faculty of Pharmacy, University of Granada, Granada, Spain
- Institute of Neurosciences "Federico Olóriz", Biomedical Research Center (CIBM), University of Granada, Granada, Spain
- Instituto de Investigación Biosanitaria, Ibs Granada, Granada, Spain
| | - Joaquín Dopazo
- Platform for Computational Medicine, Andalusian Public Foundation Progress and Health-FPS, Seville, Spain
- Computational Systems Medicine, Institute of Biomedicine of Seville (IBIS), Hospital Virgen del Rocío, Seville, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER-ISCIII), U715, Seville, Spain
| | - Luis Javier Martínez-González
- Genomics Unit, Pfizer-University of Granada-Junta de Andalucía Centre for Genomics and Oncological Research (GENYO), Granada, Spain
| | - Margarita Rivera
- Department of Biochemistry and Molecular Biology II, Faculty of Pharmacy, University of Granada, Granada, Spain.
- Institute of Neurosciences "Federico Olóriz", Biomedical Research Center (CIBM), University of Granada, Granada, Spain.
- Instituto de Investigación Biosanitaria, Ibs Granada, Granada, Spain.
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6
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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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7
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Kemper KE, Sidorenko J, Wang H, Hayes BJ, Wray NR, Yengo L, Keller MC, Goddard M, Visscher PM. Genetic influence on within-person longitudinal change in anthropometric traits in the UK Biobank. Nat Commun 2024; 15:3776. [PMID: 38710707 PMCID: PMC11074304 DOI: 10.1038/s41467-024-47802-7] [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: 05/11/2023] [Accepted: 04/10/2024] [Indexed: 05/08/2024] Open
Abstract
The causes of temporal fluctuations in adult traits are poorly understood. Here, we investigate the genetic determinants of within-person trait variability of 8 repeatedly measured anthropometric traits in 50,117 individuals from the UK Biobank. We found that within-person (non-directional) variability had a SNP-based heritability of 2-5% for height, sitting height, body mass index (BMI) and weight (P ≤ 2.4 × 10-3). We also analysed longitudinal trait change and show a loss of both average height and weight beyond about 70 years of age. A variant tracking the Alzheimer's risk APOE- E 4 allele (rs429358) was significantly associated with weight loss ( β = -0.047 kg per yr, s.e. 0.007, P = 2.2 × 10-11), and using 2-sample Mendelian Randomisation we detected a relationship consistent with causality between decreased lumbar spine bone mineral density and height loss (bxy = 0.011, s.e. 0.003, P = 3.5 × 10-4). Finally, population-level variance quantitative trait loci (vQTL) were consistent with within-person variability for several traits, indicating an overlap between trait variability assessed at the population or individual level. Our findings help elucidate the genetic influence on trait-change within an individual and highlight disease risks associated with these changes.
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Affiliation(s)
- Kathryn E Kemper
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia.
| | - Julia Sidorenko
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Huanwei Wang
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Ben J Hayes
- Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Brisbane, QLD, Australia
| | - Naomi R Wray
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Loic Yengo
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Matthew C Keller
- Institute for Behavioral Genetics, University of Colorado, Boulder, CO, USA
| | - Michael Goddard
- Faculty of Veterinary and Agricultural Science, University of Melbourne, Parkville, VIC, Australia
- Biosciences Research Division, Agriculture Victoria, Bundoora, VIC, Australia
| | - Peter M Visscher
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia.
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
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Bass AJ, Bian S, Wingo AP, Wingo TS, Cutler DJ, Epstein MP. Identifying latent genetic interactions in genome-wide association studies using multiple traits. Genome Med 2024; 16:62. [PMID: 38664839 PMCID: PMC11044415 DOI: 10.1186/s13073-024-01329-0] [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: 11/30/2023] [Accepted: 04/02/2024] [Indexed: 04/28/2024] Open
Abstract
The "missing" heritability of complex traits may be partly explained by genetic variants interacting with other genes or environments that are difficult to specify, observe, and detect. We propose a new kernel-based method called Latent Interaction Testing (LIT) to screen for genetic interactions that leverages pleiotropy from multiple related traits without requiring the interacting variable to be specified or observed. Using simulated data, we demonstrate that LIT increases power to detect latent genetic interactions compared to univariate methods. We then apply LIT to obesity-related traits in the UK Biobank and detect variants with interactive effects near known obesity-related genes (URL: https://CRAN.R-project.org/package=lit ).
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Affiliation(s)
- Andrew J Bass
- Department of Human Genetics, Emory University, Atlanta, GA, 30322, USA.
| | - Shijia Bian
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, 30322, USA
| | - Aliza P Wingo
- Department of Psychiatry, Emory University, Atlanta, GA, 30322, USA
| | - Thomas S Wingo
- Department of Human Genetics, Emory University, Atlanta, GA, 30322, USA
- Department of Neurology, Emory University, Atlanta, GA, 30322, USA
| | - David J Cutler
- Department of Human Genetics, Emory University, Atlanta, GA, 30322, USA
| | - Michael P Epstein
- Department of Human Genetics, Emory University, Atlanta, GA, 30322, USA.
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9
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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.
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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
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10
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Lu Z, Wang X, Carr M, Kim A, Gazal S, Mohammadi P, Wu L, Gusev A, Pirruccello J, Kachuri L, Mancuso N. Improved multi-ancestry fine-mapping identifies cis-regulatory variants underlying molecular traits and disease risk. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.15.24305836. [PMID: 38699369 PMCID: PMC11065034 DOI: 10.1101/2024.04.15.24305836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
Multi-ancestry statistical fine-mapping of cis-molecular quantitative trait loci (cis-molQTL) aims to improve the precision of distinguishing causal cis-molQTLs from tagging variants. However, existing approaches fail to reflect shared genetic architectures. To solve this limitation, we present the Sum of Shared Single Effects (SuShiE) model, which leverages LD heterogeneity to improve fine-mapping precision, infer cross-ancestry effect size correlations, and estimate ancestry-specific expression prediction weights. We apply SuShiE to mRNA expression measured in PBMCs (n=956) and LCLs (n=814) together with plasma protein levels (n=854) from individuals of diverse ancestries in the TOPMed MESA and GENOA studies. We find SuShiE fine-maps cis-molQTLs for 16% more genes compared with baselines while prioritizing fewer variants with greater functional enrichment. SuShiE infers highly consistent cis-molQTL architectures across ancestries on average; however, we also find evidence of heterogeneity at genes with predicted loss-of-function intolerance, suggesting that environmental interactions may partially explain differences in cis-molQTL effect sizes across ancestries. Lastly, we leverage estimated cis-molQTL effect-sizes to perform individual-level TWAS and PWAS on six white blood cell-related traits in AOU Biobank individuals (n=86k), and identify 44 more genes compared with baselines, further highlighting its benefits in identifying genes relevant for complex disease risk. Overall, SuShiE provides new insights into the cis-genetic architecture of molecular traits.
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Affiliation(s)
- Zeyun Lu
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Xinran Wang
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Matthew Carr
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Artem Kim
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Steven Gazal
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA
| | - Pejman Mohammadi
- Center for Immunity and Immunotherapies, Seattle Children’s Research Institute, Seattle, WA, USA
- Department of Pediatrics, University of Washington School of Medicine, Seattle, WA, USA
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Lang Wu
- Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaiʻi Cancer Center, University of Hawaiʻi at Mānoa, Honolulu, HI, USA
| | - Alexander Gusev
- Harvard Medical School and Dana-Farber Cancer Institute, Boston, MA, USA
| | - James Pirruccello
- Division of Cardiology, University of California San Francisco, San Francisco, CA, USA
| | - Linda Kachuri
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Nicholas Mancuso
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA
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11
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Hui D, Dudek S, Kiryluk K, Walunas TL, Kullo IJ, Wei WQ, Tiwari HK, Peterson JF, Chung WK, Davis B, Khan A, Kottyan L, Limdi NA, Feng Q, Puckelwartz MJ, Weng C, Smith JL, Karlson EW, Jarvik GP, Ritchie MD. Risk factors affecting polygenic score performance across diverse cohorts. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.05.10.23289777. [PMID: 38645167 PMCID: PMC11030495 DOI: 10.1101/2023.05.10.23289777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Apart from ancestry, personal or environmental covariates may contribute to differences in polygenic score (PGS) performance. We analyzed effects of covariate stratification and interaction on body mass index (BMI) PGS (PGSBMI) across four cohorts of European (N=491,111) and African (N=21,612) ancestry. Stratifying on binary covariates and quintiles for continuous covariates, 18/62 covariates had significant and replicable R2 differences among strata. Covariates with the largest differences included age, sex, blood lipids, physical activity, and alcohol consumption, with R2 being nearly double between best and worst performing quintiles for certain covariates. 28 covariates had significant PGSBMI-covariate interaction effects, modifying PGSBMI effects by nearly 20% per standard deviation change. We observed overlap between covariates that had significant R2 differences among strata and interaction effects - across all covariates, their main effects on BMI were correlated with their maximum R2 differences and interaction effects (0.56 and 0.58, respectively), suggesting high-PGSBMI individuals have highest R2 and increase in PGS effect. Using quantile regression, we show the effect of PGSBMI increases as BMI itself increases, and that these differences in effects are directly related to differences in R2 when stratifying by different covariates. Given significant and replicable evidence for context-specific PGSBMI performance and effects, we investigated ways to increase model performance taking into account non-linear effects. Machine learning models (neural networks) increased relative model R2 (mean 23%) across datasets. Finally, creating PGSBMI directly from GxAge GWAS effects increased relative R2 by 7.8%. These results demonstrate that certain covariates, especially those most associated with BMI, significantly affect both PGSBMI performance and effects across diverse cohorts and ancestries, and we provide avenues to improve model performance that consider these effects.
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Affiliation(s)
- Daniel Hui
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Scott Dudek
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Krzysztof Kiryluk
- Division of Nephrology, Department of Medicine, Columbia University, NY, New York
| | - Theresa L. Walunas
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | | | - Wei-Qi Wei
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
| | - Hemant K. Tiwari
- Department of Pediatrics, University of Alabama at Birmingham, Birmingham, AL
| | - Josh F. Peterson
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
| | - Wendy K. Chung
- Departments of Pediatrics and Medicine, Columbia University Irving Medical Center, Columbia University, New York, NY
| | - Brittney Davis
- Department of Neurology, School of Medicine, University of Alabama at Birmingham, Birmingham, AL
| | - Atlas Khan
- Division of Nephrology, Department of Medicine, Columbia University, NY, New York
| | - Leah Kottyan
- The Center for Autoimmune Genomics and Etiology, Division of Human Genetics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH
| | - Nita A. Limdi
- Department of Neurology, School of Medicine, University of Alabama at Birmingham, Birmingham, AL
| | - Qiping Feng
- Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Megan J. Puckelwartz
- Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Chunhua Weng
- Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY
| | - Johanna L. Smith
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
| | - Elizabeth W. Karlson
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA
| | | | - Gail P. Jarvik
- Departments of Medicine (Medical Genetics) and Genome Sciences, University of Washington Medical Center, Seattle, WA
| | - Marylyn D. Ritchie
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
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12
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Qiu S, Sun Y, Guo J, Zhang Y, Hu Y. Genome-wide analysis reveals extensive genetic overlap between childhood phenotypes and later-life type 2 diabetes. Comput Biol Med 2024; 171:108065. [PMID: 38387379 DOI: 10.1016/j.compbiomed.2024.108065] [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: 11/12/2023] [Revised: 12/26/2023] [Accepted: 01/27/2024] [Indexed: 02/24/2024]
Abstract
Observational studies have indicated a potential influence of childhood phenotypes on the later development of type 2 diabetes (T2D). However, the underlying biological mechanisms remain unclear. In this study, we conducted a comprehensive genome-wide analysis to investigate the shared genetic architecture and genetic loci between nine childhood phenotypes (N = 4202-620,26) and later-life T2D (N = 80,154) using genetic correlation, mendelian randomization (MR), and conjunctional false discovery rate (conjFDR) statistical frameworks. Our findings demonstrated substantial genetic correlations and pleiotropic enrichment between childhood obesity, body mass index (BMI), systolic blood pressure (SBP), diastolic blood pressure (DBP), and later-life T2D. Childhood obesity exhibited a significant association with increased later-life T2D risk through 10 mediators, 6 of which were adulthood obesity-related phenotypes. Additionally, we identified 69, 83, 3, 5, 10, 5, 3, and 7 loci shared between childhood obesity, BMI, SBP, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), triglycerides (TG), apolipoprotein A-I (ApoA-I), apolipoprotein B (ApoB), and T2D at conjFDR <0.05, with the majority of these loci being novel discoveries. Overall, our study reveals extensive genetic overlap between childhood obesity-related phenotypes and T2D with concordant effect directions, shedding new light on variants and phenotypes with lifelong effects.
