51
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Hussain W, Campbell MT, Jarquin D, Walia H, Morota G. Variance heterogeneity genome-wide mapping for cadmium in bread wheat reveals novel genomic loci and epistatic interactions. THE PLANT GENOME 2020; 13:e20011. [PMID: 33016629 DOI: 10.1002/tpg2.20011] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2019] [Accepted: 01/22/2020] [Indexed: 06/11/2023]
Abstract
Genome-wide association mapping identifies quantitative trait loci (QTL) that influence the mean differences between the marker genotypes for a given trait. While most loci influence the mean value of a trait, certain loci, known as variance heterogeneity QTL (vQTL) determine the variability of the trait instead of the mean trait value (mQTL). In the present study, we performed a variance heterogeneity genome-wide association study (vGWAS) for grain cadmium (Cd) concentration in bread wheat. We used double generalized linear model and hierarchical generalized linear model to identify vQTL associated with grain Cd. We identified novel vQTL regions on chromosomes 2A and 2B that contribute to the Cd variation and loci that affect both mean and variance heterogeneity (mvQTL) on chromosome 5A. In addition, our results demonstrated the presence of epistatic interactions between vQTL and mvQTL, which could explain variance heterogeneity. Overall, we provide novel insights into the genetic architecture of grain Cd concentration and report the first application of vGWAS in wheat. Moreover, our findings indicated that epistasis is an important mechanism underlying natural variation for grain Cd concentration.
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Affiliation(s)
- Waseem Hussain
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, 68583, USA
| | - Malachy T Campbell
- Department of Animal and Poultry Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, 24061, USA
| | - Diego Jarquin
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, 68583, USA
| | - Harkamal Walia
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, 68583, USA
| | - Gota Morota
- Department of Animal and Poultry Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, 24061, USA
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52
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Wragg D, Liu Q, Lin Z, Riggio V, Pugh CA, Beveridge AJ, Brown H, Hume DA, Harris SE, Deary IJ, Tenesa A, Prendergast JGD. Using regulatory variants to detect gene-gene interactions identifies networks of genes linked to cell immortalisation. Nat Commun 2020; 11:343. [PMID: 31953380 PMCID: PMC6969137 DOI: 10.1038/s41467-019-13762-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2018] [Accepted: 11/19/2019] [Indexed: 12/30/2022] Open
Abstract
The extent to which the impact of regulatory genetic variants may depend on other factors, such as the expression levels of upstream transcription factors, remains poorly understood. Here we report a framework in which regulatory variants are first aggregated into sets, and using these as estimates of the total cis-genetic effects on a gene we model their non-additive interactions with the expression of other genes in the genome. Using 1220 lymphoblastoid cell lines across platforms and independent datasets we identify 74 genes where the impact of their regulatory variant-set is linked to the expression levels of networks of distal genes. We show that these networks are predominantly associated with tumourigenesis pathways, through which immortalised cells are able to rapidly proliferate. We consequently present an approach to define gene interaction networks underlying important cellular pathways such as cell immortalisation.
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Affiliation(s)
- D. Wragg
- 0000 0004 1936 7988grid.4305.2The Roslin Institute, University of Edinburgh, Easter Bush, Midlothian, EH25 9RG UK
| | - Q. Liu
- 0000 0004 1936 7988grid.4305.2The Roslin Institute, University of Edinburgh, Easter Bush, Midlothian, EH25 9RG UK
| | - Z. Lin
- 0000 0004 1936 7988grid.4305.2The Roslin Institute, University of Edinburgh, Easter Bush, Midlothian, EH25 9RG UK
| | - V. Riggio
- 0000 0004 1936 7988grid.4305.2The Roslin Institute, University of Edinburgh, Easter Bush, Midlothian, EH25 9RG UK
| | - C. A. Pugh
- 0000 0004 1936 7988grid.4305.2The Roslin Institute, University of Edinburgh, Easter Bush, Midlothian, EH25 9RG UK
| | - A. J. Beveridge
- 0000 0001 2193 314Xgrid.8756.cGlasgow Polyomics, College of Medical, Veterinary and Life Science, University of Glasgow, Glasgow, UK
| | - H. Brown
- 0000 0004 1936 7988grid.4305.2The Roslin Institute, University of Edinburgh, Easter Bush, Midlothian, EH25 9RG UK
| | - D. A. Hume
- 0000000406180938grid.489335.0Mater Research Institute-University of Queensland, Translational Research Institute, Woolloongabba, QLD 4102 Australia
| | - S. E. Harris
- 0000 0004 1936 7988grid.4305.2Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, EH8 9JZ UK
| | - I. J. Deary
- 0000 0004 1936 7988grid.4305.2Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, EH8 9JZ UK
| | - A. Tenesa
- 0000 0004 1936 7988grid.4305.2The Roslin Institute, University of Edinburgh, Easter Bush, Midlothian, EH25 9RG UK
| | - J. G. D. Prendergast
- 0000 0004 1936 7988grid.4305.2The Roslin Institute, University of Edinburgh, Easter Bush, Midlothian, EH25 9RG UK
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53
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Dumitrascu B, Darnell G, Ayroles J, Engelhardt BE. Statistical tests for detecting variance effects in quantitative trait studies. Bioinformatics 2019; 35:200-210. [PMID: 29982387 PMCID: PMC6330007 DOI: 10.1093/bioinformatics/bty565] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2017] [Accepted: 07/04/2018] [Indexed: 11/17/2022] Open
Abstract
Motivation Identifying variants, both discrete and continuous, that are associated with quantitative traits, or QTs, is the primary focus of quantitative genetics. Most current methods are limited to identifying mean effects, or associations between genotype or covariates and the mean value of a quantitative trait. It is possible, however, that a variant may affect the variance of the quantitative trait in lieu of, or in addition to, affecting the trait mean. Here, we develop a general methodology to identify covariates with variance effects on a quantitative trait using a Bayesian heteroskedastic linear regression model (BTH). We compare BTH with existing methods to detect variance effects across a large range of simulations drawn from scenarios common to the analysis of quantitative traits. Results We find that BTH and a double generalized linear model (dglm) outperform classical tests used for detecting variance effects in recent genomic studies. We show BTH and dglm are less likely to generate spurious discoveries through simulations and application to identifying methylation variance QTs and expression variance QTs. We identify four variance effects of sex in the Cardiovascular and Pharmacogenetics study. Our work is the first to offer a comprehensive view of variance identifying methodology. We identify shortcomings in previously used methodology and provide a more conservative and robust alternative. We extend variance effect analysis to a wide array of covariates that enables a new statistical dimension in the study of sex and age specific quantitative trait effects. Availability and implementation https://github.com/b2du/bth. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Bianca Dumitrascu
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Gregory Darnell
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Julien Ayroles
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
| | - Barbara E Engelhardt
- Department of Computer Science, Princeton University, Princeton, NJ, USA.,Center for Statistics and Machine Learning, Princeton University, Princeton, NJ, USA
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54
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Young AI, Benonisdottir S, Przeworski M, Kong A. Deconstructing the sources of genotype-phenotype associations in humans. Science 2019; 365:1396-1400. [PMID: 31604265 PMCID: PMC6894903 DOI: 10.1126/science.aax3710] [Citation(s) in RCA: 120] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Efforts to link variation in the human genome to phenotypes have progressed at a tremendous pace in recent decades. Most human traits have been shown to be affected by a large number of genetic variants across the genome. To interpret these associations and to use them reliably-in particular for phenotypic prediction-a better understanding of the many sources of genotype-phenotype associations is necessary. We summarize the progress that has been made in this direction in humans, notably in decomposing direct and indirect genetic effects as well as population structure confounding. We discuss the natural next steps in data collection and methodology development, with a focus on what can be gained by analyzing genotype and phenotype data from close relatives.
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Affiliation(s)
- Alexander I Young
- Big Data Institute, Li Ka Shing Centre for Health Information Discovery, University of Oxford, Oxford, UK.
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Stefania Benonisdottir
- Big Data Institute, Li Ka Shing Centre for Health Information Discovery, University of Oxford, Oxford, UK
| | - Molly Przeworski
- Department of Biological Sciences, Columbia University, New York, NY, USA.
- Department of Systems Biology, Columbia University, New York, NY, USA
| | - Augustine Kong
- Big Data Institute, Li Ka Shing Centre for Health Information Discovery, University of Oxford, Oxford, UK.
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55
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Discovering genetic interactions bridging pathways in genome-wide association studies. Nat Commun 2019; 10:4274. [PMID: 31537791 PMCID: PMC6753138 DOI: 10.1038/s41467-019-12131-7] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Accepted: 08/20/2019] [Indexed: 12/20/2022] Open
Abstract
Genetic interactions have been reported to underlie phenotypes in a variety of systems, but the extent to which they contribute to complex disease in humans remains unclear. In principle, genome-wide association studies (GWAS) provide a platform for detecting genetic interactions, but existing methods for identifying them from GWAS data tend to focus on testing individual locus pairs, which undermines statistical power. Importantly, a global genetic network mapped for a model eukaryotic organism revealed that genetic interactions often connect genes between compensatory functional modules in a highly coherent manner. Taking advantage of this expected structure, we developed a computational approach called BridGE that identifies pathways connected by genetic interactions from GWAS data. Applying BridGE broadly, we discover significant interactions in Parkinson's disease, schizophrenia, hypertension, prostate cancer, breast cancer, and type 2 diabetes. Our novel approach provides a general framework for mapping complex genetic networks underlying human disease from genome-wide genotype data.