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Affiliation(s)
- Shizheng Qiu
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, 150001, China
| | - Yige Sun
- Department of Radiology, Harbin Medical University Cancer Hospital, Harbin, China
| | - Jiahe Guo
- School of Future Technology, Harbin Institute of Technology, Harbin, 150001, China
| | - Yu Zhang
- Beidahuang Industry Group General Hospital, Harbin, 150088, China.
| | - Yang Hu
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, 150001, China.
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13
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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.
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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
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14
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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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15
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Bass AJ, Bian S, Wingo AP, Wingo TS, Cutler DJ, Epstein MP. Identifying latent genetic interactions in genome-wide association studies using multiple traits. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.11.557155. [PMID: 37745553 PMCID: PMC10515795 DOI: 10.1101/2023.09.11.557155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Genome-wide association studies of complex traits frequently find that SNP-based estimates of heritability are considerably smaller than estimates from classic family-based studies. This 'missing' heritability may be partly explained by genetic variants interacting with other genes or environments that are difficult to specify, observe, and detect. To circumvent these challenges, we propose a new method to detect genetic interactions that leverages pleiotropy from multiple related traits without requiring the interacting variable to be specified or observed. Our approach, Latent Interaction Testing (LIT), uses the observation that correlated traits with shared latent genetic interactions have trait variance and covariance patterns that differ by genotype. LIT examines the relationship between trait variance/covariance patterns and genotype using a flexible kernel-based framework that is computationally scalable for biobank-sized datasets with a large number of traits. We first use simulated data to demonstrate that LIT substantially increases power to detect latent genetic interactions compared to a trait-by-trait univariate method. We then apply LIT to four obesity-related traits in the UK Biobank and detect genetic variants with interactive effects near known obesity-related genes. Overall, we show that LIT, implemented in the R package lit, uses shared information across traits to improve detection of latent genetic interactions compared to standard approaches.
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Affiliation(s)
- Andrew J. Bass
- Department of Human Genetics, Emory University, Atlanta, GA 30322, USA
| | - Shijia Bian
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322, USA
| | - Aliza P. Wingo
- Department of Psychiatry, Emory University, Atlanta, GA 30322, USA
| | - Thomas S. Wingo
- Department of Human Genetics, Emory University, Atlanta, GA 30322, USA
- Department of Neurology, Emory University, Atlanta, GA 30322, USA
| | - David J. Cutler
- Department of Human Genetics, Emory University, Atlanta, GA 30322, USA
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16
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Ojavee SE, Darrous L, Patxot M, Läll K, Fischer K, Mägi R, Kutalik Z, Robinson MR. Genetic insights into the age-specific biological mechanisms governing human ovarian aging. Am J Hum Genet 2023; 110:1549-1563. [PMID: 37543033 PMCID: PMC10502738 DOI: 10.1016/j.ajhg.2023.07.006] [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: 03/10/2023] [Revised: 07/07/2023] [Accepted: 07/09/2023] [Indexed: 08/07/2023] Open
Abstract
There is currently little evidence that the genetic basis of human phenotype varies significantly across the lifespan. However, time-to-event phenotypes are understudied and can be thought of as reflecting an underlying hazard, which is unlikely to be constant through life when values take a broad range. Here, we find that 74% of 245 genome-wide significant genetic associations with age at natural menopause (ANM) in the UK Biobank show a form of age-specific effect. Nineteen of these replicated discoveries are identified only by our modeling framework, which determines the time dependency of DNA-variant age-at-onset associations without a significant multiple-testing burden. Across the range of early to late menopause, we find evidence for significantly different underlying biological pathways, changes in the signs of genetic correlations of ANM to health indicators and outcomes, and differences in inferred causal relationships. We find that DNA damage response processes only act to shape ovarian reserve and depletion for women of early ANM. Genetically mediated delays in ANM were associated with increased relative risk of breast cancer and leiomyoma at all ages and with high cholesterol and heart failure for late-ANM women. These findings suggest that a better understanding of the age dependency of genetic risk factor relationships among health indicators and outcomes is achievable through appropriate statistical modeling of large-scale biobank data.
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Affiliation(s)
- Sven E Ojavee
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland; Swiss Institute of Bioinformatics, Lausanne, Switzerland.
| | - Liza Darrous
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland; Swiss Institute of Bioinformatics, Lausanne, Switzerland; University Center for Primary Care and Public Health, Lausanne, Switzerland
| | - Marion Patxot
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland; Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Kristi Läll
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Krista Fischer
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia; Institute of Mathematics and Statistics, University of Tartu, Tartu, Estonia
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Zoltan Kutalik
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland; Swiss Institute of Bioinformatics, Lausanne, Switzerland; University Center for Primary Care and Public Health, Lausanne, Switzerland
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17
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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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18
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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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Soroceanu RP, Timofte DV, Danila R, Timofeiov S, Livadariu R, Miler AA, Ciuntu BM, Drugus D, Checherita LE, Drochioi IC, Ciofu ML, Azoicai D. The Impact of Bariatric Surgery on Quality of Life in Patients with Obesity. J Clin Med 2023; 12:4225. [PMID: 37445261 DOI: 10.3390/jcm12134225] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2023] [Revised: 06/16/2023] [Accepted: 06/20/2023] [Indexed: 07/15/2023] Open
Abstract
Obesity has become a widespread health problem influencing people's health, general well-being, and healthcare costs. It also represents an important risk factor for multiple comorbidities and malignancies. OBJECTIVES the primary objective of this study was to provide notable insights to healthcare professionals regarding the management of patients with obesity, to highlight the effectiveness of bariatric surgical methods in losing excess weight, and to establish the relationship between weight loss and changes in quality of life (QoL). MATERIAL AND METHODS our study evaluated the QoL of 76 patients following bariatric surgery at different postoperative stages using the 36-Item Short Form Survey (SF-36) and The World Health Organization Quality of Life-BREF (WHOQOL-BREF) questionnaires. RESULTS regarding the type of bariatric procedure performed, out of the 76 respondents, 39.47% underwent gastric bypass surgery (RYGB), 56.57% underwent sleeve gastrectomy (LSG), and only 3.94% underwent single anastomosis duodeno-ileal switch (SADI-S). Pertaining to the SF-36 questionnaire, the lowest average scores were found in the energy/fatigue subscales and in the limitations due to mental health, which remained consistent across surgery types with a significant decrease in the SADI-S group. Concerning the WHOQOL-BREF questionnaire, the lowest mean scores were found in the environment (15.03 ± 2.37) and social relations (16.08 ± 2.22) subscales, whilst the highest average scores were in physical health (16.30 ± 2.03) and mental health (16.57 ± 2.16). CONCLUSIONS the findings revealed that whilst bariatric surgery significantly improved physical health, it resulted in a decrease in mental health scores. Consequently, the study emphasizes the importance of adopting a holistic approach to managing obesity that considers improving both physical and mental health outcomes.
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Affiliation(s)
- Radu Petru Soroceanu
- Department of Surgery, "Grigore T. Popa" University of Medicine and Pharmacy, 700115 Iasi, Romania
- Department of Surgery, "St. Spiridon" County Clinical Emergency Hospital, 700111 Iasi, Romania
| | - Daniel Vasile Timofte
- Department of Surgery, "Grigore T. Popa" University of Medicine and Pharmacy, 700115 Iasi, Romania
- Department of Surgery, "St. Spiridon" County Clinical Emergency Hospital, 700111 Iasi, Romania
| | - Radu Danila
- Department of Surgery, "Grigore T. Popa" University of Medicine and Pharmacy, 700115 Iasi, Romania
- Department of Surgery, "St. Spiridon" County Clinical Emergency Hospital, 700111 Iasi, Romania
| | - Sergiu Timofeiov
- Department of Surgery, "Grigore T. Popa" University of Medicine and Pharmacy, 700115 Iasi, Romania
- Department of Surgery, "St. Spiridon" County Clinical Emergency Hospital, 700111 Iasi, Romania
| | - Roxana Livadariu
- Department of Surgery, "Grigore T. Popa" University of Medicine and Pharmacy, 700115 Iasi, Romania
- Department of Surgery, "St. Spiridon" County Clinical Emergency Hospital, 700111 Iasi, Romania
| | - Ancuta Andreea Miler
- Department of Surgery, "Grigore T. Popa" University of Medicine and Pharmacy, 700115 Iasi, Romania
- Department of Surgery, "St. Spiridon" County Clinical Emergency Hospital, 700111 Iasi, Romania
| | - Bogdan Mihnea Ciuntu
- Department of Surgery, "Grigore T. Popa" University of Medicine and Pharmacy, 700115 Iasi, Romania
- Department of Surgery, "St. Spiridon" County Clinical Emergency Hospital, 700111 Iasi, Romania
| | - Daniela Drugus
- Department of Preventive Medicine and Interdisciplinarity, "Grigore T. Popa" University of Medicine and Pharmacy, 700115 Iasi, Romania
| | | | - Ilie Cristian Drochioi
- Department of Oral and Maxillofacial Surgery, "Grigore T. Popa" University of Medicine and Pharmacy, 700115 Iasi, Romania
| | - Mihai Liviu Ciofu
- Department of Oral and Maxillofacial Surgery, "Grigore T. Popa" University of Medicine and Pharmacy, 700115 Iasi, Romania
| | - Doina Azoicai
- Department of Preventive Medicine and Interdisciplinarity, "Grigore T. Popa" University of Medicine and Pharmacy, 700115 Iasi, Romania
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20
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Luo G, Li Y, Yao C, Li M, Li J, Zhang X. Prevalence of overweight and obesity in patients with major depressive disorder with anxiety: Mediating role of thyroid hormones and metabolic parameters. J Affect Disord 2023; 335:298-304. [PMID: 37201896 DOI: 10.1016/j.jad.2023.05.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/21/2023] [Revised: 04/28/2023] [Accepted: 05/05/2023] [Indexed: 05/20/2023]
Abstract
BACKGROUNDS Anxiety is a common comorbidity in major depressive disorder (MDD); however, its role in overweight and obesity in MDD patients remains unclear. We examined the relationship between severe anxiety and overweight and obesity, as well as the mediating role of thyroid hormones and metabolic parameters in MDD patients. METHODS This cross-sectional study recruited 1718 first-episode drug-naïve MDD outpatients. All participants were rated on the Hamilton Depression Rating Scale for depression and Hamilton Anxiety Rating Scale for anxiety and measured in thyroid hormones and metabolic parameters. RESULTS A total of 218 (12.7 %) individuals had severe anxiety. The prevalence of overweight and obesity in patients with severe anxiety was 62.8 % and 5.5 %, respectively. Severe anxiety symptoms were related to overweight (Odds Ratio [OR]: 1.47, 95 % CI: 1.08, 2.00) and obesity (OR: 2.10, 95 % CI: 1.07, 4.15). The association between severe anxiety and overweight was mainly attenuated by thyroid hormones (40.4 %), blood pressure (31.9 %), and plasma glucose (19.1 %). For obesity, the association with severe anxiety was mainly attenuated by thyroid hormones (48.2 %), blood pressure (39.1 %), and total cholesterol (28.2 %). LIMITATIONS Due to the cross-sectional design, no causal relationship could be derived. CONCLUSIONS Thyroid hormones and metabolic parameters can explain the risk of overweight and obesity associated with severe anxiety in MDD patients. These findings add to the knowledge of the pathological pathway of overweight and obesity in severe anxiety comorbid MDD.
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Affiliation(s)
- Guoshuai Luo
- Laboratory of Biological Psychiatry, Institute of Mental Health, Tianjin Anding Hospital, Mental Health Center of Tianjin Medical University, Tianjin, China, 300222
| | - Yaxi Li
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, 3210 Humin Rd, Shanghai 201108, China
| | - Cong Yao
- Laboratory of Biological Psychiatry, Institute of Mental Health, Tianjin Anding Hospital, Mental Health Center of Tianjin Medical University, Tianjin, China, 300222
| | - Meijuan Li
- Laboratory of Biological Psychiatry, Institute of Mental Health, Tianjin Anding Hospital, Mental Health Center of Tianjin Medical University, Tianjin, China, 300222
| | - Jie Li
- Laboratory of Biological Psychiatry, Institute of Mental Health, Tianjin Anding Hospital, Mental Health Center of Tianjin Medical University, Tianjin, China, 300222.
| | - Xiangyang Zhang
- Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China.
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21
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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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22
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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.
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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
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23
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Alamin M, Sultana MH, Lou X, Jin W, Xu H. Dissecting Complex Traits Using Omics Data: A Review on the Linear Mixed Models and Their Application in GWAS. PLANTS (BASEL, SWITZERLAND) 2022; 11:3277. [PMID: 36501317 PMCID: PMC9739826 DOI: 10.3390/plants11233277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 11/23/2022] [Accepted: 11/25/2022] [Indexed: 06/17/2023]
Abstract
Genome-wide association study (GWAS) is the most popular approach to dissecting complex traits in plants, humans, and animals. Numerous methods and tools have been proposed to discover the causal variants for GWAS data analysis. Among them, linear mixed models (LMMs) are widely used statistical methods for regulating confounding factors, including population structure, resulting in increased computational proficiency and statistical power in GWAS studies. Recently more attention has been paid to pleiotropy, multi-trait, gene-gene interaction, gene-environment interaction, and multi-locus methods with the growing availability of large-scale GWAS data and relevant phenotype samples. In this review, we have demonstrated all possible LMMs-based methods available in the literature for GWAS. We briefly discuss the different LMM methods, software packages, and available open-source applications in GWAS. Then, we include the advantages and weaknesses of the LMMs in GWAS. Finally, we discuss the future perspective and conclusion. The present review paper would be helpful to the researchers for selecting appropriate LMM models and methods quickly for GWAS data analysis and would benefit the scientific society.
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Affiliation(s)
- Md. Alamin
- Institute of Bioinformatics, Zhejiang University, Hangzhou 310058, China
- Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
| | | | - Xiangyang Lou
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Wenfei Jin
- Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
| | - Haiming Xu
- Institute of Bioinformatics, Zhejiang University, Hangzhou 310058, China
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24
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Sørensen TIA, Metz S, Kilpeläinen TO. Do gene-environment interactions have implications for the precision prevention of type 2 diabetes? Diabetologia 2022; 65:1804-1813. [PMID: 34993570 DOI: 10.1007/s00125-021-05639-5] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 11/05/2021] [Indexed: 01/10/2023]
Abstract
The past decades have seen a rapid global rise in the incidence of type 2 diabetes. This surge has been driven by diabetogenic environmental changes that may act together with a genetic predisposition to type 2 diabetes. It is possible that there is a synergistic gene-environment interaction, where the effects of the diabetogenic environment depend on the genetic predisposition to type 2 diabetes. Randomised trials have shown that it is possible to delay, or even prevent the development of type 2 diabetes in individuals at elevated risk through behavioural modification, focusing on weight loss, physical activity and diet. There is wide heterogeneity between individuals regarding the effectiveness of these interventions, which could, in part, be due to genetic differences. However, the studies of gene-environment interactions performed thus far suggest that behavioural modifications appear equally effective in reducing the incidence of type 2 diabetes from the stage of impaired glucose tolerance, regardless of the known underlying genetic predisposition. Recent studies suggest that there may be several subtypes of type 2 diabetes, which give new opportunities for gaining insight into gene-environment interactions. At present, the role of gene-environment interactions in the development of type 2 diabetes remains unclear. With many puzzle pieces missing in the general picture of type 2 diabetes development, the available evidence of gene-environment interactions is not ready for translation to individualised type 2 diabetes prevention based on genetic profiling.
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Affiliation(s)
- Thorkild I A Sørensen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Section of Epidemiology, Department of Public Health, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Sophia Metz
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Tuomas O Kilpeläinen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
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25
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Leone M, Kuja-Halkola R, Leval A, Butwicka A, Skov J, Zhang R, Liu S, Larsson H, Bergen SE. Genetic and Environmental Contribution to the Co-Occurrence of Endocrine-Metabolic Disorders and Depression: A Nationwide Swedish Study of Siblings. Am J Psychiatry 2022; 179:824-832. [PMID: 36128682 DOI: 10.1176/appi.ajp.21090954] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
OBJECTIVE Depression is common in individuals with endocrine-metabolic disorders and vice versa, and a better understanding of the underlying factors contributing to the comorbidity of these disorders is needed. This study investigated the familial coaggregation of depression and endocrine-metabolic disorders and estimated the contribution of genetic and environmental factors to their co-occurrence. METHODS This population-based cohort study included 2.2 million individuals born in Sweden between 1973 and 1996, with follow-up through 2013. Participants were linked to their biological parents, allowing identification of full siblings, maternal half siblings, and paternal half siblings. Diagnoses of depression and endocrine-metabolic conditions were investigated, with the latter grouped into autoimmune disorders (autoimmune hypothyroidism, Graves' disease, and type 1 diabetes) and non-autoimmune disorders (type 2 diabetes, obesity, and polycystic ovary syndrome). Logistic regression and Cox regression were used to estimate the associations between endocrine-metabolic disorders and depression within the same individual and across siblings. Quantitative genetic modeling was performed to investigate the relative contribution of genetic and environmental influences. RESULTS Individuals with endocrine-metabolic disorders had a significantly higher risk of depression, with odds ratios ranging from 1.43 (95% CI=1.30, 1.57) for Graves' disease to 3.48 (95% CI=3.25, 3.72) for type 2 diabetes. Increased risks extended to full and half siblings. These correlations were mainly explained by shared genetic influences for non-autoimmune conditions, and by nonshared environmental factors for autoimmune disorders, especially for type 1 diabetes. CONCLUSIONS These findings provide phenotypic and etiological insights into the co-occurrence of depression and various endocrine-metabolic conditions, which could guide future research aiming at identifying pathophysiological mechanisms and intervention targets.
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Affiliation(s)
- Marica Leone
- Janssen Pharmaceutical Companies of Johnson & Johnson, Solna, Sweden (Leone, Leval); Department of Medical Epidemiology and Biostatistics (Leone, Kuja-Halkola, Leval, Butwicka, Zhang, Liu, Larsson, Bergen) and Department of Molecular Medicine and Surgery (Skov), Karolinska Institutet, Solna, Sweden; Child and Adolescent Psychiatry Stockholm, Stockholm Health Care Services, Region Stockholm, Sweden (Butwicka); Department of Child Psychiatry, Medical University of Warsaw, Warsaw (Butwicka); Department of Biostatistics and Translational Medicine, Medical University of Lodz, Lodz, Poland (Butwicka); Department of Medicine, Karlstad Central Hospital, Karlstad, Sweden (Skov); School of Medical Sciences, Örebro University, Örebro, Sweden (Larsson)
| | - Ralf Kuja-Halkola
- Janssen Pharmaceutical Companies of Johnson & Johnson, Solna, Sweden (Leone, Leval); Department of Medical Epidemiology and Biostatistics (Leone, Kuja-Halkola, Leval, Butwicka, Zhang, Liu, Larsson, Bergen) and Department of Molecular Medicine and Surgery (Skov), Karolinska Institutet, Solna, Sweden; Child and Adolescent Psychiatry Stockholm, Stockholm Health Care Services, Region Stockholm, Sweden (Butwicka); Department of Child Psychiatry, Medical University of Warsaw, Warsaw (Butwicka); Department of Biostatistics and Translational Medicine, Medical University of Lodz, Lodz, Poland (Butwicka); Department of Medicine, Karlstad Central Hospital, Karlstad, Sweden (Skov); School of Medical Sciences, Örebro University, Örebro, Sweden (Larsson)
| | - Amy Leval
- Janssen Pharmaceutical Companies of Johnson & Johnson, Solna, Sweden (Leone, Leval); Department of Medical Epidemiology and Biostatistics (Leone, Kuja-Halkola, Leval, Butwicka, Zhang, Liu, Larsson, Bergen) and Department of Molecular Medicine and Surgery (Skov), Karolinska Institutet, Solna, Sweden; Child and Adolescent Psychiatry Stockholm, Stockholm Health Care Services, Region Stockholm, Sweden (Butwicka); Department of Child Psychiatry, Medical University of Warsaw, Warsaw (Butwicka); Department of Biostatistics and Translational Medicine, Medical University of Lodz, Lodz, Poland (Butwicka); Department of Medicine, Karlstad Central Hospital, Karlstad, Sweden (Skov); School of Medical Sciences, Örebro University, Örebro, Sweden (Larsson)
| | - Agnieszka Butwicka
- Janssen Pharmaceutical Companies of Johnson & Johnson, Solna, Sweden (Leone, Leval); Department of Medical Epidemiology and Biostatistics (Leone, Kuja-Halkola, Leval, Butwicka, Zhang, Liu, Larsson, Bergen) and Department of Molecular Medicine and Surgery (Skov), Karolinska Institutet, Solna, Sweden; Child and Adolescent Psychiatry Stockholm, Stockholm Health Care Services, Region Stockholm, Sweden (Butwicka); Department of Child Psychiatry, Medical University of Warsaw, Warsaw (Butwicka); Department of Biostatistics and Translational Medicine, Medical University of Lodz, Lodz, Poland (Butwicka); Department of Medicine, Karlstad Central Hospital, Karlstad, Sweden (Skov); School of Medical Sciences, Örebro University, Örebro, Sweden (Larsson)
| | - Jakob Skov
- Janssen Pharmaceutical Companies of Johnson & Johnson, Solna, Sweden (Leone, Leval); Department of Medical Epidemiology and Biostatistics (Leone, Kuja-Halkola, Leval, Butwicka, Zhang, Liu, Larsson, Bergen) and Department of Molecular Medicine and Surgery (Skov), Karolinska Institutet, Solna, Sweden; Child and Adolescent Psychiatry Stockholm, Stockholm Health Care Services, Region Stockholm, Sweden (Butwicka); Department of Child Psychiatry, Medical University of Warsaw, Warsaw (Butwicka); Department of Biostatistics and Translational Medicine, Medical University of Lodz, Lodz, Poland (Butwicka); Department of Medicine, Karlstad Central Hospital, Karlstad, Sweden (Skov); School of Medical Sciences, Örebro University, Örebro, Sweden (Larsson)
| | - Ruyue Zhang
- Janssen Pharmaceutical Companies of Johnson & Johnson, Solna, Sweden (Leone, Leval); Department of Medical Epidemiology and Biostatistics (Leone, Kuja-Halkola, Leval, Butwicka, Zhang, Liu, Larsson, Bergen) and Department of Molecular Medicine and Surgery (Skov), Karolinska Institutet, Solna, Sweden; Child and Adolescent Psychiatry Stockholm, Stockholm Health Care Services, Region Stockholm, Sweden (Butwicka); Department of Child Psychiatry, Medical University of Warsaw, Warsaw (Butwicka); Department of Biostatistics and Translational Medicine, Medical University of Lodz, Lodz, Poland (Butwicka); Department of Medicine, Karlstad Central Hospital, Karlstad, Sweden (Skov); School of Medical Sciences, Örebro University, Örebro, Sweden (Larsson)
| | - Shengxin Liu
- Janssen Pharmaceutical Companies of Johnson & Johnson, Solna, Sweden (Leone, Leval); Department of Medical Epidemiology and Biostatistics (Leone, Kuja-Halkola, Leval, Butwicka, Zhang, Liu, Larsson, Bergen) and Department of Molecular Medicine and Surgery (Skov), Karolinska Institutet, Solna, Sweden; Child and Adolescent Psychiatry Stockholm, Stockholm Health Care Services, Region Stockholm, Sweden (Butwicka); Department of Child Psychiatry, Medical University of Warsaw, Warsaw (Butwicka); Department of Biostatistics and Translational Medicine, Medical University of Lodz, Lodz, Poland (Butwicka); Department of Medicine, Karlstad Central Hospital, Karlstad, Sweden (Skov); School of Medical Sciences, Örebro University, Örebro, Sweden (Larsson)
| | - Henrik Larsson
- Janssen Pharmaceutical Companies of Johnson & Johnson, Solna, Sweden (Leone, Leval); Department of Medical Epidemiology and Biostatistics (Leone, Kuja-Halkola, Leval, Butwicka, Zhang, Liu, Larsson, Bergen) and Department of Molecular Medicine and Surgery (Skov), Karolinska Institutet, Solna, Sweden; Child and Adolescent Psychiatry Stockholm, Stockholm Health Care Services, Region Stockholm, Sweden (Butwicka); Department of Child Psychiatry, Medical University of Warsaw, Warsaw (Butwicka); Department of Biostatistics and Translational Medicine, Medical University of Lodz, Lodz, Poland (Butwicka); Department of Medicine, Karlstad Central Hospital, Karlstad, Sweden (Skov); School of Medical Sciences, Örebro University, Örebro, Sweden (Larsson)
| | - Sarah E Bergen
- Janssen Pharmaceutical Companies of Johnson & Johnson, Solna, Sweden (Leone, Leval); Department of Medical Epidemiology and Biostatistics (Leone, Kuja-Halkola, Leval, Butwicka, Zhang, Liu, Larsson, Bergen) and Department of Molecular Medicine and Surgery (Skov), Karolinska Institutet, Solna, Sweden; Child and Adolescent Psychiatry Stockholm, Stockholm Health Care Services, Region Stockholm, Sweden (Butwicka); Department of Child Psychiatry, Medical University of Warsaw, Warsaw (Butwicka); Department of Biostatistics and Translational Medicine, Medical University of Lodz, Lodz, Poland (Butwicka); Department of Medicine, Karlstad Central Hospital, Karlstad, Sweden (Skov); School of Medical Sciences, Örebro University, Örebro, Sweden (Larsson)
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Mapping the genetic architecture of cortical morphology through neuroimaging: progress and perspectives. Transl Psychiatry 2022; 12:447. [PMID: 36241627 PMCID: PMC9568576 DOI: 10.1038/s41398-022-02193-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 09/06/2022] [Accepted: 09/20/2022] [Indexed: 11/26/2022] Open
Abstract
Cortical morphology is a key determinant of cognitive ability and mental health. Its development is a highly intricate process spanning decades, involving the coordinated, localized expression of thousands of genes. We are now beginning to unravel the genetic architecture of cortical morphology, thanks to the recent availability of large-scale neuroimaging and genomic data and the development of powerful biostatistical tools. Here, we review the progress made in this field, providing an overview of the lessons learned from genetic studies of cortical volume, thickness, surface area, and folding as captured by neuroimaging. It is now clear that morphology is shaped by thousands of genetic variants, with effects that are region- and time-dependent, thereby challenging conventional study approaches. The most recent genome-wide association studies have started discovering common genetic variants influencing cortical thickness and surface area, yet together these explain only a fraction of the high heritability of these measures. Further, the impact of rare variants and non-additive effects remains elusive. There are indications that the quickly increasing availability of data from whole-genome sequencing and large, deeply phenotyped population cohorts across the lifespan will enable us to uncover much of the missing heritability in the upcoming years. Novel approaches leveraging shared information across measures will accelerate this process by providing substantial increases in statistical power, together with more accurate mapping of genetic relationships. Important challenges remain, including better representation of understudied demographic groups, integration of other 'omics data, and mapping of effects from gene to brain to behavior across the lifespan.