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56
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Wang H, Zhang F, Zeng J, Wu Y, Kemper KE, Xue A, Zhang M, Powell JE, Goddard ME, Wray NR, Visscher PM, McRae AF, Yang J. Genotype-by-environment interactions inferred from genetic effects on phenotypic variability in the UK Biobank. SCIENCE ADVANCES 2019; 5:eaaw3538. [PMID: 31453325 PMCID: PMC6693916 DOI: 10.1126/sciadv.aaw3538] [Citation(s) in RCA: 102] [Impact Index Per Article: 20.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2018] [Accepted: 07/11/2019] [Indexed: 05/17/2023]
Abstract
Genotype-by-environment interaction (GEI) is a fundamental component in understanding complex trait variation. However, it remains challenging to identify genetic variants with GEI effects in humans largely because of the small effect sizes and the difficulty of monitoring environmental fluctuations. Here, we demonstrate that GEI can be inferred from genetic variants associated with phenotypic variability in a large sample without the need of measuring environmental factors. We performed a genome-wide variance quantitative trait locus (vQTL) analysis of ~5.6 million variants on 348,501 unrelated individuals of European ancestry for 13 quantitative traits in the UK Biobank and identified 75 significant vQTLs with P < 2.0 × 10-9 for 9 traits, especially for those related to obesity. Direct GEI analysis with five environmental factors showed that the vQTLs were strongly enriched with GEI effects. Our results indicate pervasive GEI effects for obesity-related traits and demonstrate the detection of GEI without environmental data.
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Affiliation(s)
- Huanwei Wang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland 4072, Australia
| | - Futao Zhang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland 4072, Australia
| | - Jian Zeng
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland 4072, Australia
| | - Yang Wu
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland 4072, Australia
| | - Kathryn E. Kemper
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland 4072, Australia
| | - Angli Xue
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland 4072, Australia
| | - Min Zhang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland 4072, Australia
| | - Joseph E. Powell
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland 4072, Australia
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute for Medical Research, Sydney, New South Wales 2010, Australia
- Faculty of Medicine, University of New South Wales, Sydney, New South Wales 2052, Australia
| | - Michael E. Goddard
- Faculty of Veterinary and Agricultural Science, University of Melbourne, Parkville, Victoria, Australia
- Biosciences Research Division, Department of Economic Development, Jobs, Transport and Resources, Bundoora, Victoria, Australia
| | - Naomi R. Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland 4072, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland 4072, Australia
| | - Peter M. Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland 4072, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland 4072, Australia
| | - Allan F. McRae
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland 4072, Australia
| | - Jian Yang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland 4072, Australia
- Institute for Advanced Research, Wenzhou Medical University, Wenzhou, Zhejiang 325027, China
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57
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Deng WQ, Mao S, Kalnapenkis A, Esko T, Mägi R, Paré G, Sun L. Analytical strategies to include the X-chromosome in variance heterogeneity analyses: Evidence for trait-specific polygenic variance structure. Genet Epidemiol 2019; 43:815-830. [PMID: 31332826 DOI: 10.1002/gepi.22247] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2019] [Revised: 06/07/2019] [Accepted: 06/13/2019] [Indexed: 12/12/2022]
Abstract
Genotype-stratified variance of a quantitative trait could differ in the presence of gene-gene or gene-environment interactions. Genetic markers associated with phenotypic variance are thus considered promising candidates for follow-up interaction or joint location-scale analyses. However, as in studies of main effects, the X-chromosome is routinely excluded from "whole-genome" scans due to analytical challenges. Specifically, as males carry only one copy of the X-chromosome, the inherent sex-genotype dependency could bias the trait-genotype association, through sexual dimorphism in quantitative traits with sex-specific means or variances. Here we investigate phenotypic variance heterogeneity associated with X-chromosome single nucleotide polymorphisms (SNPs) and propose valid and powerful strategies. Among those, a generalized Levene's test has adequate power and remains robust to sexual dimorphism. An alternative approach is a sex-stratified analysis but at the cost of slightly reduced power and modeling flexibility. We applied both methods to an Estonian study of gene expression quantitative trait loci (eQTL; n = 841), and two complex trait studies of height, hip, and waist circumferences, and body mass index from Multi-Ethnic Study of Atherosclerosis (MESA; n = 2,073) and UK Biobank (UKB; n = 327,393). Consistent with previous eQTL findings on mean, we found some but no conclusive evidence for cis regulators being enriched for variance association. SNP rs2681646 is associated with variance of waist circumference (p = 9.5E-07) at X-chromosome-wide significance in UKB, with a suggestive female-specific effect in MESA (p = 0.048). Collectively, an enrichment analysis using permutated UKB (p < 0.1) and MESA (p < 0.01) datasets, suggests a possible polygenic structure for the variance of human height.
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Affiliation(s)
- Wei Q Deng
- Department of Statistical Sciences, Faculty of Arts and Science, University of Toronto, Toronto, Canada
| | - Shihong Mao
- Department of Pathology and Molecular Medicine, Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, Canada
| | - Anette Kalnapenkis
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Tõnu Esko
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia.,Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts
| | - Reedik Mägi
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Guillaume Paré
- Department of Pathology and Molecular Medicine, Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, Canada.,Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Canada
| | - Lei Sun
- Department of Statistical Sciences, Faculty of Arts and Science, University of Toronto, Toronto, Canada.,Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
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58
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Pozarickij A, Williams C, Hysi PG, Guggenheim JA. Quantile regression analysis reveals widespread evidence for gene-environment or gene-gene interactions in myopia development. Commun Biol 2019; 2:167. [PMID: 31069276 PMCID: PMC6502837 DOI: 10.1038/s42003-019-0387-5] [Citation(s) in RCA: 25] [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: 12/20/2018] [Accepted: 03/15/2019] [Indexed: 12/18/2022] Open
Abstract
A genetic contribution to refractive error has been confirmed by the discovery of more than 150 associated variants in genome-wide association studies (GWAS). Environmental factors such as education and time outdoors also demonstrate strong associations. Currently however, the extent of gene-environment or gene-gene interactions in myopia is unknown. We tested the hypothesis that refractive error-associated variants exhibit effect size heterogeneity, a hallmark feature of genetic interactions. Of 146 variants tested, evidence of non-uniform, non-linear effects were observed for 66 (45%) at Bonferroni-corrected significance (P < 1.1 × 10-4) and 128 (88%) at nominal significance (P < 0.05). LAMA2 variant rs12193446, for example, had an effect size varying from -0.20 diopters (95% CI -0.18 to -0.23) to -0.89 diopters (95% CI -0.71 to -1.07) in different individuals. SNP effects were strongest at the phenotype extremes and weaker in emmetropes. A parsimonious explanation for these findings is that gene-environment or gene-gene interactions in myopia are pervasive.
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Affiliation(s)
- Alfred Pozarickij
- School of Optometry & Vision Sciences, Cardiff University, Cardiff, CF24 4HQ UK
| | - Cathy Williams
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN UK
| | - Pirro G. Hysi
- Department of Ophthalmology, King’s College London, St. Thomas’ Hospital, London, SE1 7EH UK
- Department of Twin & Genetic Epidemiology, King’s College London, St. Thomas’ Hospital, London, SE1 7EH UK
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59
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Ek WE, Rask-Andersen M, Karlsson T, Enroth S, Gyllensten U, Johansson Å. Genetic variants influencing phenotypic variance heterogeneity. Hum Mol Genet 2019; 27:799-810. [PMID: 29325024 DOI: 10.1093/hmg/ddx441] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2017] [Accepted: 12/22/2017] [Indexed: 12/22/2022] Open
Abstract
Most genetic studies identify genetic variants associated with disease risk or with the mean value of a quantitative trait. More rarely, genetic variants associated with variance heterogeneity are considered. In this study, we have identified such variance single-nucleotide polymorphisms (vSNPs) and examined if these represent biological gene × gene or gene × environment interactions or statistical artifacts caused by multiple linked genetic variants influencing the same phenotype. We have performed a genome-wide study, to identify vSNPs associated with variance heterogeneity in DNA methylation levels. Genotype data from over 10 million single-nucleotide polymorphisms (SNPs), and DNA methylation levels at over 430 000 CpG sites, were analyzed in 729 individuals. We identified vSNPs for 7195 CpG sites (P < 9.4 × 10-11). This is a relatively low number compared to 52 335 CpG sites for which SNPs were associated with mean DNA methylation levels. We further showed that variance heterogeneity between genotypes mainly represents additional, often rare, SNPs in linkage disequilibrium (LD) with the respective vSNP and for some vSNPs, multiple low frequency variants co-segregating with one of the vSNP alleles. Therefore, our results suggest that variance heterogeneity of DNA methylation mainly represents phenotypic effects by multiple SNPs, rather than biological interactions. Such effects may also be important for interpreting variance heterogeneity of more complex clinical phenotypes.
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Affiliation(s)
- Weronica E Ek
- Science for Life Laboratory, Department of Immunology Genetics and Pathology, Uppsala University, 751 08 Uppsala, Sweden
| | - Mathias Rask-Andersen
- Science for Life Laboratory, Department of Immunology Genetics and Pathology, Uppsala University, 751 08 Uppsala, Sweden
| | - Torgny Karlsson
- Science for Life Laboratory, Department of Immunology Genetics and Pathology, Uppsala University, 751 08 Uppsala, Sweden
| | - Stefan Enroth
- Science for Life Laboratory, Department of Immunology Genetics and Pathology, Uppsala University, 751 08 Uppsala, Sweden
| | - Ulf Gyllensten
- Science for Life Laboratory, Department of Immunology Genetics and Pathology, Uppsala University, 751 08 Uppsala, Sweden
| | - Åsa Johansson
- Science for Life Laboratory, Department of Immunology Genetics and Pathology, Uppsala University, 751 08 Uppsala, Sweden
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60
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Ahmad S, Fatima SS, Rukh G, Smith CE. Gene Lifestyle Interactions With Relation to Obesity, Cardiometabolic, and Cardiovascular Traits Among South Asians. Front Endocrinol (Lausanne) 2019; 10:221. [PMID: 31024458 PMCID: PMC6465946 DOI: 10.3389/fendo.2019.00221] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/03/2018] [Accepted: 03/20/2019] [Indexed: 01/05/2023] Open
Abstract
The rapid rise of obesity, type 2 diabetes mellitus (T2DM) and cardiovascular disease (CVD) during the last few decades among South Asians has been largely attributed to a major shift in lifestyles including physical inactivity, unhealthy dietary patterns, and an overall pattern of sedentary lifestyle. Genetic predisposition to these cardiometabolic risk factors may have interacted with these obesogenic environments in determining the higher cardiometabolic disease prevalence. Based on the premise that gene-environment interactions cause obesity and cardiometabolic diseases, we systematically searched the literature and considered the knowledge gaps that future studies might fulfill. We identified only seven published studies that focused specifically on gene-environment interactions for cardiometabolic traits in South Asians, most of which were limited by relatively small sample and lack of replication. Some studies reported that the differences in metabolic response to higher physical activity and low caloric diet might be modified by genetic risk related to these cardiometabolic traits. Although studies on gene lifestyle interactions in cardiometabolic traits report significant interactions, future studies must focus on more precise assessment of lifestyle factors, investigation of a larger set of genetic variants and the application of powerful statistical methods to facilitate translatable approaches. Future studies should also be integrated with findings both using mechanistic studies through laboratory settings and randomized clinical trials for clinical outcomes.
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Affiliation(s)
- Shafqat Ahmad
- Department of Medical Sciences, Molecular Epidemiology, Uppsala University, Uppsala, Sweden
- Preventive Medicine Division, Harvard Medical School, Brigham and Women's Hospital, Boston, MA, United States
- Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, United States
- *Correspondence: Shafqat Ahmad
| | - Syeda Sadia Fatima
- Department of Biological and Biomedical Sciences, Aga Khan University, Karachi, Pakistan
| | - Gull Rukh
- Department of Neuroscience, Functional Pharmacology, Uppsala University, Uppsala, Sweden
| | - Caren E. Smith
- Nutrition and Genomics Laboratory, Jean Mayer U. S. Department of Agriculture Human Nutrition Research Center on Aging, Tufts University, Boston, MA, United States
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61
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Gene expression variation and parental allele inheritance in a Xiphophorus interspecies hybridization model. PLoS Genet 2018; 14:e1007875. [PMID: 30586357 PMCID: PMC6324826 DOI: 10.1371/journal.pgen.1007875] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2018] [Revised: 01/08/2019] [Accepted: 12/04/2018] [Indexed: 01/06/2023] Open
Abstract
Understanding the genetic mechanisms underlying segregation of phenotypic variation through successive generations is important for understanding physiological changes and disease risk. Tracing the etiology of variation in gene expression enables identification of genetic interactions, and may uncover molecular mechanisms leading to the phenotypic expression of a trait, especially when utilizing model organisms that have well-defined genetic lineages. There are a plethora of studies that describe relationships between gene expression and genotype, however, the idea that global variations in gene expression are also controlled by genotype remains novel. Despite the identification of loci that control gene expression variation, the global understanding of how genome constitution affects trait variability is unknown. To study this question, we utilized Xiphophorus fish of different, but tractable genetic backgrounds (inbred, F1 interspecies hybrids, and backcross hybrid progeny), and measured each individual’s gene expression concurrent with the degrees of inter-individual expression variation. We found, (a) F1 interspecies hybrids exhibited less variability than inbred animals, indicting gene expression variation is not affected by the fraction of heterozygous loci within an individual genome, and (b), that mixing genotypes in backcross populations led to higher levels of gene expression variability, supporting the idea that expression variability is caused by heterogeneity of genotypes of cis or trans loci. In conclusion, heterogeneity of genotype, introduced by inheritance of different alleles, accounts for the largest effects on global phenotypical variability. Phenotypical variability is a multi-factorial phenomenon. Although it has been shown that inheriting certain gene is associated with lower phenotypical variability, how genome complexity affect phenotypical variability is still unclear. To study this question, we used inbred Xiphophorus fish, backcross interspecies hybrids, and F1 interspecies hybrids between select Xiphophorus species to model genetic composition with minimum, medium, and maximum heterozygosity respectively, and measured their global gene expression variability. We found gene expression variation is not affected by the percentage of heterozygous loci in individual genome, but instead related to heterogeneity of genotype at local or remote loci.
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62
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Corty RW, Valdar W. vqtl: An R Package for Mean-Variance QTL Mapping. G3 (BETHESDA, MD.) 2018; 8:3757-3766. [PMID: 30389795 PMCID: PMC6288833 DOI: 10.1534/g3.118.200642] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/14/2018] [Accepted: 10/23/2018] [Indexed: 12/26/2022]
Abstract
We present vqtl, an R package for mean-variance QTL mapping. This QTL mapping approach tests for genetic loci that influence the mean of the phenotype, termed mean QTL, the variance of the phenotype, termed variance QTL, or some combination of the two, termed mean-variance QTL. It is unique in its ability to correct for variance heterogeneity arising not only from the QTL itself but also from nuisance factors, such as sex, batch, or housing. This package provides functions to conduct genome scans, run permutations to assess the statistical significance, and make informative plots to communicate results. Because it is inter-operable with the popular qtl package and uses many of the same data structures and input patterns, it will be straightforward for geneticists to analyze future experiments with vqtl as well as re-analyze past experiments, possibly discovering new QTL.
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Affiliation(s)
- Robert W Corty
- Department of Genetics
- Bioinformatics and Computational Biology Curriculum
| | - William Valdar
- Department of Genetics
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC
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63
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Corty RW, Valdar W. QTL Mapping on a Background of Variance Heterogeneity. G3 (BETHESDA, MD.) 2018; 8:3767-3782. [PMID: 30389794 DOI: 10.1101/276980] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
Standard QTL mapping procedures seek to identify genetic loci affecting the phenotypic mean while assuming that all individuals have the same residual variance. But when the residual variance differs systematically between groups, perhaps due to a genetic or environmental factor, such standard procedures can falter: in testing for QTL associations, they attribute too much weight to observations that are noisy and too little to those that are precise, resulting in reduced power and and increased susceptibility to false positives. The negative effects of such "background variance heterogeneity" (BVH) on standard QTL mapping have received little attention until now, although the subject is closely related to work on the detection of variance-controlling genes. Here we use simulation to examine how BVH affects power and false positive rate for detecting QTL affecting the mean (mQTL), the variance (vQTL), or both (mvQTL). We compare linear regression for mQTL and Levene's test for vQTL, with tests more recently developed, including tests based on the double generalized linear model (DGLM), which can model BVH explicitly. We show that, when used in conjunction with a suitable permutation procedure, the DGLM-based tests accurately control false positive rate and are more powerful than the other tests. We also find that some adverse effects of BVH can be mitigated by applying a rank inverse normal transform. We apply our novel approach, which we term "mean-variance QTL mapping", to publicly available data on a mouse backcross and, after accommodating BVH driven by sire, detect a new mQTL for bodyweight.
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Affiliation(s)
- Robert W Corty
- Department of Genetics
- Bioinformatics and Computational Biology Curriculum
| | - William Valdar
- Department of Genetics
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC
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64
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Corty RW, Valdar W. QTL Mapping on a Background of Variance Heterogeneity. G3 (BETHESDA, MD.) 2018; 8:3767-3782. [PMID: 30389794 PMCID: PMC6288843 DOI: 10.1534/g3.118.200790] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/27/2018] [Accepted: 10/28/2018] [Indexed: 12/21/2022]
Abstract
Standard QTL mapping procedures seek to identify genetic loci affecting the phenotypic mean while assuming that all individuals have the same residual variance. But when the residual variance differs systematically between groups, perhaps due to a genetic or environmental factor, such standard procedures can falter: in testing for QTL associations, they attribute too much weight to observations that are noisy and too little to those that are precise, resulting in reduced power and and increased susceptibility to false positives. The negative effects of such "background variance heterogeneity" (BVH) on standard QTL mapping have received little attention until now, although the subject is closely related to work on the detection of variance-controlling genes. Here we use simulation to examine how BVH affects power and false positive rate for detecting QTL affecting the mean (mQTL), the variance (vQTL), or both (mvQTL). We compare linear regression for mQTL and Levene's test for vQTL, with tests more recently developed, including tests based on the double generalized linear model (DGLM), which can model BVH explicitly. We show that, when used in conjunction with a suitable permutation procedure, the DGLM-based tests accurately control false positive rate and are more powerful than the other tests. We also find that some adverse effects of BVH can be mitigated by applying a rank inverse normal transform. We apply our novel approach, which we term "mean-variance QTL mapping", to publicly available data on a mouse backcross and, after accommodating BVH driven by sire, detect a new mQTL for bodyweight.