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27
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Chung JY, Jung HU, Kim DJ, Baek EJ, Kim HK, Kang JO, Lim JE, Oh B. Identification of five genetic variants with differential effects on obesity-related traits based on age. Front Genet 2022; 13:970657. [PMID: 36276968 PMCID: PMC9585212 DOI: 10.3389/fgene.2022.970657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 09/13/2022] [Indexed: 11/13/2022] Open
Abstract
Obesity is a major public health concern, and its prevalence generally increases with age. As the number of elderly people is increasing in the aging population, the age-dependent increase in obesity has raised interest in the underlying mechanism. To understand the genetic basis of age-related increase in obesity, we identified genetic variants showing age-dependent differential effects on obesity. We conducted stratified analyses between young and old groups using genome-wide association studies of 355,335 United Kingom Biobank participants for five obesity-related phenotypes, including body mass index, body fat percentage, waist-hip ratio, waist circumference, and hip circumference. Using t-statistic, we identified five significant lead single nucleotide polymorphisms: rs2258461 with body mass index, rs9861311 and rs429358 with body fat percentage, rs2870099 with waist-hip ratio, and rs145500243 with waist circumference. Among these single nucleotide polymorphisms, rs429358, located in APOE gene was associated with diverse age-related diseases, such as Alzheimer’s disease, coronary artery disease, age-related degenerative macular diseases, and cognitive decline. The C allele of rs429358 gradually decreases body fat percentage as one grows older in the range of 40–69 years. In conclusion, we identified five genetic variants with differential effects on obesity-related phenotypes based on age using a stratified analysis between young and old groups, which may help to elucidate the mechanisms by which age influences the development of obesity.
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Affiliation(s)
- Ju Yeon Chung
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, Korea
| | - Hae-Un Jung
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, Korea
| | - Dong Jun Kim
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, Korea
| | - Eun Ju Baek
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, Korea
| | - Han Kyul Kim
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul, Korea
| | - Ji-One Kang
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul, Korea
| | - Ji Eun Lim
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul, Korea
- *Correspondence: Ji Eun Lim, ; Bermseok Oh,
| | - Bermseok Oh
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, Korea
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul, Korea
- *Correspondence: Ji Eun Lim, ; Bermseok Oh,
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28
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Ju D, Hui D, Hammond DA, Wonkam A, Tishkoff SA. Importance of Including Non-European Populations in Large Human Genetic Studies to Enhance Precision Medicine. Annu Rev Biomed Data Sci 2022; 5:321-339. [PMID: 35576557 PMCID: PMC9904154 DOI: 10.1146/annurev-biodatasci-122220-112550] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
One goal of genomic medicine is to uncover an individual's genetic risk for disease, which generally requires data connecting genotype to phenotype, as done in genome-wide association studies (GWAS). While there may be clinical promise to employing prediction tools such as polygenic risk scores (PRS), it currently stands that individuals of non-European ancestry may not reap the benefits of genomic medicine because of underrepresentation in large-scale genetics studies. Here, we discuss why this inequity poses a problem for genomic medicine and the reasons for the low transferability of PRS across populations. We also survey the ancestry representation of published GWAS and investigate how estimates of ancestry diversity in GWASparticipants might be biased. We highlight the importance of expanding genetic research in Africa, one of the most underrepresented regions in human genomics research, and discuss issues of ethics, resources, and technology for equitable advancement of genomic medicine.
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Affiliation(s)
- Dan Ju
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA;
| | - Daniel Hui
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA;
- Graduate Program in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Dorothy A Hammond
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA;
- Penn Center for Global Genomics & Health Equity, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Ambroise Wonkam
- Division of Human Genetics, Department of Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- Department of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland, USA;
| | - Sarah A Tishkoff
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA;
- Department of Biology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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29
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Bermudez-Lekerika P, Crump KB, Tseranidou S, Nüesch A, Kanelis E, Alminnawi A, Baumgartner L, Muñoz-Moya E, Compte R, Gualdi F, Alexopoulos LG, Geris L, Wuertz-Kozak K, Le Maitre CL, Noailly J, Gantenbein B. Immuno-Modulatory Effects of Intervertebral Disc Cells. Front Cell Dev Biol 2022; 10:924692. [PMID: 35846355 PMCID: PMC9277224 DOI: 10.3389/fcell.2022.924692] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 05/20/2022] [Indexed: 11/29/2022] Open
Abstract
Low back pain is a highly prevalent, chronic, and costly medical condition predominantly triggered by intervertebral disc degeneration (IDD). IDD is often caused by structural and biochemical changes in intervertebral discs (IVD) that prompt a pathologic shift from an anabolic to catabolic state, affecting extracellular matrix (ECM) production, enzyme generation, cytokine and chemokine production, neurotrophic and angiogenic factor production. The IVD is an immune-privileged organ. However, during degeneration immune cells and inflammatory factors can infiltrate through defects in the cartilage endplate and annulus fibrosus fissures, further accelerating the catabolic environment. Remarkably, though, catabolic ECM disruption also occurs in the absence of immune cell infiltration, largely due to native disc cell production of catabolic enzymes and cytokines. An unbalanced metabolism could be induced by many different factors, including a harsh microenvironment, biomechanical cues, genetics, and infection. The complex, multifactorial nature of IDD brings the challenge of identifying key factors which initiate the degenerative cascade, eventually leading to back pain. These factors are often investigated through methods including animal models, 3D cell culture, bioreactors, and computational models. However, the crosstalk between the IVD, immune system, and shifted metabolism is frequently misconstrued, often with the assumption that the presence of cytokines and chemokines is synonymous to inflammation or an immune response, which is not true for the intact disc. Therefore, this review will tackle immunomodulatory and IVD cell roles in IDD, clarifying the differences between cellular involvements and implications for therapeutic development and assessing models used to explore inflammatory or catabolic IVD environments.
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Affiliation(s)
- Paola Bermudez-Lekerika
- Tissue Engineering for Orthopaedics and Mechanobiology, Bone and Joint Program, Department for BioMedical Research (DBMR), Faculty of Medicine, University of Bern, Bern, Switzerland.,Department of Orthopaedic Surgery and Traumatology, Inselspital, Bern University Hospital, Medical Faculty, University of Bern, Bern, Switzerland
| | - Katherine B Crump
- Tissue Engineering for Orthopaedics and Mechanobiology, Bone and Joint Program, Department for BioMedical Research (DBMR), Faculty of Medicine, University of Bern, Bern, Switzerland.,Department of Orthopaedic Surgery and Traumatology, Inselspital, Bern University Hospital, Medical Faculty, University of Bern, Bern, Switzerland
| | | | - Andrea Nüesch
- Biomolecular Sciences Research Centre, Sheffield Hallam University, Sheffield, United Kingdom
| | - Exarchos Kanelis
- ProtATonce Ltd., Athens, Greece.,School of Mechanical Engineering, National Technical University of Athens, Zografou, Greece
| | - Ahmad Alminnawi
- GIGA In Silico Medicine, University of Liège, Liège, Belgium.,Skeletal Biology and Engineering Research Center, KU Leuven, Leuven, Belgium
| | | | | | - Roger Compte
- Twin Research and Genetic Epidemiology, St Thomas' Hospital, King's College London, London, United Kingdom
| | - Francesco Gualdi
- Institut Hospital Del Mar D'Investigacions Mèdiques (IMIM), Barcelona, Spain
| | - Leonidas G Alexopoulos
- ProtATonce Ltd., Athens, Greece.,School of Mechanical Engineering, National Technical University of Athens, Zografou, Greece
| | - Liesbet Geris
- GIGA In Silico Medicine, University of Liège, Liège, Belgium.,Skeletal Biology and Engineering Research Center, KU Leuven, Leuven, Belgium.,Biomechanics Research Unit, KU Leuven, Leuven, Belgium
| | - Karin Wuertz-Kozak
- Department of Biomedical Engineering, Rochester Institute of Technology, Rochester, NY, United States.,Spine Center, Schön Klinik München Harlaching Academic Teaching Hospital and Spine Research Institute of the Paracelsus Private Medical University Salzburg (Austria), Munich, Germany
| | - Christine L Le Maitre
- Biomolecular Sciences Research Centre, Sheffield Hallam University, Sheffield, United Kingdom
| | | | - Benjamin Gantenbein
- Tissue Engineering for Orthopaedics and Mechanobiology, Bone and Joint Program, Department for BioMedical Research (DBMR), Faculty of Medicine, University of Bern, Bern, Switzerland.,Department of Orthopaedic Surgery and Traumatology, Inselspital, Bern University Hospital, Medical Faculty, University of Bern, Bern, Switzerland
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30
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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.
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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
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31
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Li D, Kim W, An J, Kim S, Lee S, Do A, Kim W, Lee S, Yoon D, Lee K, Ha S, Silverman EK, Cho M, Shin C, Won S. Heritability Analyses Uncover Shared Genetic Effects of Lung Function and Change over Time. Genes (Basel) 2022; 13:genes13071261. [PMID: 35886044 PMCID: PMC9316642 DOI: 10.3390/genes13071261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 07/05/2022] [Accepted: 07/08/2022] [Indexed: 01/27/2023] Open
Abstract
Genetic influence on lung functions has been identified in previous studies; however, the relative longitudinal effects of genetic factors and their interactions with smoking on lung function remain unclear. Here, we identified the longitudinal effects of genetic variants on lung function by determining single nucleotide polymorphism (SNP) heritability and genetic correlations, and by analyzing interactions with smoking. Subject-specific means and annual change rates were calculated for eight spirometric measures obtained from 6622 Korean adults aged 40−69 years every two years for 14 years, and their heritabilities were estimated separately. Statistically significant (p < 0.05) heritability for the subject-specific means of all spirometric measures (8~32%) and change rates of forced expiratory volume in 1 s to forced vital capacity ratio (FEV1/FVC; 16%) and post-bronchodilator FEV1/FVC (17%) were detected. Significant genetic correlations of the change rate with the subject-specific mean were observed for FEV1/FVC (ρg = 0.64) and post-bronchodilator FEV1/FVC (ρg = 0.47). Furthermore, post-bronchodilator FEV1/FVC showed significant heritability of SNP-by-smoking interaction (hGXS2 = 0.4) for the annual change rate. The GWAS also detected genome-wide significant SNPs for FEV1 (rs4793538), FEV1/FVC (rs2704589, rs62201158, and rs9391733), and post-bronchodilator FEV1/FVC (rs2445936). We found statistically significant evidence of heritability role on the change in lung function, and this was shared with the effects on cross-sectional measurements. We also found some evidence of interaction with smoking for the change of lung function.
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Affiliation(s)
- Donghe Li
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea; (D.L.); (A.D.)
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, MA 02118, USA
| | - Woojin Kim
- Department of Internal Medicine and Environmental Health Center, School of Medicine, Kangwon National University, Chuncheon 24341, Korea;
| | - Jahoon An
- Department of Public Health Science, Graduate School of Public Health, Seoul National University, Seoul 08826, Korea;
| | - Soriul Kim
- Institute for Human Genomic Study, College of Medicine, Korea University, Seoul 136701, Korea; (S.K.); (S.L.)
| | - Seungku Lee
- Institute for Human Genomic Study, College of Medicine, Korea University, Seoul 136701, Korea; (S.K.); (S.L.)
| | - Ahra Do
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea; (D.L.); (A.D.)
| | - Wonji Kim
- Channing Division of Network Medicine and Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA; (W.K.); (E.K.S.); (M.C.)
| | - Sanghun Lee
- Department of Medical Consilience, Graduate School, Dankook University, Yongin 16890, Korea;
| | - Dankyu Yoon
- Division of Allergy and Respiratory Disease Research, Department of Chronic Disease Convergence Research, National Institute of Health, Korea Disease Control and Prevention Agency, Cheongju 28159, Korea;
| | - Kwangbae Lee
- Korea Medical Institute, Seoul 03173, Korea; (K.L.); (S.H.)
| | - Seounguk Ha
- Korea Medical Institute, Seoul 03173, Korea; (K.L.); (S.H.)
| | - Edwin K. Silverman
- Channing Division of Network Medicine and Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA; (W.K.); (E.K.S.); (M.C.)
| | - Michael Cho
- Channing Division of Network Medicine and Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA; (W.K.); (E.K.S.); (M.C.)
| | - Chol Shin
- Institute for Human Genomic Study, College of Medicine, Korea University, Seoul 136701, Korea; (S.K.); (S.L.)
- Division of Pulmonary Sleep and Critical Care Medicine, Department of Internal Medicine, Korea University Ansan Hospital, Ansan 15355, Korea
- Correspondence: (C.S.); (S.W.)
| | - Sungho Won
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea; (D.L.); (A.D.)
- Department of Public Health Science, Graduate School of Public Health, Seoul National University, Seoul 08826, Korea;
- Institute of Health and Environment, Seoul National University, Seoul 08826, Korea
- RexSoft Inc., Seoul 08826, Korea
- Correspondence: (C.S.); (S.W.)
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32
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Gershon ES, Lee SH, Zhou X, Sweeney JA, Tamminga C, Pearlson GA, Clementz BA, Keshavan MS, Alliey-Rodriguez N, Hudgens-Haney M, Keedy SK, Glahn DC, Asif H, Lencer R, Hill SK. An opportunity for primary prevention research in psychotic disorders. Schizophr Res 2022; 243:433-439. [PMID: 34315649 PMCID: PMC8784565 DOI: 10.1016/j.schres.2021.07.001] [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: 01/21/2021] [Revised: 04/29/2021] [Accepted: 07/01/2021] [Indexed: 10/20/2022]
Abstract
An opportunity has opened for research into primary prevention of psychotic disorders, based on progress in endophenotypes, genetics, and genomics. Primary prevention requires reliable prediction of susceptibility before any symptoms are present. We studied a battery of measures where published data supports abnormalities of these measurements prior to appearance of initial psychosis symptoms. These neurobiological and behavioral measurements included cognition, eye movement tracking, Event Related Potentials, and polygenic risk scores. They generated an acceptably precise separation of healthy controls from outpatients with a psychotic disorder. METHODS: The Bipolar and Schizophrenia Network on Intermediate Phenotypes (B-SNIP) measured this battery in an ancestry-diverse series of consecutively recruited adult outpatients with a psychotic disorder and healthy controls. Participants include all genders, 16 to 50 years of age, 261 with psychotic disorders (Schizophrenia (SZ) 109, Bipolar with psychosis (BPP) 92, Schizoaffective disorder (SAD) 60), 110 healthy controls. Logistic Regression, and an extension of the Linear Mixed Model to include analysis of pairwise interactions between measures (Environmental kernel Relationship Matrices (ERM)) with multiple iterations, were performed to predict case-control status. Each regression analysis was validated with four-fold cross-validation. RESULTS AND CONCLUSIONS: Sensitivity, specificity, and Area Under the Curve of Receiver Operating Characteristic of 85%, 62%, and 86%, respectively, were obtained for both analytic methods. These prediction metrics demonstrate a promising diagnostic distinction based on premorbid risk variables. There were also statistically significant pairwise interactions between measures in the ERM model. The strong prediction metrics of both types of analytic model provide proof-of-principle for biologically-based laboratory tests as a first step toward primary prevention studies. Prospective studies of adolescents at elevated risk, vs. healthy adolescent controls, would be a next step toward development of primary prevention strategies.
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Affiliation(s)
- Elliot S Gershon
- University of Chicago, Department of Psychiatry, United States of America; University of Chicago, Department of Human Genetics, United States of America.
| | - S Hong Lee
- Australian Centre for Precision Health, University of South Australia Cancer Research Institute, 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, South Australia 5000, Australia.
| | - Xuan Zhou
- Australian Centre for Precision Health, University of South Australia Cancer Research Institute, 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, South Australia 5000, Australia.
| | - John A Sweeney
- University of Cincinnati, Department of Psychiatry United States of America, Sichuan University, Hauxi Center for MR Research, China.
| | - Carol Tamminga
- University of Texas Southwestern, United States of America.
| | | | | | | | | | | | | | - David C Glahn
- Harvard Medical School, Boston Children's Hospital, United States of America.
| | - Huma Asif
- University of Chicago, United States of America.
| | - Rebekka Lencer
- University of Muenster, Muenster, Germany; Department of Psychiatry and Psychotherapy, University of Luebeck, Luebeck, Germany.
| | - S Kristian Hill
- Rosalind Franklin University of Medicine and Science, United States of America.
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Li M, Zhang YW, Zhang ZC, Xiang Y, Liu MH, Zhou YH, Zuo JF, Zhang HQ, Chen Y, Zhang YM. A compressed variance component mixed model for detecting QTNs and QTN-by-environment and QTN-by-QTN interactions in genome-wide association studies. MOLECULAR PLANT 2022; 15:630-650. [PMID: 35202864 DOI: 10.1016/j.molp.2022.02.012] [Citation(s) in RCA: 50] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2021] [Revised: 01/26/2022] [Accepted: 02/19/2022] [Indexed: 05/25/2023]
Abstract
Although genome-wide association studies are widely used to mine genes for quantitative traits, the effects to be estimated are confounded, and the methodologies for detecting interactions are imperfect. To address these issues, the mixed model proposed here first estimates the genotypic effects for AA, Aa, and aa, and the genotypic polygenic background replaces additive and dominance polygenic backgrounds. Then, the estimated genotypic effects are partitioned into additive and dominance effects using a one-way analysis of variance model. This strategy was further expanded to cover QTN-by-environment interactions (QEIs) and QTN-by-QTN interactions (QQIs) using the same mixed-model framework. Thus, a three-variance-component mixed model was integrated with our multi-locus random-SNP-effect mixed linear model (mrMLM) method to establish a new methodological framework, 3VmrMLM, that detects all types of loci and estimates their effects. In Monte Carlo studies, 3VmrMLM correctly detected all types of loci and almost unbiasedly estimated their effects, with high powers and accuracies and a low false positive rate. In re-analyses of 10 traits in 1439 rice hybrids, detection of 269 known genes, 45 known gene-by-environment interactions, and 20 known gene-by-gene interactions strongly validated 3VmrMLM. Further analyses of known genes showed more small (67.49%), minor-allele-frequency (35.52%), and pleiotropic (30.54%) genes, with higher repeatability across datasets (54.36%) and more dominance loci. In addition, a heteroscedasticity mixed model in multiple environments and dimension reduction methods in quite a number of environments were developed to detect QEIs, and variable selection under a polygenic background was proposed for QQI detection. This study provides a new approach for revealing the genetic architecture of quantitative traits.
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Affiliation(s)
- Mei Li
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Ya-Wen Zhang
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China; State Key Laboratory of Cotton Biology, Anyang 455000, China
| | - Ze-Chang Zhang
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Yu Xiang
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Ming-Hui Liu
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Ya-Hui Zhou
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Jian-Fang Zuo
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Han-Qing Zhang
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Ying Chen
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Yuan-Ming Zhang
- Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China.
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Gong H, Rehman F, Li Z, Liu J, Yang T, Liu J, Li H, Hu Z, Ma Q, Wu Z, A B, Yang M, Gao H, Zhi H, Qu H, Di D, Wang Y. Discrimination of Geographical Origins of Wolfberry ( Lycium barbarum L.) Fruits Using Stable Isotopes, Earth Elements, Free Amino Acids, and Saccharides. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2022; 70:2984-2997. [PMID: 35179024 DOI: 10.1021/acs.jafc.1c06207] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
To develop sophisticated approaches for distinguishing goji origins, 325 wolfberry fruit samples of a certain cultivar, plant age, drying method, and collection season were gathered from 26 producing areas across Northwest China in 2017 and 2018. We employed 49 indices, including stable isotopes, earth elements, soluble amino acids, and saccharides, to identify the regions of origin of these goji fruits. Analysis of variance (ANOVA) and heritability analysis were used to assess the effects of the environment (producing areas), cultivar, plant age, drying process, and collection season. Samples from the same place can be classified and partially discriminated using principal component analysis (PCA). We were able to distinguish fruits produced in Zhongning County from those produced in the other five producing provinces using orthogonal projection to latent structure-discriminant analysis (OPLS-DA). Calcium (Ca), manganese (Mn), ornithine (Orn), cystine (Cys-Cys), glutamate (Glu), phenylalanine (Phe), phosphoserine (Ps), serine (Ser), lysine (Lys), taurine (Tau), proline (Pro), and tyrosine (Tyr) indices were chosen using S-plots and heritability analysis, and their repeatability was established with samples collected in 2018. The indices selected in this study can distinguish goji berries produced in Zhongning County from fruits originating from five other Provinces with high repeatability, which was validated with various cultivars, drying methods, harvest seasons, and plant ages and with heritability analysis.