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Affiliation(s)
- Robert W Corty
- Department of Genetics
- Bioinformatics and Computational Biology Curriculum
| | - William Valdar
- Department of Genetics
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC
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65
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Zhang T, Sun L. Beyond the traditional simulation design for evaluating type 1 error control: From the "theoretical" null to "empirical" null. Genet Epidemiol 2018; 43:166-179. [PMID: 30478944 PMCID: PMC6518945 DOI: 10.1002/gepi.22172] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2018] [Revised: 09/10/2018] [Accepted: 09/21/2018] [Indexed: 01/25/2023]
Abstract
When evaluating a newly developed statistical test, an important step is to check its type 1 error (T1E) control using simulations. This is often achieved by the standard simulation design S0 under the so-called "theoretical" null of no association. In practice, the whole-genome association analyses scan through a large number of genetic markers ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>G</mml:mi></mml:math> s) for the ones associated with an outcome of interest ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>Y</mml:mi></mml:math> ), where <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>Y</mml:mi></mml:math> comes from an alternative while the majority of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>G</mml:mi></mml:math> s are not associated with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>Y</mml:mi></mml:math> ; the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>Y</mml:mi> <mml:mo>-</mml:mo> <mml:mi>G</mml:mi></mml:math> relationships are under the "empirical" null. This reality can be better represented by two other simulation designs, where design S1.1 simulates <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>Y</mml:mi></mml:math> from analternative model based on <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>G</mml:mi></mml:math> , then evaluates its association with independently generated <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mrow/> <mml:msub><mml:mi>G</mml:mi> <mml:mrow><mml:mi>n</mml:mi> <mml:mi>e</mml:mi> <mml:mi>w</mml:mi></mml:mrow> </mml:msub> </mml:mrow> </mml:math> ; while design S1.2 evaluates the association between permutated <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>Y</mml:mi></mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>G</mml:mi></mml:math> . More than a decade ago, Efron (2004) has noted the important distinction between the "theoretical" and "empirical" null in false discovery rate control. Using scale tests for variance heterogeneity, direct univariate, and multivariate interaction tests as examples, here we show that not all null simulation designs are equal. In examining the accuracy of a likelihood ratio test, while simulation design S0 suggested the method being accurate, designs S1.1 and S1.2 revealed its increased empirical T1E rate if applied in real data setting. The inflation becomes more severe at the tail and does not diminish as sample size increases. This is an important observation that calls for new practices for methods evaluation and T1E control interpretation.
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Affiliation(s)
- Ting Zhang
- Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Lei Sun
- Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.,Department of Statistical Sciences, Faculty of Arts and Science, University of Toronto, Toronto, Ontario, Canada
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66
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Young AI, Wauthier FL, Donnelly P. Identifying loci affecting trait variability and detecting interactions in genome-wide association studies. Nat Genet 2018; 50:1608-1614. [PMID: 30323177 DOI: 10.1038/s41588-018-0225-6] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2017] [Accepted: 08/03/2018] [Indexed: 11/09/2022]
Abstract
Identification of genetic variants with effects on trait variability can provide insights into the biological mechanisms that control variation and can identify potential interactions. We propose a two-degree-of-freedom test for jointly testing mean and variance effects to identify such variants. We implement the test in a linear mixed model, for which we provide an efficient algorithm and software. To focus on biologically interesting settings, we develop a test for dispersion effects, that is, variance effects not driven solely by mean effects when the trait distribution is non-normal. We apply our approach to body mass index in the subsample of the UK Biobank population with British ancestry (n ~408,000) and show that our approach can increase the power to detect associated loci. We identify and replicate novel associations with significant variance effects that cannot be explained by the non-normality of body mass index, and we provide suggestive evidence for a connection between leptin levels and body mass index variability.
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Affiliation(s)
- Alexander I Young
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK. .,Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
| | - Fabian L Wauthier
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK.,Department of Statistics, University of Oxford, Oxford, UK
| | - Peter Donnelly
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK. .,Department of Statistics, University of Oxford, Oxford, UK.
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67
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Wang J, Liu Q, Pierce BL, Huo D, Olopade OI, Ahsan H, Chen LS. A meta-analysis approach with filtering for identifying gene-level gene-environment interactions. Genet Epidemiol 2018; 42:434-446. [PMID: 29430690 PMCID: PMC6013347 DOI: 10.1002/gepi.22115] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2017] [Revised: 12/13/2017] [Accepted: 01/02/2018] [Indexed: 02/02/2023]
Abstract
There is a growing recognition that gene-environment interaction (G × E) plays a pivotal role in the development and progression of complex diseases. Despite a wealth of genetic data on various complex diseases/traits generated from association and sequencing studies, detecting G × E via genome-wide analysis remains challenging due to power issues. In genome-wide G × E studies, a common strategy to improve power is to first conduct a filtering test and retain only the genetic variants that pass the filtering step for subsequent G × E analyses. Two-stage, multistage, and unified tests have been proposed to jointly consider the filtering statistics in G × E tests. However, such G × E tests based on data from a single study may still be underpowered. Meanwhile, large-scale consortia have been formed to borrow strength across studies and populations. In this work, motivated by existing single-study G × E tests with filtering and the needs for meta-analysis G × E approaches based on consortia data, we propose a meta-analysis framework for detecting gene-based G × E effects, and introduce meta-analysis-based filtering statistics in the gene-level G × E tests. Simulations demonstrate the advantages of the proposed method-the ofGEM test. We apply the proposed tests to existing data from two breast cancer consortia to identify the genes harboring genetic variants with age-dependent penetrance (i.e., gene-age interaction effects). We develop an R software package ofGEM for the proposed meta-analysis tests.
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Affiliation(s)
- Jiebiao Wang
- Department of Public Health Sciences, The University of Chicago, Chicago, Illinois, USA
| | | | - Brandon L. Pierce
- Department of Public Health Sciences, The University of Chicago, Chicago, Illinois, USA
- Department of Human Genetics, The University of Chicago, Chicago, Illinois, USA
| | - Dezheng Huo
- Department of Public Health Sciences, The University of Chicago, Chicago, Illinois, USA
- Department of Medicine, The University of Chicago, Chicago, Illinois, USA
| | - Olufunmilayo I. Olopade
- Department of Human Genetics, The University of Chicago, Chicago, Illinois, USA
- Department of Medicine, The University of Chicago, Chicago, Illinois, USA
- Center for Clinical Cancer Genetics & Global Health, The University of Chicago Medical Center, Chicago, Illinois, USA
| | - Habibul Ahsan
- Department of Public Health Sciences, The University of Chicago, Chicago, Illinois, USA
- Department of Human Genetics, The University of Chicago, Chicago, Illinois, USA
- Department of Medicine, The University of Chicago, Chicago, Illinois, USA
| | - Lin S. Chen
- Department of Public Health Sciences, The University of Chicago, Chicago, Illinois, USA
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68
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Conley D, Johnson R, Domingue B, Dawes C, Boardman J, Siegal M. A sibling method for identifying vQTLs. PLoS One 2018; 13:e0194541. [PMID: 29617452 PMCID: PMC5884517 DOI: 10.1371/journal.pone.0194541] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2017] [Accepted: 03/05/2018] [Indexed: 12/11/2022] Open
Abstract
The propensity of a trait to vary within a population may have evolutionary, ecological, or clinical significance. In the present study we deploy sibling models to offer a novel and unbiased way to ascertain loci associated with the extent to which phenotypes vary (variance-controlling quantitative trait loci, or vQTLs). Previous methods for vQTL-mapping either exclude genetically related individuals or treat genetic relatedness among individuals as a complicating factor addressed by adjusting estimates for non-independence in phenotypes. The present method uses genetic relatedness as a tool to obtain unbiased estimates of variance effects rather than as a nuisance. The family-based approach, which utilizes random variation between siblings in minor allele counts at a locus, also allows controls for parental genotype, mean effects, and non-linear (dominance) effects that may spuriously appear to generate variation. Simulations show that the approach performs equally well as two existing methods (squared Z-score and DGLM) in controlling type I error rates when there is no unobserved confounding, and performs significantly better than these methods in the presence of small degrees of confounding. Using height and BMI as empirical applications, we investigate SNPs that alter within-family variation in height and BMI, as well as pathways that appear to be enriched. One significant SNP for BMI variability, in the MAST4 gene, replicated. Pathway analysis revealed one gene set, encoding members of several signaling pathways related to gap junction function, which appears significantly enriched for associations with within-family height variation in both datasets (while not enriched in analysis of mean levels). We recommend approximating laboratory random assignment of genotype using family data and more careful attention to the possible conflation of mean and variance effects.
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Affiliation(s)
- Dalton Conley
- Department of Sociology, Princeton University, Princeton, NJ, United States of America
| | - Rebecca Johnson
- Department of Sociology, Princeton University, Princeton, NJ, United States of America
| | - Ben Domingue
- Graduate School of Education, Stanford University, Stanford, CA, United States of America
| | - Christopher Dawes
- Wilff Family Department of Politics, New York University, New York City, NY, United States of America
| | - Jason Boardman
- Institute for Behavioral Sciences, University of Colorado, Boulder, Boulder, CO, United States of America
| | - Mark Siegal
- Center for Genomics and Systems Biology, New York University, New York University, New York City, NY, United States of America
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69
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Brown AA. veqtl-mapper: variance association mapping for molecular phenotypes. Bioinformatics 2018; 33:2772-2773. [PMID: 28449110 DOI: 10.1093/bioinformatics/btx273] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2016] [Accepted: 04/14/2017] [Indexed: 11/12/2022] Open
Abstract
Motivation Genetic loci associated with the variance of phenotypic traits have been of recent interest as they can be signatures of genetic interactions, gene by environment interactions, parent of origin effects and canalization. We present a fast efficient tool to map loci affecting variance of gene expression and other molecular phenotypes in cis. Results: Applied to the publicly available Geuvadis gene expression dataset, we identify 816 loci associated with variance of gene expression using an additive model, and 32 showing differences in variance between homozygous and heterozygous alleles, signatures of parent of origin effects. Availability and implementation Documentation and links to source code and binaries for linux can be found at https://funpopgen.github.io/veqm/ . Contact andrew.brown@unige.ch. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Andrew Anand Brown
- Department of Genetic Medicine and Development.,Institute of Genetics and Genomics in Geneva, University of Geneva, Geneva, Switzerland.,Swiss Institute of Bioinformatics, Geneva, Switzerland
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70
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Viñuela A, Brown AA, Buil A, Tsai PC, Davies MN, Bell JT, Dermitzakis ET, Spector TD, Small KS. Age-dependent changes in mean and variance of gene expression across tissues in a twin cohort. Hum Mol Genet 2018; 27:732-741. [PMID: 29228364 PMCID: PMC5886097 DOI: 10.1093/hmg/ddx424] [Citation(s) in RCA: 61] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2017] [Revised: 11/10/2017] [Accepted: 11/29/2017] [Indexed: 12/13/2022] Open
Abstract
Changes in the mean and variance of gene expression with age have consequences for healthy aging and disease development. Age-dependent changes in phenotypic variance have been associated with a decline in regulatory functions leading to increase in disease risk. Here, we investigate age-related mean and variance changes in gene expression measured by RNA-seq of fat, skin, whole blood and derived lymphoblastoid cell lines (LCLs) expression from 855 adult female twins. We see evidence of up to 60% of age effects on transcription levels shared across tissues, and 47% of those on splicing. Using gene expression variance and discordance between genetically identical MZ twin pairs, we identify 137 genes with age-related changes in variance and 42 genes with age-related discordance between co-twins; implying the latter are driven by environmental effects. We identify four eQTLs whose effect on expression is age-dependent (FDR 5%). Combined, these results show a complicated mix of environmental and genetically driven changes in expression with age. Using the twin structure in our data, we show that additive genetic effects explain considerably more of the variance in gene expression than aging, but less that other environmental factors, potentially explaining why reliable expression-derived biomarkers for healthy-aging have proved elusive compared with those derived from methylation.