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Affiliation(s)
- Haiguang Gong
- Key Laboratory of South China Agricultural Plant Molecular Analysis and Genetic Improvement, Provincial Key Laboratory of Digital Botanical Garden and Public Science, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou 510650, P. R. China
| | - Fazal Rehman
- Key Laboratory of South China Agricultural Plant Molecular Analysis and Genetic Improvement, Provincial Key Laboratory of Digital Botanical Garden and Public Science, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou 510650, P. R. China
| | - Zhong Li
- Bairuiyuan Company, Yinchuan 750000, P. R. China
| | - Jianfei Liu
- CAS Key Laboratory of Chemistry of Northwestern Plant Resources and Key Laboratory for Natural Medicine of Gansu Province, Lanzhou Institute of Chemical Physics, Chinese Academy of Sciences (CAS), Lanzhou 730000, P. R. China
| | - Tianshun Yang
- Key Laboratory of South China Agricultural Plant Molecular Analysis and Genetic Improvement, Provincial Key Laboratory of Digital Botanical Garden and Public Science, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou 510650, P. R. China
| | - Juan Liu
- Zhongning County Goji Industry Development Service Bureau, Zhongwei 755100, Ningxia, P. R. China
| | - Haoran Li
- Zhongning County Goji Industry Development Service Bureau, Zhongwei 755100, Ningxia, P. R. China
| | - Zhongqing Hu
- Zhongning County Goji Industry Development Service Bureau, Zhongwei 755100, Ningxia, P. R. China
| | - Qihu Ma
- Beijing TongRenTang Health-Pharmaceutical (Ningxia) Co., Ltd., Yinchuan 750000, Ningxia, P. R. China
| | - Zhigeng Wu
- Key Laboratory of South China Agricultural Plant Molecular Analysis and Genetic Improvement, Provincial Key Laboratory of Digital Botanical Garden and Public Science, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou 510650, P. R. China
| | - Biao A
- Key Laboratory of South China Agricultural Plant Molecular Analysis and Genetic Improvement, Provincial Key Laboratory of Digital Botanical Garden and Public Science, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou 510650, P. R. China
| | - Meizhen Yang
- Key Laboratory of South China Agricultural Plant Molecular Analysis and Genetic Improvement, Provincial Key Laboratory of Digital Botanical Garden and Public Science, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou 510650, P. R. China
| | - Hao Gao
- Institute of Traditional Chinese Medicine and Natural Products, College of Pharmacy/Guangdong Province Key Laboratory of Pharmacodynamic Constituents of TCM and New Drugs Research, Jinan University, Guangzhou 510632, P. R. China
| | - Hui Zhi
- School of Chinese Materia Medica, Guangzhou University of Chinese Medicine, Guangzhou 510006, P. R. China
| | - Hongxia Qu
- Key Laboratory of South China Agricultural Plant Molecular Analysis and Genetic Improvement, Provincial Key Laboratory of Digital Botanical Garden and Public Science, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou 510650, P. R. China
| | - Duolong Di
- CAS Key Laboratory of Chemistry of Northwestern Plant Resources and Key Laboratory for Natural Medicine of Gansu Province, Lanzhou Institute of Chemical Physics, Chinese Academy of Sciences (CAS), Lanzhou 730000, P. R. China
- Center of Resource Chemical and New Material, Lanzhou Institute of Chemical Physics, Chinese Academy of Sciences, Qingdao 266100, P. R. China
| | - Ying Wang
- Key Laboratory of South China Agricultural Plant Molecular Analysis and Genetic Improvement, Provincial Key Laboratory of Digital Botanical Garden and Public Science, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou 510650, P. R. China
- Gannan Normal University, Ganzhou, Jinagxi 341000, P. R. China
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35
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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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36
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Anguita-Ruiz A, Zarza-Rebollo JA, Pérez-Gutiérrez AM, Molina E, Gutiérrez B, Bellón JÁ, Moreno-Peral P, Conejo-Cerón S, Aiarzagüena JM, Ballesta-Rodríguez MI, Fernández A, Fernández-Alonso C, Martín-Pérez C, Montón-Franco C, Rodríguez-Bayón A, Torres-Martos Á, López-Isac E, Cervilla J, Rivera M. Body mass index interacts with a genetic-risk score for depression increasing the risk of the disease in high-susceptibility individuals. Transl Psychiatry 2022; 12:30. [PMID: 35075110 PMCID: PMC8786870 DOI: 10.1038/s41398-022-01783-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 11/24/2021] [Accepted: 01/04/2022] [Indexed: 11/22/2022] Open
Abstract
Depression is strongly associated with obesity among other chronic physical diseases. The latest mega- and meta-analysis of genome-wide association studies have identified multiple risk loci robustly associated with depression. In this study, we aimed to investigate whether a genetic-risk score (GRS) combining multiple depression risk single nucleotide polymorphisms (SNPs) might have utility in the prediction of this disorder in individuals with obesity. A total of 30 depression-associated SNPs were included in a GRS to predict the risk of depression in a large case-control sample from the Spanish PredictD-CCRT study, a national multicentre, randomized controlled trial, which included 104 cases of depression and 1546 controls. An unweighted GRS was calculated as a summation of the number of risk alleles for depression and incorporated into several logistic regression models with depression status as the main outcome. Constructed models were trained and evaluated in the whole recruited sample. Non-genetic-risk factors were combined with the GRS in several ways across the five predictive models in order to improve predictive ability. An enrichment functional analysis was finally conducted with the aim of providing a general understanding of the biological pathways mapped by analyzed SNPs. We found that an unweighted GRS based on 30 risk loci was significantly associated with a higher risk of depression. Although the GRS itself explained a small amount of variance of depression, we found a significant improvement in the prediction of depression after including some non-genetic-risk factors into the models. The highest predictive ability for depression was achieved when the model included an interaction term between the GRS and the body mass index (BMI), apart from the inclusion of classical demographic information as marginal terms (AUC = 0.71, 95% CI = [0.65, 0.76]). Functional analyses on the 30 SNPs composing the GRS revealed an over-representation of the mapped genes in signaling pathways involved in processes such as extracellular remodeling, proinflammatory regulatory mechanisms, and circadian rhythm alterations. Although the GRS on its own explained a small amount of variance of depression, a significant novel feature of this study is that including non-genetic-risk factors such as BMI together with a GRS came close to the conventional threshold for clinical utility used in ROC analysis and improves the prediction of depression. In this study, the highest predictive ability was achieved by the model combining the GRS and the BMI under an interaction term. Particularly, BMI was identified as a trigger-like risk factor for depression acting in a concerted way with the GRS component. This is an interesting finding since it suggests the existence of a risk overlap between both diseases, and the need for individual depression genetics-risk evaluation in subjects with obesity. This research has therefore potential clinical implications and set the basis for future research directions in exploring the link between depression and obesity-associated disorders. While it is likely that future genome-wide studies with large samples will detect novel genetic variants associated with depression, it seems clear that a combination of genetics and non-genetic information (such is the case of obesity status and other depression comorbidities) will still be needed for the optimization prediction of depression in high-susceptibility individuals.
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Affiliation(s)
- Augusto Anguita-Ruiz
- grid.4489.10000000121678994Department of Biochemistry and Molecular Biology II, Faculty of Pharmacy, University of Granada, Granada, Spain ,grid.4489.10000000121678994Institute of Nutrition and Food Technology “José Mataix”, Biomedical Research Center (CIBM), University of Granada, Granada, Spain ,grid.507088.2Instituto de Investigación Biosanitaria ibs.GRANADA, Granada, Spain ,grid.413448.e0000 0000 9314 1427CIBEROBN (Physiopathology of Obesity and Nutrition CB12/03/30038), Institute of Health Carlos III (ISCIII), Madrid, Spain
| | - Juan Antonio Zarza-Rebollo
- Department of Biochemistry and Molecular Biology II, Faculty of Pharmacy, University of Granada, Granada, Spain. .,Institute of Neurosciences 'Federico Olóriz', Biomedical Research Center (CIBM), University of Granada, Granada, Spain.
| | - Ana M Pérez-Gutiérrez
- grid.4489.10000000121678994Department of Biochemistry and Molecular Biology II, Faculty of Pharmacy, University of Granada, Granada, Spain ,grid.4489.10000000121678994Institute of Neurosciences ‘Federico Olóriz’, Biomedical Research Center (CIBM), University of Granada, Granada, Spain
| | - Esther Molina
- grid.507088.2Instituto de Investigación Biosanitaria ibs.GRANADA, Granada, Spain ,grid.4489.10000000121678994Institute of Neurosciences ‘Federico Olóriz’, Biomedical Research Center (CIBM), University of Granada, Granada, Spain ,grid.4489.10000000121678994Department of Nursing, Faculty of Health Sciences, University of Granada, Granada, Spain
| | - Blanca Gutiérrez
- grid.507088.2Instituto de Investigación Biosanitaria ibs.GRANADA, Granada, Spain ,grid.4489.10000000121678994Institute of Neurosciences ‘Federico Olóriz’, Biomedical Research Center (CIBM), University of Granada, Granada, Spain ,grid.4489.10000000121678994Department of Psychiatry, Faculty of Medicine, University of Granada, Granada, Spain
| | - Juan Ángel Bellón
- grid.452525.1Primary Care District of Málaga-Guadalhorce, Biomedical Research Institute of Málaga (IBIMA), Primary Care Prevention and Health Promotion Network (redIAPP), Málaga, Spain ,grid.10215.370000 0001 2298 7828Department of Public Health and Psychiatry, Faculty of Medicine, University of Málaga, Málaga, Spain
| | - Patricia Moreno-Peral
- grid.452525.1Primary Care District of Málaga-Guadalhorce, Biomedical Research Institute of Málaga (IBIMA), Primary Care Prevention and Health Promotion Network (redIAPP), Málaga, Spain
| | - Sonia Conejo-Cerón
- grid.452525.1Primary Care District of Málaga-Guadalhorce, Biomedical Research Institute of Málaga (IBIMA), Primary Care Prevention and Health Promotion Network (redIAPP), Málaga, Spain
| | | | | | - Anna Fernández
- grid.428876.7Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, Barcelona, Spain ,grid.466571.70000 0004 1756 6246CIBERESP, Centro de Investigacion Biomedica en Red de Epidemiologia y Salud Publica, Madrid, Spain
| | | | - Carlos Martín-Pérez
- grid.418355.eMarquesado Health Centre, Servicio Andaluz de Salud, Granada, Spain
| | - Carmen Montón-Franco
- grid.488737.70000000463436020Casablanca Health Centre, Aragonese Institute of Health Sciences, IIS Aragón, Zaragoza, Spain ,grid.11205.370000 0001 2152 8769Department of Medicine and Psychiatry, University of Zaragoza, Zaragoza, Spain
| | | | - Álvaro Torres-Martos
- grid.4489.10000000121678994Department of Biochemistry and Molecular Biology II, Faculty of Pharmacy, University of Granada, Granada, Spain
| | - Elena López-Isac
- grid.4489.10000000121678994Department of Biochemistry and Molecular Biology II, Faculty of Pharmacy, University of Granada, Granada, Spain ,grid.4489.10000000121678994Institute of Neurosciences ‘Federico Olóriz’, Biomedical Research Center (CIBM), University of Granada, Granada, Spain
| | - Jorge Cervilla
- grid.507088.2Instituto de Investigación Biosanitaria ibs.GRANADA, Granada, Spain ,grid.4489.10000000121678994Institute of Neurosciences ‘Federico Olóriz’, Biomedical Research Center (CIBM), University of Granada, Granada, Spain ,grid.4489.10000000121678994Department of Psychiatry, Faculty of Medicine, University of Granada, Granada, Spain
| | - Margarita Rivera
- grid.4489.10000000121678994Department of Biochemistry and Molecular Biology II, Faculty of Pharmacy, University of Granada, Granada, Spain ,grid.507088.2Instituto de Investigación Biosanitaria ibs.GRANADA, Granada, Spain ,grid.4489.10000000121678994Institute of Neurosciences ‘Federico Olóriz’, Biomedical Research Center (CIBM), University of Granada, Granada, Spain
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Shin J, Zhou X, Tan JTM, Hyppönen E, Benyamin B, Lee SH. Lifestyle Modifies the Diabetes-Related Metabolic Risk, Conditional on Individual Genetic Differences. Front Genet 2022; 13:759309. [PMID: 35356427 PMCID: PMC8959634 DOI: 10.3389/fgene.2022.759309] [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: 08/17/2021] [Accepted: 01/10/2022] [Indexed: 12/26/2022] Open
Abstract
Metabolic syndrome is a group of heritable metabolic traits that are highly associated with type 2 diabetes (T2DM). Classical interventions to T2DM include individual self-management of environmental risk factors, such as improving diet quality, increasing physical activity, and reducing smoking and alcohol consumption, which decreases the risk of developing metabolic syndrome. However, it is poorly understood how the phenotypes of diabetes-related metabolic traits change with respect to lifestyle modifications at the individual level. In the analysis, we used 12 diabetes-related metabolic traits and eight lifestyle covariates from the UK Biobank comprising 288,837 white British participants genotyped for 1,133,273 genome-wide single nucleotide polymorphisms. We found 16 GxE interactions. Modulation of genetic effects by physical activity was seen for four traits (glucose, HbA1c, C-reactive protein, systolic blood pressure) and by alcohol and smoking for three (BMI, glucose, waist-hip ratio and BMI and diastolic and systolic blood pressure, respectively). We also found a number of significant phenotypic modulations by the lifestyle covariates, which were not attributed to the genetic effects in the model. Overall, modulation in the metabolic risk in response to the level of lifestyle covariates was clearly observed, and its direction and magnitude were varied depending on individual differences. We also showed that the metabolic risk inferred by our model was notably higher in T2DM prospective cases than controls. Our findings highlight the importance of individual genetic differences in the prevention and management of diabetes and suggest that the one-size-fits-all approach may not benefit all.