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Affiliation(s)
- Ana Viñuela
- Department of Twin Research and Genetic Epidemiology, King's College London, St Thomas' Campus, SE1 7EH London, UK
- Department of Genetic Medicine and Development, University of Geneva Medical School, 1211 Geneva, Switzerland
- Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, 1211 Geneva, Switzerland
- Swiss Institute of Bioinformatics, 1211 Geneva, Switzerland
| | - Andrew A Brown
- Wellcome Trust Sanger Institute, Hinxton CB10 1SA, Cambridge, UK
- Division of Mental Health and Addiction, NORMENT, KG Jebsen Centre for Psychosis Research, Oslo University Hospital, Oslo 0450, Norway
| | - Alfonso Buil
- Department of Genetic Medicine and Development, University of Geneva Medical School, 1211 Geneva, Switzerland
- Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, 1211 Geneva, Switzerland
- Swiss Institute of Bioinformatics, 1211 Geneva, Switzerland
| | - Pei-Chien Tsai
- Department of Twin Research and Genetic Epidemiology, King's College London, St Thomas' Campus, SE1 7EH London, UK
| | - Matthew N Davies
- Department of Twin Research and Genetic Epidemiology, King's College London, St Thomas' Campus, SE1 7EH London, UK
| | - Jordana T Bell
- Department of Twin Research and Genetic Epidemiology, King's College London, St Thomas' Campus, SE1 7EH London, UK
| | - Emmanouil T Dermitzakis
- Department of Genetic Medicine and Development, University of Geneva Medical School, 1211 Geneva, Switzerland
- Institute for Genetics and Genomics in Geneva (iGE3), University of Geneva, 1211 Geneva, Switzerland
- Swiss Institute of Bioinformatics, 1211 Geneva, Switzerland
| | - Timothy D Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, St Thomas' Campus, SE1 7EH London, UK
| | - Kerrin S Small
- Department of Twin Research and Genetic Epidemiology, King's College London, St Thomas' Campus, SE1 7EH London, UK
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71
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Yang E, Wang G, Yang J, Zhou B, Tian Y, Cai JJ. Epistasis and destabilizing mutations shape gene expression variability in humans via distinct modes of action. Hum Mol Genet 2018; 25:4911-4919. [PMID: 28171656 DOI: 10.1093/hmg/ddw314] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2016] [Revised: 08/19/2016] [Accepted: 09/12/2016] [Indexed: 11/14/2022] Open
Abstract
Increasing evidence shows that phenotypic variance is genetically determined, but the underlying mechanisms of genetic control over the variance remain obscure. Here, we conducted variance-association mapping analyses to identify expression variability QTLs (evQTLs), i.e. genomic loci associated with gene expression variance, in humans. We discovered that common genetic variants may contribute to increasing gene expression variance via two distinct modes of action—epistasis and destabilization. Specifically, epistasis explains a quarter of the identified evQTLs, of which the formation is attributed to the presence of ‘third-party’ eQTLs that influence the gene expression mean in a fraction, rather than the entire set, of sampled individuals. On the other hand, the destabilization model explains the other three-quarters of evQTLs, caused by mutations that disrupt the stability of the transcription process of genes. To show the destabilizing effect, we measured discordant gene expression between monozygotic twins, and estimated the stability of gene expression in single samples using repetitive qRT-PCR assays. The mutations that cause destabilizing evQTLs were found to be associated with more pronounced expression discordance between twin pairs and less stable gene expression in single samples. Together, our results suggest that common genetic variants work either interactively or independently to shape the variability of gene expression in humans. Our findings contribute to the understanding of the mechanisms of genetic control over phenotypic variance and may have implications for the development of variance-centred analytic methods for quantitative trait mapping.
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Affiliation(s)
- Ence Yang
- Department of Veterinary Integrative Biosciences.,Institute for Systems Biomedicine, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China
| | - Gang Wang
- Department of Veterinary Integrative Biosciences
| | - Jizhou Yang
- Department of Veterinary Integrative Biosciences
| | - Beiyan Zhou
- Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, TX, USA.,Department of Immunology, University of Connecticut Health Center, Farmington, CT, USA
| | - Yanan Tian
- Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, TX, USA
| | - James J Cai
- Department of Veterinary Integrative Biosciences.,Interdisciplinary Program of Genetics, Texas A&M University, College Station, TX, USA
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72
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Yadav A, Dhole K, Sinha H. Differential Regulation of Cryptic Genetic Variation Shapes the Genetic Interactome Underlying Complex Traits. Genome Biol Evol 2018; 8:3559-3573. [PMID: 28172852 PMCID: PMC5381507 DOI: 10.1093/gbe/evw258] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/21/2016] [Indexed: 12/21/2022] Open
Abstract
Cryptic genetic variation (CGV) refers to genetic variants whose effects are buffered in most conditions but manifest phenotypically upon specific genetic and environmental perturbations. Despite having a central role in adaptation, contribution of CGV to regulation of quantitative traits is unclear. Instead, a relatively simplistic architecture of additive genetic loci is known to regulate phenotypic variation in most traits. In this paper, we investigate the regulation of CGV and its implication on the genetic architecture of quantitative traits at a genome-wide level. We use a previously published dataset of biparental recombinant population of Saccharomyces cerevisiae phenotyped in 34 diverse environments to perform single locus, two-locus, and covariance mapping. We identify loci that have independent additive effects as well as those which regulate the phenotypic manifestation of other genetic variants (variance QTL). We find that whereas additive genetic variance is predominant, a higher order genetic interaction network regulates variation in certain environments. Despite containing pleiotropic loci, with effects across environments, these genetic networks are highly environment specific. CGV is buffered under most allelic combinations of these networks and perturbed only in rare combinations resulting in high phenotypic variance. The presence of such environment specific genetic networks is the underlying cause of abundant gene–environment interactions. We demonstrate that overlaying identified molecular networks on such genetic networks can identify potential candidate genes and underlying mechanisms regulating phenotypic variation. Such an integrated approach applied to human disease datasets has the potential to improve the ability to predict disease predisposition and identify specific therapeutic targets.
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Affiliation(s)
- Anupama Yadav
- Department of Biological Sciences, Tata Institute of Fundamental Research, Mumbai, India
| | - Kaustubh Dhole
- Department of Biological Sciences, Tata Institute of Fundamental Research, Mumbai, India
| | - Himanshu Sinha
- Department of Biological Sciences, Tata Institute of Fundamental Research, Mumbai, India.,Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India.,Initiative for Biological Systems Engineering, Indian Institute of Technology Madras, Chennai, India
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73
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Genotypic variability-based genome-wide association study identifies non-additive loci HLA-C and IL12B for psoriasis. J Hum Genet 2017; 63:289-296. [DOI: 10.1038/s10038-017-0350-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2017] [Revised: 09/04/2017] [Accepted: 09/05/2017] [Indexed: 12/19/2022]
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74
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Abadi A, Alyass A, Robiou du Pont S, Bolker B, Singh P, Mohan V, Diaz R, Engert JC, Yusuf S, Gerstein HC, Anand SS, Meyre D. Penetrance of Polygenic Obesity Susceptibility Loci across the Body Mass Index Distribution. Am J Hum Genet 2017; 101:925-938. [PMID: 29220676 PMCID: PMC5812888 DOI: 10.1016/j.ajhg.2017.10.007] [Citation(s) in RCA: 52] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2017] [Accepted: 10/12/2017] [Indexed: 12/17/2022] Open
Abstract
A growing number of single-nucleotide polymorphisms (SNPs) have been associated with body mass index (BMI) and obesity, but whether the effects of these obesity-susceptibility loci are uniform across the BMI distribution remains unclear. We studied the effects of 37 BMI-associated SNPs in 75,230 adults of European ancestry across BMI percentiles by using conditional quantile regression (CQR) and meta-regression (MR) models. The effects of nine SNPs (24%)-rs1421085 (FTO; p = 8.69 × 10-15), rs6235 (PCSK1; p = 7.11 × 10-6), rs7903146 (TCF7L2; p = 9.60 × 10-6), rs11873305 (MC4R; p = 5.08 × 10-5), rs12617233 (FANCL; p = 5.30 × 10-5), rs11672660 (GIPR; p = 1.64 × 10-4), rs997295 (MAP2K5; p = 3.25 × 10-4), rs6499653 (FTO; p = 6.23 × 10-4), and rs3824755 (NT5C2; p = 7.90 × 10-4)-increased significantly across the sample BMI distribution. We showed that such increases stemmed from unadjusted gene interactions that enhanced the effects of SNPs in persons with a high BMI. When 125 height-associated SNPs were analyzed for comparison, only one (<1%), rs6219 (IGF1, p = 1.80 × 10-4), showed effects that varied significantly across height percentiles. Cumulative gene scores of these SNPs (GS-BMI and GS-height) showed that only GS-BMI had effects that increased significantly across the sample distribution (BMI: p = 7.03 × 10-37; height: p = 0.499). Overall, these findings underscore the importance of gene-gene and gene-environment interactions in shaping the genetic architecture of BMI and advance a method for detecting such interactions by using only the sample outcome distribution.