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Affiliation(s)
- Jisu Shin
- Australian Centre for Precision Health, University of South Australia Cancer Research Institute, University of South Australia, Adelaide, SA, Australia.,UniSA Allied Health and Human Performance, University of South Australia, Adelaide, SA, Australia.,National Cancer Center, Goyang-si, South Korea
| | - Xuan Zhou
- Australian Centre for Precision Health, University of South Australia Cancer Research Institute, University of South Australia, Adelaide, SA, Australia.,UniSA Allied Health and Human Performance, University of South Australia, Adelaide, SA, Australia
| | - Joanne T M Tan
- Vascular Research Centre, Heart and Vascular Health Program, Lifelong Health Theme, South Australian Health and Medical Research Institute, Adelaide, SA, Australia.,Adelaide Medical School, University of Adelaide, Adelaide, SA, Australia
| | - Elina Hyppönen
- Australian Centre for Precision Health, University of South Australia Cancer Research Institute, 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
| | - Beben Benyamin
- Australian Centre for Precision Health, University of South Australia Cancer Research Institute, University of South Australia, Adelaide, SA, Australia.,UniSA Allied Health and Human Performance, University of South Australia, Adelaide, SA, Australia.,South Australian Health and Medical Research Institute, Adelaide, SA, Australia
| | - S Hong Lee
- Australian Centre for Precision Health, University of South Australia Cancer Research Institute, University of South Australia, Adelaide, SA, Australia.,UniSA Allied Health and Human Performance, University of South Australia, Adelaide, SA, Australia.,South Australian Health and Medical Research Institute, Adelaide, SA, Australia
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38
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O'Nunain K, Sanderson E, Holmes M, Davey Smith G, Richardson T. A genome-wide association study of childhood adiposity and blood lipids. Wellcome Open Res 2021. [DOI: 10.12688/wellcomeopenres.16928.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Background: The rising prevalence of childhood obesity and dyslipidaemia is a major public health concern due to its association with morbidity and mortality in later life. Methods: In this study, we have conducted genome-wide association studies (GWAS) for eight measures of adiposity and lipids in a cohort of young individuals (mean age 9.9) from the Avon Longitudinal Study of Parents and Children (ALSPAC). These measures were body mass index (BMI), systolic and diastolic blood pressure, high- density and low-density lipoprotein cholesterol, triglycerides, apolipoprotein A-I and apolipoprotein B. We next undertook functional enrichment, pathway analyses and linkage disequilibrium (LD) score regression to evaluate genetic correlations with later-life cardiometabolic diseases. Results: Using GWAS we identified 14 unique loci associated with at least one risk factor in this cohort of age 10 individuals (P<5x10-8), with lipoprotein lipid-associated loci being enriched for liver tissue-derived gene expression and lipid synthesis pathways. LD score regression provided evidence of various genetic correlations, such as childhood systolic blood pressure being genetically correlated with later-life coronary artery disease (rG=0.26, 95% CI=0.07 to 0.46, P=0.009) and hypertension (rG=0.37, 95% CI=0.19 to 0.55, P=6.57x10-5), as well as childhood BMI with type 2 diabetes (rG=0.35, 95% CI=0.18 to 0.51, P=3.28x10-5). Conclusions: Our findings suggest that there are genetic variants inherited at birth which begin to exert their effects on cardiometabolic risk factors as early as age 10 in the life course. However, further research is required to assess whether the genetic correlations we have identified are due to direct or indirect effects of childhood adiposity and lipid traits.
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39
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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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40
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Borisevich D, Schnurr TM, Engelbrechtsen L, Rakitko A, Ängquist L, Ilinsky V, Aadahl M, Grarup N, Pedersen O, Sørensen TIA, Hansen T. Non-linear interaction between physical activity and polygenic risk score of body mass index in Danish and Russian populations. PLoS One 2021; 16:e0258748. [PMID: 34662357 PMCID: PMC8523041 DOI: 10.1371/journal.pone.0258748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Accepted: 10/04/2021] [Indexed: 11/19/2022] Open
Abstract
Body mass index (BMI) is a highly heritable polygenic trait. It is also affected by various environmental and behavioral risk factors. We used a BMI polygenic risk score (PRS) to study the interplay between the genetic and environmental factors defining BMI. First, we generated a BMI PRS that explained more variance than a BMI genetic risk score (GRS), which was using only genome-wide significant BMI-associated variants (R2 = 13.1% compared to 6.1%). Second, we analyzed interactions between BMI PRS and seven environmental factors. We found a significant interaction between physical activity and BMI PRS, even when the well-known effect of the FTO region was excluded from the PRS, using a small dataset of 6,179 samples. Third, we stratified the study population into two risk groups using BMI PRS. The top 22% of the studied populations were included in a high PRS risk group. Engagement in self-reported physical activity was associated with a 1.66 kg/m2 decrease in BMI in this group, compared to a 0.84 kg/m2 decrease in BMI in the rest of the population. Our results (i) confirm that genetic background strongly affects adult BMI in the general population, (ii) show a non-linear interaction between BMI genetics and physical activity, and (iii) provide a standardized framework for future gene-environment interaction analyses.
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Affiliation(s)
- Dmitrii Borisevich
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- * E-mail:
| | - Theresia M. Schnurr
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Line Engelbrechtsen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Gynecology and Obstetrics, Herlev Hospital, Herlev, Denmark
| | | | - Lars Ängquist
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | | | - Mette Aadahl
- Center for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, Frederiksberg, Denmark
- Department of Public Health, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Niels Grarup
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Oluf Pedersen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Thorkild I. A. Sørensen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Public Health, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Torben Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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41
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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.
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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
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42
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Amador C, Zeng Y, Barber M, Walker RM, Campbell A, McIntosh AM, Evans KL, Porteous DJ, Hayward C, Wilson JF, Navarro P, Haley CS. Genome-wide methylation data improves dissection of the effect of smoking on body mass index. PLoS Genet 2021; 17:e1009750. [PMID: 34499657 PMCID: PMC8428545 DOI: 10.1371/journal.pgen.1009750] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Accepted: 07/28/2021] [Indexed: 11/18/2022] Open
Abstract
Variation in obesity-related traits has a genetic basis with heritabilities between 40 and 70%. While the global obesity pandemic is usually associated with environmental changes related to lifestyle and socioeconomic changes, most genetic studies do not include all relevant environmental covariates, so the genetic contribution to variation in obesity-related traits cannot be accurately assessed. Some studies have described interactions between a few individual genes linked to obesity and environmental variables but there is no agreement on their total contribution to differences between individuals. Here we compared self-reported smoking data and a methylation-based proxy to explore the effect of smoking and genome-by-smoking interactions on obesity related traits from a genome-wide perspective to estimate the amount of variance they explain. Our results indicate that exploiting omic measures can improve models for complex traits such as obesity and can be used as a substitute for, or jointly with, environmental records to better understand causes of disease.
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Affiliation(s)
- Carmen Amador
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom
| | - Yanni Zeng
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom
- Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-Sen University, China
| | - Michael Barber
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom
| | - Rosie M. Walker
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom
- Centre for Clinical Brain Sciences, Chancellor’s Building, 49 Little France Crescent, Edinburgh BioQuarter, Edinburgh, United Kingdom
| | - Archie Campbell
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom
| | - Andrew M. McIntosh
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
| | - Kathryn L. Evans
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom
| | - David J. Porteous
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom
| | - Caroline Hayward
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom
| | - James F. Wilson
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Pau Navarro
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom
| | - Chris S. Haley
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom
- Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, United Kingdom
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43
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Hiraike Y, Yang CT, Liu WJ, Yamada T, Lee CL. FTO Obesity Variant-Exercise Interaction on Changes in Body Weight and BMI: The Taiwan Biobank Study. J Clin Endocrinol Metab 2021; 106:e3673-e3681. [PMID: 33929497 DOI: 10.1210/clinem/dgab295] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Indexed: 02/05/2023]
Abstract
CONTEXT Gene-exercise interaction on cross-sectional body mass index (BMI) has been extensively studied and is well established. However, gene-exercise interaction on changes in body weight/BMI remains controversial. OBJECTIVE To examine the interaction between the FTO obesity variant and regular exercise on changes in body weight/BMI. PARTICIPANTS Taiwan Biobank participants aged 30-70 years (N = 20 906) were examined at both baseline and follow-up visit (mean follow-up duration: 3.7 years). MAIN OUTCOME MEASURES The interaction between the FTO obesity variant rs1421085 and regular exercise habit (no exercise, ≤20 metabolic equivalent of tasks (METs)/week exercise, >20 METs/week exercise) on changes in body weight/BMI. RESULTS Individuals with the risk allele of rs1421085 gained more weight and increased BMI than those without the risk allele if they did not exercise. In contrast, individuals with the risk allele gained less weight and BMI if they exercised regularly, indicating an interaction between rs1421085 and regular exercise habit (P = .030 for Δbody weight and P = .034 for ΔBMI). The effect of exercise on maintaining body weight was larger in those with the risk allele of rs1421085. When we focused on individuals without regular exercise at baseline, individuals with the risk allele again tended to lose more weight than those with a nonrisk allele if they had acquired an exercise habit by the follow-up visit. CONCLUSION The beneficial effect of exercise is greater in individuals genetically prone to obesity due to the interaction between the FTO obesity variant rs1421085 and regular exercise on changes in body weight and BMI.
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Affiliation(s)
- Yuta Hiraike
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02115, USA
- Department of Cell Biology, Harvard Medical School , Boston, MA 02115, USA
| | - Chao-Tung Yang
- Department of Computer Science, Tunghai University, Taichung, Taiwan
- Research Center for Smart Sustainable Circular Economy, Tunghai University, Taiwan
- Research Center for Nanotechnology, Tunghai University, Taiwan
| | - Wei-Ju Liu
- Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Tomohide Yamada
- Institute of Population Health, King's College London, London, UK
| | - Chia-Lin Lee
- Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
- Department of Medicine, School of Medicine, National Yang-Ming Chiao Tung University, Taipei, Taiwan
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44
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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.
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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
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45
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Shin J, Lee SH. GxEsum: a novel approach to estimate the phenotypic variance explained by genome-wide GxE interaction based on GWAS summary statistics for biobank-scale data. Genome Biol 2021; 22:183. [PMID: 34154633 PMCID: PMC8218431 DOI: 10.1186/s13059-021-02403-1] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Accepted: 06/04/2021] [Indexed: 12/14/2022] Open
Abstract
Genetic variation in response to the environment, that is, genotype-by-environment interaction (GxE), is fundamental in the biology of complex traits and diseases. However, existing methods are computationally demanding and infeasible to handle biobank-scale data. Here, we introduce GxEsum, a method for estimating the phenotypic variance explained by genome-wide GxE based on GWAS summary statistics. Through comprehensive simulations and analysis of UK Biobank with 288,837 individuals, we show that GxEsum can handle a large-scale biobank dataset with controlled type I error rates and unbiased GxE estimates, and its computational efficiency can be hundreds of times higher than existing GxE methods.
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Affiliation(s)
- Jisu Shin
- Australian Centre for Precision Health, University of South Australia Cancer Research Institute, University of South Australia, Adelaide, SA, 5000, Australia
- UniSA: Allied Health and Human Performance, University of South Australia, Adelaide, SA, 5000, Australia
| | - Sang Hong Lee
- Australian Centre for Precision Health, University of South Australia Cancer Research Institute, University of South Australia, Adelaide, SA, 5000, Australia.