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Affiliation(s)
- Arkan Abadi
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON L8S 4L8, Canada
| | - Akram Alyass
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON L8S 4L8, Canada
| | - Sebastien Robiou du Pont
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON L8S 4L8, Canada
| | - Ben Bolker
- Department of Mathematics and Statistics, McMaster University, Hamilton, ON L8S 4L8, Canada
| | - Pardeep Singh
- Department of Pathology and Molecular Medicine, McMaster University, Hamilton, ON L8S 4L8, Canada
| | - Viswanathan Mohan
- Madras Diabetes Research Foundation, Gopalapuram, Chennai 600086, India
| | - Rafael Diaz
- Estudios Clínicos Latino America, Paraguay 160, S2000CVD Rosario, Santa Fe, Argentina
| | | | - Salim Yusuf
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON L8S 4L8, Canada; Population Health Research Institute, McMaster University and Hamilton Health Sciences, Hamilton General Hospital, Hamilton, ON L8S 4L8, Canada; Department of Medicine, McMaster University, Hamilton, ON L8S 4L8, Canada
| | - Hertzel C Gerstein
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON L8S 4L8, Canada; Population Health Research Institute, McMaster University and Hamilton Health Sciences, Hamilton General Hospital, Hamilton, ON L8S 4L8, Canada; Department of Medicine, McMaster University, Hamilton, ON L8S 4L8, Canada
| | - Sonia S Anand
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON L8S 4L8, Canada; Population Health Research Institute, McMaster University and Hamilton Health Sciences, Hamilton General Hospital, Hamilton, ON L8S 4L8, Canada; Department of Medicine, McMaster University, Hamilton, ON L8S 4L8, Canada
| | - David Meyre
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON L8S 4L8, Canada; Department of Pathology and Molecular Medicine, McMaster University, Hamilton, ON L8S 4L8, Canada.
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75
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Forsberg SKG, Carlborg Ö. On the relationship between epistasis and genetic variance heterogeneity. JOURNAL OF EXPERIMENTAL BOTANY 2017; 68:5431-5438. [PMID: 28992256 DOI: 10.1093/jxb/erx283] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2017] [Accepted: 07/18/2017] [Indexed: 06/07/2023]
Abstract
Epistasis and genetic variance heterogeneity are two non-additive genetic inheritance patterns that are often, but not always, related. Here we use theoretical examples and empirical results from earlier analyses of experimental data to illustrate the connection between the two. This includes an introduction to the relationship between epistatic gene action, statistical epistasis, and genetic variance heterogeneity, and a brief discussion about how genetic processes other than epistasis can also give rise to genetic variance heterogeneity.
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Affiliation(s)
- Simon K G Forsberg
- Department of Medical Biochemistry and Microbiology, Uppsala University, Box 582, SE-75123 Uppsala, Sweden
| | - Örjan Carlborg
- Department of Medical Biochemistry and Microbiology, Uppsala University, Box 582, SE-75123 Uppsala, Sweden
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76
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McAllister K, Mechanic LE, Amos C, Aschard H, Blair IA, Chatterjee N, Conti D, Gauderman WJ, Hsu L, Hutter CM, Jankowska MM, Kerr J, Kraft P, Montgomery SB, Mukherjee B, Papanicolaou GJ, Patel CJ, Ritchie MD, Ritz BR, Thomas DC, Wei P, Witte JS. Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases. Am J Epidemiol 2017; 186:753-761. [PMID: 28978193 PMCID: PMC5860428 DOI: 10.1093/aje/kwx227] [Citation(s) in RCA: 116] [Impact Index Per Article: 16.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2016] [Revised: 03/14/2017] [Accepted: 03/16/2017] [Indexed: 12/25/2022] Open
Abstract
Recently, many new approaches, study designs, and statistical and analytical methods have emerged for studying gene-environment interactions (G×Es) in large-scale studies of human populations. There are opportunities in this field, particularly with respect to the incorporation of -omics and next-generation sequencing data and continual improvement in measures of environmental exposures implicated in complex disease outcomes. In a workshop called "Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases," held October 17-18, 2014, by the National Institute of Environmental Health Sciences and the National Cancer Institute in conjunction with the annual American Society of Human Genetics meeting, participants explored new approaches and tools that have been developed in recent years for G×E discovery. This paper highlights current and critical issues and themes in G×E research that need additional consideration, including the improved data analytical methods, environmental exposure assessment, and incorporation of functional data and annotations.
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Affiliation(s)
| | - Leah E. Mechanic
- Correspondence to Dr. Leah E. Mechanic, Genomic Epidemiology Branch, Epidemiology and Genomics Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, 9609 Medical Center Drive, Room 4E104, MSC 9763, Bethesda, MD 20892 (e-mail: )
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77
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Gauderman WJ, Mukherjee B, Aschard H, Hsu L, Lewinger JP, Patel CJ, Witte JS, Amos C, Tai CG, Conti D, Torgerson DG, Lee S, Chatterjee N. Update on the State of the Science for Analytical Methods for Gene-Environment Interactions. Am J Epidemiol 2017; 186:762-770. [PMID: 28978192 PMCID: PMC5859988 DOI: 10.1093/aje/kwx228] [Citation(s) in RCA: 74] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2016] [Revised: 04/24/2017] [Accepted: 04/25/2017] [Indexed: 12/14/2022] Open
Abstract
The analysis of gene-environment interaction (G×E) may hold the key for further understanding the etiology of many complex traits. The current availability of high-volume genetic data, the wide range in types of environmental data that can be measured, and the formation of consortiums of multiple studies provide new opportunities to identify G×E but also new analytical challenges. In this article, we summarize several statistical approaches that can be used to test for G×E in a genome-wide association study. These include traditional models of G×E in a case-control or quantitative trait study as well as alternative approaches that can provide substantially greater power. The latest methods for analyzing G×E with gene sets and with data in a consortium setting are summarized, as are issues that arise due to the complexity of environmental data. We provide some speculation on why detecting G×E in a genome-wide association study has thus far been difficult. We conclude with a description of software programs that can be used to implement most of the methods described in the paper.
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Affiliation(s)
- W. James Gauderman
- Correspondence to Dr. W. James Gauderman, Division of Biostatistics, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, 2001 North Soto Street, 202-K, Los Angeles, CA 90032 (e-mail: )
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78
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Ritchie MD, Davis JR, Aschard H, Battle A, Conti D, Du M, Eskin E, Fallin MD, Hsu L, Kraft P, Moore JH, Pierce BL, Bien SA, Thomas DC, Wei P, Montgomery SB. Incorporation of Biological Knowledge Into the Study of Gene-Environment Interactions. Am J Epidemiol 2017; 186:771-777. [PMID: 28978191 PMCID: PMC5860556 DOI: 10.1093/aje/kwx229] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2016] [Revised: 04/07/2017] [Accepted: 04/10/2017] [Indexed: 12/12/2022] Open
Abstract
A growing knowledge base of genetic and environmental information has greatly enabled the study of disease risk factors. However, the computational complexity and statistical burden of testing all variants by all environments has required novel study designs and hypothesis-driven approaches. We discuss how incorporating biological knowledge from model organisms, functional genomics, and integrative approaches can empower the discovery of novel gene-environment interactions and discuss specific methodological considerations with each approach. We consider specific examples where the application of these approaches has uncovered effects of gene-environment interactions relevant to drug response and immunity, and we highlight how such improvements enable a greater understanding of the pathogenesis of disease and the realization of precision medicine.
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Affiliation(s)
- Marylyn D. Ritchie
- Correspondence to Dr. Stephen B. Montgomery, Departments of Genetics and Pathology, Stanford University School of Medicine, Stanford, CA 94305 (e-mail: ); or Dr. Marylyn D. Ritchie, Geisinger Health System, 205 Hood Center for Health Research, Center Street, Danville, PA 17821(e-mail: )
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Stephen B. Montgomery
- Correspondence to Dr. Stephen B. Montgomery, Departments of Genetics and Pathology, Stanford University School of Medicine, Stanford, CA 94305 (e-mail: ); or Dr. Marylyn D. Ritchie, Geisinger Health System, 205 Hood Center for Health Research, Center Street, Danville, PA 17821(e-mail: )
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79
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San-Jose LM, Ducret V, Ducrest AL, Simon C, Roulin A. Beyond mean allelic effects: A locus at the major color gene MC1R associates also with differing levels of phenotypic and genetic (co)variance for coloration in barn owls. Evolution 2017; 71:2469-2483. [PMID: 28861897 DOI: 10.1111/evo.13343] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2017] [Revised: 08/09/2017] [Accepted: 08/11/2017] [Indexed: 01/05/2023]
Abstract
The mean phenotypic effects of a discovered variant help to predict major aspects of the evolution and inheritance of a phenotype. However, differences in the phenotypic variance associated to distinct genotypes are often overlooked despite being suggestive of processes that largely influence phenotypic evolution, such as interactions between the genotypes with the environment or the genetic background. We present empirical evidence for a mutation at the melanocortin-1-receptor gene, a major vertebrate coloration gene, affecting phenotypic variance in the barn owl, Tyto alba. The white MC1R allele, which associates with whiter plumage coloration, also associates with a pronounced phenotypic and additive genetic variance for distinct color traits. Contrarily, the rufous allele, associated with a rufous coloration, relates to a lower phenotypic and additive genetic variance, suggesting that this allele may be epistatic over other color loci. Variance differences between genotypes entailed differences in the strength of phenotypic and genetic associations between color traits, suggesting that differences in variance also alter the level of integration between traits. This study highlights that addressing variance differences of genotypes in wild populations provides interesting new insights into the evolutionary mechanisms and the genetic architecture underlying the phenotype.