- UniSA: Allied Health and Human Performance, University of South Australia, Adelaide, SA, 5000, Australia.
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46
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Admixture/fine-mapping in Brazilians reveals a West African associated potential regulatory variant (rs114066381) with a strong female-specific effect on body mass and fat mass indexes. Int J Obes (Lond) 2021; 45:1017-1029. [PMID: 33633342 PMCID: PMC9952852 DOI: 10.1038/s41366-021-00761-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Revised: 11/21/2020] [Accepted: 01/20/2021] [Indexed: 11/08/2022]
Abstract
BACKGROUND/OBJECTIVES Admixed populations are a resource to study the global genetic architecture of complex phenotypes, which is critical, considering that non-European populations are severely underrepresented in genomic studies. Here, we study the genetic architecture of BMI in children, young adults, and elderly individuals from the admixed population of Brazil. SUBJECTS/METHODS Leveraging admixture in Brazilians, whose chromosomes are mosaics of fragments of Native American, European, and African origins, we used genome-wide data to perform admixture mapping/fine-mapping of body mass index (BMI) in three Brazilian population-based cohorts from Northeast (Salvador), Southeast (Bambuí), and South (Pelotas). RESULTS We found significant associations with African-associated alleles in children from Salvador (PALD1 and ZMIZ1 genes), and in young adults from Pelotas (NOD2 and MTUS2 genes). More importantly, in Pelotas, rs114066381, mapped in a potential regulatory region, is significantly associated only in females (p = 2.76e-06). This variant is rare in Europeans but with frequencies of ~3% in West Africa and has a strong female-specific effect (95% CI: 2.32-5.65 kg/m2 per each A allele). We confirmed this sex-specific association and replicated its strong effect for an adjusted fat mass index in the same Pelotas cohort, and for BMI in another Brazilian cohort from São Paulo (Southeast Brazil). A meta-analysis confirmed the significant association. Remarkably, we observed that while the frequency of rs114066381-A allele ranges from 0.8 to 2.1% in the studied populations, it attains ~9% among women with morbid obesity from Pelotas, São Paulo, and Bambuí. The effect size of rs114066381 is at least five times higher than the FTO SNPs rs9939609 and rs1558902, already emblematic for their high effects. CONCLUSIONS We identified six candidate SNPs associated with BMI. rs114066381 stands out for its high effect that was replicated and its high frequency in women with morbid obesity. We demonstrate how admixed populations are a source of new relevant phenotype-associated genetic variants.
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47
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Loos RJF, Burant C, Schur EA. Strategies to Understand the Weight-Reduced State: Genetics and Brain Imaging. Obesity (Silver Spring) 2021; 29 Suppl 1:S39-S50. [PMID: 33759393 PMCID: PMC8500189 DOI: 10.1002/oby.23101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Revised: 12/03/2020] [Accepted: 12/03/2020] [Indexed: 11/09/2022]
Abstract
Most individuals with obesity or overweight have difficulty maintaining weight loss. The weight-reduced state induces changes in many physiological processes that appear to drive weight regain. Here, we review the use of cell biology, genetics, and imaging techniques that are being used to begin understanding why weight regain is the normal response to dieting. As with obesity itself, weight regain has both genetic and environmental drivers. Genetic drivers for "thinness" and "obesity" largely overlap, but there is evidence for specific genetic loci that are different for each of these weight states. There is only limited information regarding the genetics of weight regain. Currently, most genetic loci related to weight point to the central nervous system as the organ responsible for determining the weight set point. Neuroimaging tools have proved useful in studying the contribution of the central nervous system to the weight-reduced state in humans. Neuroimaging technologies fall into three broad categories: functional, connectivity, and structural neuroimaging. Connectivity and structural imaging techniques offer unique opportunities for testing mechanistic hypotheses about changes in brain function or tissue structure in the weight-reduced state.
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Affiliation(s)
- Ruth J. F. Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Charles Burant
- Department of Internal Medicine, University of Washington, Seattle, Washington, USA
| | - Ellen A. Schur
- Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
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48
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Schnurr TM, Stallknecht BM, Sørensen TIA, Kilpeläinen TO, Hansen T. Evidence for shared genetics between physical activity, sedentary behaviour and adiposity-related traits. Obes Rev 2021; 22:e13182. [PMID: 33354910 DOI: 10.1111/obr.13182] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Revised: 11/23/2020] [Accepted: 11/25/2020] [Indexed: 12/20/2022]
Abstract
Observational, cross-sectional and longitudinal studies showed that physical activity and sedentary behaviour are associated with adiposity-related traits, apparently in a bidirectional manner. Physical activity is also suggested to suppress the genetic risk of adiposity. Since phenotypic associations with genetic variants are not subject to reverse causation or confounding, they may be used as tools to shed light on cause and effect in this complex interdependency. We review the evidence for shared genetics of physical activity and adiposity-related traits and for gene-by-physical activity interactions on adiposity-related traits in human studies. We outline limitations, challenges and opportunities in studying and understanding of these relationships. In summary, physical activity and sedentary behaviour are genetically correlated with body mass index and fat percentage but may not be correlated with lean body mass. Mendelian randomisation analyses show that physical activity and sedentary behaviour have bidirectional relationships with adiposity. Several studies suggest that physical activity suppresses genetic risk of adiposity. No studies have yet tested whether adiposity enhances genetic predisposition to sedentariness. The complexity of the comprehensive causal model makes the assessment of the single or combined components challenging. Substantial progress in this field may need long-term intervention studies.
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Affiliation(s)
- Theresia M Schnurr
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Bente M Stallknecht
- Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Thorkild I A Sørensen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Department of Public Health, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Tuomas O Kilpeläinen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Torben Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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49
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Ludwig M, Richter M, Goltermann J, Redlich R, Repple J, Flint C, Grotegerd D, Koch K, Leehr EJ, Meinert S, Hülsmann C, Enneking V, Kugel H, Hahn T, Baune BT, Dannlowski U, Opel N. Novelty seeking is associated with increased body weight and orbitofrontal grey matter volume reduction. Psychoneuroendocrinology 2021; 126:105148. [PMID: 33513455 DOI: 10.1016/j.psyneuen.2021.105148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Revised: 01/18/2021] [Accepted: 01/18/2021] [Indexed: 10/22/2022]
Abstract
Novelty seeking (NS) has previously been identified as a personality trait that is associated with elevated body mass index (BMI) and obesity. Of note, both obesity and reduced impulse control - a core feature of NS - have previously been associated with grey matter volume (GMV) reductions in the orbitofrontal cortex (OFC). Yet, it remains unknown, if body weight-related grey matter decline in the OFC might be explained by higher levels of NS. To address this question, we studied associations between NS, BMI and brain structure in 355 healthy subjects. Brain images were pre-processed using voxel-based morphometry (VBM). BMI was calculated from self-reported height and weight. The Tridimensional Personality Questionnaire (TPQ) was used to assess NS. NS and BMI were associated positively (r = .137, p = .01) with NS being a significant predictor of BMI (B = 0.172; SE B = 0.05; ß = 0.184; p = 0.001). Significant associations between BMI and GMV specifically in the OFC (x = -44, y = 56, z = -2, t(350) = 4.34, k = 5, pFWE = 0.011) did not uphold when correcting for NS in the model. In turn, a significant negative association between NS and OFC GMV was found independent of BMI (x = -2, y = 48, z = -10, t(349) = 4.42, k = 88, pFWE = 0.008). Body mass-related grey matter decrease outside the OFC could not be attributed to NS. Our results suggest that body-weight-related orbitofrontal grey matter reduction can at least partly be linked to higher levels of NS. Given the pivotal role of the OFC in overweight as well as cognitive domains such as impulse inhibition, executive control and reward processing, its association with NS seems to provide a tenable neurobiological correlate for future research.
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Affiliation(s)
- Marius Ludwig
- Department of Psychiatry, University of Münster, Germany
| | - Maike Richter
- Department of Psychiatry, University of Münster, Germany
| | | | - Ronny Redlich
- Department of Psychiatry, University of Münster, Germany; Department of Psychology, University of Halle, Germany
| | | | - Claas Flint
- Department of Psychiatry, University of Münster, Germany; Department of Mathematics and Computer Science, University of Münster, Germany
| | | | - Katharina Koch
- Department of Psychiatry, University of Münster, Germany
| | | | | | | | | | - Harald Kugel
- Institute of Clinical Radiology, University of Münster, Germany
| | - Tim Hahn
- Department of Psychiatry, University of Münster, Germany
| | - Bernhard T Baune
- Department of Psychiatry, University of Münster, Germany; Department of Psychiatry, Melbourne Medical School, The University of Melbourne, Melbourne, VIC, Australia; The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia
| | - Udo Dannlowski
- Department of Psychiatry, University of Münster, Germany
| | - Nils Opel
- Department of Psychiatry, University of Münster, Germany.
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50
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Guo J, Bakshi A, Wang Y, Jiang L, Yengo L, Goddard ME, Visscher PM, Yang J. Quantifying genetic heterogeneity between continental populations for human height and body mass index. Sci Rep 2021; 11:5240. [PMID: 33664403 PMCID: PMC7933291 DOI: 10.1038/s41598-021-84739-z] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Accepted: 02/22/2021] [Indexed: 12/26/2022] Open
Abstract
Genome-wide association studies (GWAS) in samples of European ancestry have identified thousands of genetic variants associated with complex traits in humans. However, it remains largely unclear whether these associations can be used in non-European populations. Here, we seek to quantify the proportion of genetic variation for a complex trait shared between continental populations. We estimated the between-population correlation of genetic effects at all SNPs (\documentclass[12pt]{minimal}
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\begin{document}$$r_{{g\left( {GWS} \right)}}$$\end{document}rgGWS) for height and body mass index (BMI) in samples of European (EUR; \documentclass[12pt]{minimal}
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\begin{document}$$n = 49,839$$\end{document}n=49,839) and African (AFR; \documentclass[12pt]{minimal}
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\begin{document}$$n = 17,426$$\end{document}n=17,426) ancestry. The \documentclass[12pt]{minimal}
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\begin{document}$$\hat{r}_{g}$$\end{document}r^g between EUR and AFR was 0.75 (\documentclass[12pt]{minimal}
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\begin{document}$${\text{s}}.{\text{e}}. = 0.035$$\end{document}s.e.=0.035) for height and 0.68 (\documentclass[12pt]{minimal}
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\begin{document}$${\text{s}}.{\text{e}}. = 0.062$$\end{document}s.e.=0.062) for BMI, and the corresponding \documentclass[12pt]{minimal}
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\begin{document}$$\hat{r}_{{g\left( {GWS} \right)}}$$\end{document}r^gGWS was 0.82 (\documentclass[12pt]{minimal}
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\begin{document}$${\text{s}}.{\text{e}}. = 0.030$$\end{document}s.e.=0.030) for height and 0.87 (\documentclass[12pt]{minimal}
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\begin{document}$${\text{s}}.{\text{e}}. = 0.064$$\end{document}s.e.=0.064) for BMI, suggesting that a large proportion of GWAS findings discovered in Europeans are likely applicable to non-Europeans for height and BMI. There was no evidence that \documentclass[12pt]{minimal}
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\begin{document}$$\hat{r}_{g}$$\end{document}r^g differs in SNP groups with different levels of between-population difference in allele frequency or linkage disequilibrium, which, however, can be due to the lack of power.
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Affiliation(s)
- Jing Guo
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia.,Human Genetics, Wellcome Sanger Institute, Hinxton, CB10 1SA, UK
| | - Andrew Bakshi
- Monash Partners Comprehensive Cancer Consortium, Monash Biomedicine Discovery Institute Cancer Program, Prostate Cancer Research Group, Department of Anatomy and Developmental Biology, Monash University, Clayton, VIC, 3800, Australia.,Queensland Brain Institute, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Ying Wang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Longda Jiang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Loic Yengo
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Michael E Goddard
- Faculty of Veterinary and Agricultural Science, University of Melbourne, Parkville, VIC, Australia.,Biosciences Research Division, Department of Economic Development, Jobs, Transport and Resources, Bundoora, VIC, Australia
| | - Peter M Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Jian Yang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia. .,School of Life Sciences, Westlake University, Hangzhou, 310024, Zhejiang, China. .,Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, 310024, Zhejiang, China.
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