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Affiliation(s)
- Luis M San-Jose
- Department of Ecology and Evolution, University of Lausanne, Biophore Building, CH-1015 Lausanne, Switzerland
| | - Valérie Ducret
- Department of Ecology and Evolution, University of Lausanne, Biophore Building, CH-1015 Lausanne, Switzerland
| | - Anne-Lyse Ducrest
- Department of Ecology and Evolution, University of Lausanne, Biophore Building, CH-1015 Lausanne, Switzerland
| | - Céline Simon
- Department of Ecology and Evolution, University of Lausanne, Biophore Building, CH-1015 Lausanne, Switzerland
| | - Alexandre Roulin
- Department of Ecology and Evolution, University of Lausanne, Biophore Building, CH-1015 Lausanne, Switzerland
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80
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Ivarsdottir EV, Steinthorsdottir V, Daneshpour MS, Thorleifsson G, Sulem P, Holm H, Sigurdsson S, Hreidarsson AB, Sigurdsson G, Bjarnason R, Thorsson AV, Benediktsson R, Eyjolfsson G, Sigurdardottir O, Olafsson I, Zeinali S, Azizi F, Thorsteinsdottir U, Gudbjartsson DF, Stefansson K. Effect of sequence variants on variance in glucose levels predicts type 2 diabetes risk and accounts for heritability. Nat Genet 2017; 49:1398-1402. [DOI: 10.1038/ng.3928] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2017] [Accepted: 07/11/2017] [Indexed: 12/18/2022]
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81
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Ran D, Daye ZJ. Gene expression variability and the analysis of large-scale RNA-seq studies with the MDSeq. Nucleic Acids Res 2017; 45:e127. [PMID: 28535263 PMCID: PMC5737414 DOI: 10.1093/nar/gkx456] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2016] [Revised: 04/10/2017] [Accepted: 05/19/2017] [Indexed: 12/21/2022] Open
Abstract
Rapidly decreasing cost of next-generation sequencing has led to the recent availability of large-scale RNA-seq data, that empowers the analysis of gene expression variability, in addition to gene expression means. In this paper, we present the MDSeq, based on the coefficient of dispersion, to provide robust and computationally efficient analysis of both gene expression means and variability on RNA-seq counts. The MDSeq utilizes a novel reparametrization of the negative binomial to provide flexible generalized linear models (GLMs) on both the mean and dispersion. We address challenges of analyzing large-scale RNA-seq data via several new developments to provide a comprehensive toolset that models technical excess zeros, identifies outliers efficiently, and evaluates differential expressions at biologically interesting levels. We evaluated performances of the MDSeq using simulated data when the ground truths are known. Results suggest that the MDSeq often outperforms current methods for the analysis of gene expression mean and variability. Moreover, the MDSeq is applied in two real RNA-seq studies, in which we identified functionally relevant genes and gene pathways. Specifically, the analysis of gene expression variability with the MDSeq on the GTEx human brain tissue data has identified pathways associated with common neurodegenerative disorders when gene expression means were conserved.
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Affiliation(s)
- Di Ran
- Mel and Enid Zuckerman College of Public Health, The University of Arizona, Tucson, AZ 85724, USA
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82
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Genotypic variability based association identifies novel non-additive loci DHCR7 and IRF4 in sero-negative rheumatoid arthritis. Sci Rep 2017; 7:5261. [PMID: 28706201 PMCID: PMC5509675 DOI: 10.1038/s41598-017-05447-1] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2017] [Accepted: 05/30/2017] [Indexed: 12/21/2022] Open
Abstract
Sero-negative rheumatoid arthritis (RA) is a highly heterogeneous disorder with only a few additive loci identified to date. We report a genotypic variability-based genome-wide association study (vGWAS) of six cohorts of sero-negative RA recruited in Europe and the US that were genotyped with the Immunochip. A two-stage approach was used: (1) a mixed model to partition dichotomous phenotypes into an additive component and non-additive residuals on the liability scale and (2) the Levene’s test to assess equality of the residual variances across genotype groups. The vGWAS identified rs2852853 (P = 1.3e-08, DHCR7) and rs62389423 (P = 1.8e-05, near IRF4) in addition to two previously identified loci (HLA-DQB1 and ANKRD55), which were all statistically validated using cross validation. DHCR7 encodes an enzyme important in cutaneous synthesis of vitamin D and DHCR7 mutations are believed to be important for early humans to adapt to Northern Europe where residents have reduced ultraviolet-B exposure and tend to have light skin color. IRF4 is a key locus responsible for skin color, with a vitamin D receptor-binding interval. These vGWAS results together suggest that vitamin D deficiency is potentially causal of sero-negative RA and provide new insights into the pathogenesis of the disorder.
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83
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The importance of gene-environment interactions in human obesity. Clin Sci (Lond) 2017; 130:1571-97. [PMID: 27503943 DOI: 10.1042/cs20160221] [Citation(s) in RCA: 108] [Impact Index Per Article: 15.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2016] [Accepted: 05/23/2016] [Indexed: 12/16/2022]
Abstract
The worldwide obesity epidemic has been mainly attributed to lifestyle changes. However, who becomes obese in an obesity-prone environment is largely determined by genetic factors. In the last 20 years, important progress has been made in the elucidation of the genetic architecture of obesity. In parallel with successful gene identifications, the number of gene-environment interaction (GEI) studies has grown rapidly. This paper reviews the growing body of evidence supporting gene-environment interactions in the field of obesity. Heritability, monogenic and polygenic obesity studies provide converging evidence that obesity-predisposing genes interact with a variety of environmental, lifestyle and treatment exposures. However, some skepticism remains regarding the validity of these studies based on several issues, which include statistical modelling, confounding, low replication rate, underpowered analyses, biological assumptions and measurement precision. What follows in this review includes (1) an introduction to the study of GEI, (2) the evidence of GEI in the field of obesity, (3) an outline of the biological mechanisms that may explain these interaction effects, (4) methodological challenges associated with GEI studies and potential solutions, and (5) future directions of GEI research. Thus far, this growing body of evidence has provided a deeper understanding of GEI influencing obesity and may have tremendous applications in the emerging field of personalized medicine and individualized lifestyle recommendations.
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84
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Shungin D, Deng WQ, Varga TV, Luan J, Mihailov E, Metspalu A, Morris AP, Forouhi NG, Lindgren C, Magnusson PKE, Pedersen NL, Hallmans G, Chu AY, Justice AE, Graff M, Winkler TW, Rose LM, Langenberg C, Cupples LA, Ridker PM, Wareham NJ, Ong KK, Loos RJF, Chasman DI, Ingelsson E, Kilpeläinen TO, Scott RA, Mägi R, Paré G, Franks PW. Ranking and characterization of established BMI and lipid associated loci as candidates for gene-environment interactions. PLoS Genet 2017; 13:e1006812. [PMID: 28614350 PMCID: PMC5489225 DOI: 10.1371/journal.pgen.1006812] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2016] [Revised: 06/28/2017] [Accepted: 05/10/2017] [Indexed: 11/30/2022] Open
Abstract
Phenotypic variance heterogeneity across genotypes at a single nucleotide polymorphism (SNP) may reflect underlying gene-environment (G×E) or gene-gene interactions. We modeled variance heterogeneity for blood lipids and BMI in up to 44,211 participants and investigated relationships between variance effects (Pv), G×E interaction effects (with smoking and physical activity), and marginal genetic effects (Pm). Correlations between Pv and Pm were stronger for SNPs with established marginal effects (Spearman’s ρ = 0.401 for triglycerides, and ρ = 0.236 for BMI) compared to all SNPs. When Pv and Pm were compared for all pruned SNPs, only BMI was statistically significant (Spearman’s ρ = 0.010). Overall, SNPs with established marginal effects were overrepresented in the nominally significant part of the Pv distribution (Pbinomial <0.05). SNPs from the top 1% of the Pm distribution for BMI had more significant Pv values (PMann–Whitney= 1.46×10−5), and the odds ratio of SNPs with nominally significant (<0.05) Pm and Pv was 1.33 (95% CI: 1.12, 1.57) for BMI. Moreover, BMI SNPs with nominally significant G×E interaction P-values (Pint<0.05) were enriched with nominally significant Pv values (Pbinomial = 8.63×10−9 and 8.52×10−7 for SNP × smoking and SNP × physical activity, respectively). We conclude that some loci with strong marginal effects may be good candidates for G×E, and variance-based prioritization can be used to identify them. Most contemporary studies of gene-environment interactions focus on gene variants that are known to bear strong and reliable associations with the traits of interest. The strategy is intuitive because it helps limit the number of tests performed by focusing on a relatively small number of gene variants. However, this approach is predicated on an implicit assumption that these loci are strong candidates for interactions owing to their established relationships with the index traits. The counter-argument is that, because these loci have highly consistent signals within and between populations that vary by environmental characteristics, the probability that these variants interact with other factors is low. The current analysis tests whether variants with strong marginal effects signals (i.e., those prioritized through conventional genome-wide association analyses) are strong or weak candidates for gene-environment interactions. Here we describe analyses focused on lipids and BMI that test this hypothesis by comparing marginal effect signals with variance effect signals and those derived from explicit genome-wide, gene-environment interaction analyses. We conclude that for BMI, there are features of the top-ranking marginal effect loci that render them stronger candidates for interactions than is true of variants with weaker marginal effects signals. These findings are likely to help optimize the efficiency of future gene-environment interaction analyses by providing evidence-based rankings for strong candidate loci.
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Affiliation(s)
- Dmitry Shungin
- Department of Clinical Sciences, Genetic & Molecular Epidemiology Unit, Lund University Diabetes Centre, Skåne University Hospital, Malmö, Sweden
- Department of Odontology, Umeå University, Umeå, Sweden
- Department of Public Health and Clinical Medicine, Unit of Medicine, Umeå University, Umeå, Sweden
- Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, MA, United States of America
| | - Wei Q. Deng
- Department of Statistical Sciences, University of Toronto, Toronto, Canada
| | - Tibor V. Varga
- Department of Clinical Sciences, Genetic & Molecular Epidemiology Unit, Lund University Diabetes Centre, Skåne University Hospital, Malmö, Sweden
- Novo Nordisk Foundation Center for Protein Research, Translational Disease Systems Biology Group, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Health Sciences, Exercise Physiology Group, Lund University, Lund, Sweden
| | - Jian'an Luan
- MRC Epidemiology Unit, University of Cambridge, Institute of Metabolic Science, Addenbrooke’s Hospital, Cambridge, United Kingdom
| | | | - Andres Metspalu
- Estonian Genome Center, University of Tartu, Tartu, Estonia
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
| | | | - Andrew P. Morris
- Estonian Genome Center, University of Tartu, Tartu, Estonia
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Department of Biostatistics, University of Liverpool, Liverpool, United Kingdom
| | - Nita G. Forouhi
- MRC Epidemiology Unit, University of Cambridge, Institute of Metabolic Science, Addenbrooke’s Hospital, Cambridge, United Kingdom
| | - Cecilia Lindgren
- Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, MA, United States of America
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
| | - Patrik K. E. Magnusson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Nancy L. Pedersen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Göran Hallmans
- Department of Biobank Research, Umeå University, Umeå, Sweden
| | - Audrey Y. Chu
- Harvard Medical School, Boston, MA, United States of America
| | - Anne E. Justice
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
| | - Mariaelisa Graff
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
| | - Thomas W. Winkler
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, DE, Germany
| | - Lynda M. Rose
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, United States of America
| | - Claudia Langenberg
- MRC Epidemiology Unit, University of Cambridge, Institute of Metabolic Science, Addenbrooke’s Hospital, Cambridge, United Kingdom
- Department of Epidemiology and Public Health, UCL London, United Kingdom
| | - L. Adrienne Cupples
- Department of Biostatistics, Boston University School of Public Health, Boston, MA
- The NHLBI Framingham Heart Study, Framingham, MA
| | - Paul M. Ridker
- Harvard Medical School, Boston, MA, United States of America
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, United States of America
| | - Nicholas J. Wareham
- MRC Epidemiology Unit, University of Cambridge, Institute of Metabolic Science, Addenbrooke’s Hospital, Cambridge, United Kingdom
| | - Ken K. Ong
- MRC Epidemiology Unit, University of Cambridge, Institute of Metabolic Science, Addenbrooke’s Hospital, Cambridge, United Kingdom
| | - Ruth J. F. Loos
- The Genetics of Obesity and Related Metabolic Traits Program, Icahn School of Medicine at Mount Sinai, New York, NY
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
- The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Daniel I. Chasman
- Harvard Medical School, Boston, MA, United States of America
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, United States of America
| | - Erik Ingelsson
- Science for Life Laboratory, Uppsala University, Uppsala, Sweden
- Department of Medical Sciences, Molecular Epidemiology, Uppsala University, Uppsala, Sweden
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, United States of America
| | - Tuomas O. Kilpeläinen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Robert A. Scott
- MRC Epidemiology Unit, University of Cambridge, Institute of Metabolic Science, Addenbrooke’s Hospital, Cambridge, United Kingdom
| | - Reedik Mägi
- Estonian Genome Center, University of Tartu, Tartu, Estonia
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
| | - Guillaume Paré
- Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Canada
- * E-mail: (PWF); (GP)
| | - Paul W. Franks
- Department of Clinical Sciences, Genetic & Molecular Epidemiology Unit, Lund University Diabetes Centre, Skåne University Hospital, Malmö, Sweden
- Department of Public Health and Clinical Medicine, Unit of Medicine, Umeå University, Umeå, Sweden
- Department of Nutrition, Harvard TH Chan School of Public Health, Boston, Massachusetts, United States of America
- Oxford Centre for Diabetes, Endocrinology & Metabolism, Radcliff Department of Medicine, University of Oxford, Oxford, United Kingdom
- * E-mail: (PWF); (GP)
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85
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Frånberg M, Strawbridge RJ, Hamsten A, de Faire U, Lagergren J, Sennblad B. Fast and general tests of genetic interaction for genome-wide association studies. PLoS Comput Biol 2017; 13:e1005556. [PMID: 28586362 PMCID: PMC5478145 DOI: 10.1371/journal.pcbi.1005556] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2016] [Revised: 06/20/2017] [Accepted: 05/09/2017] [Indexed: 11/29/2022] Open
Abstract
A complex disease has, by definition, multiple genetic causes. In theory, these causes could be identified individually, but their identification will likely benefit from informed use of anticipated interactions between causes. In addition, characterizing and understanding interactions must be considered key to revealing the etiology of any complex disease. Large-scale collaborative efforts are now paving the way for comprehensive studies of interaction. As a consequence, there is a need for methods with a computational efficiency sufficient for modern data sets as well as for improvements of statistical accuracy and power. Another issue is that, currently, the relation between different methods for interaction inference is in many cases not transparent, complicating the comparison and interpretation of results between different interaction studies. In this paper we present computationally efficient tests of interaction for the complete family of generalized linear models (GLMs). The tests can be applied for inference of single or multiple interaction parameters, but we show, by simulation, that jointly testing the full set of interaction parameters yields superior power and control of false positive rate. Based on these tests we also describe how to combine results from multiple independent studies of interaction in a meta-analysis. We investigate the impact of several assumptions commonly made when modeling interactions. We also show that, across the important class of models with a full set of interaction parameters, jointly testing the interaction parameters yields identical results. Further, we apply our method to genetic data for cardiovascular disease. This allowed us to identify a putative interaction involved in Lp(a) plasma levels between two ‘tag’ variants in the LPA locus (p = 2.42 ⋅ 10−09) as well as replicate the interaction (p = 6.97 ⋅ 10−07). Finally, our meta-analysis method is used in a small (N = 16,181) study of interactions in myocardial infarction. Interaction between organic molecules forms the basis of all biological systems. The availability of high-throughput genotyping and sequencing platforms enables us to cost-effectively genotype a large number of individuals. For sufficiently large datasets it is possible to reconstruct the genetic dependencies that underlie complex traits and diseases. However, there is a need for efficient statistical methodologies that can tackle the large sample size and computational resources required to study interaction. In this work we provide theory that reduces the required computational resources, and enable multiple research groups to effectively combine their results.
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Affiliation(s)
- Mattias Frånberg
- Cardiovascular Medicine Unit, Department of Medicine, Solna, Karolinska Institutet, Stockholm, Sweden
- Department of Numerical Analysis and Computer Science, Stockholm University, Stockholm, Sweden
- Science for Life Laboratory, Stockholm, Sweden
- * E-mail:
| | - Rona J. Strawbridge
- Cardiovascular Medicine Unit, Department of Medicine, Solna, Karolinska Institutet, Stockholm, Sweden
| | - Anders Hamsten
- Cardiovascular Medicine Unit, Department of Medicine, Solna, Karolinska Institutet, Stockholm, Sweden
| | | | - Ulf de Faire
- Division of Cardiovascular Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Cardiology, Karolinska University Hospital, Stockholm, Sweden
| | - Jens Lagergren
- Science for Life Laboratory, Stockholm, Sweden
- The School of Computer Science and Communications, KTH Royal Institute of Technology, Stockholm, Sweden
- Swedish e-science Research Center (SeRC), Stockholm, Sweden
| | - Bengt Sennblad
- Cardiovascular Medicine Unit, Department of Medicine, Solna, Karolinska Institutet, Stockholm, Sweden
- Science for Life Laboratory, Stockholm, Sweden
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86
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Hong C, Ning Y, Wei P, Cao Y, Chen Y. A semiparametric model for vQTL mapping. Biometrics 2017; 73:571-581. [PMID: 27861717 PMCID: PMC5780188 DOI: 10.1111/biom.12612] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2014] [Revised: 07/01/2016] [Accepted: 08/01/2016] [Indexed: 11/30/2022]
Abstract
Quantitative trait locus analysis has been used as an important tool to identify markers where the phenotype or quantitative trait is linked with the genotype. Most existing tests for single locus association with quantitative traits aim at the detection of the mean differences across genotypic groups. However, recent research has revealed functional genetic loci that affect the variance of traits, known as variability-controlling quantitative trait locus. In addition, it has been suggested that many genotypes have both mean and variance effects, while the mean effects or variance effects alone may not be strong enough to be detected. The existing methods accounting for unequal variances include the Levene's test, the Lepage test, and the D-test, but suffer from their limitations of lack of robustness or lack of power. We propose a semiparametric model and a novel pairwise conditional likelihood ratio test. Specifically, the semiparametric model is designed to identify the combined differences in higher moments among genotypic groups. The pairwise likelihood is constructed based on conditioning procedure, where the unknown reference distribution is eliminated. We show that the proposed pairwise likelihood ratio test has a simple asymptotic chi-square distribution, which does not require permutation or bootstrap procedures. Simulation studies show that the proposed test performs well in controlling Type I errors and having competitive power in identifying the differences across genotypic groups. In addition, the proposed test has certain robustness to model mis-specifications. The proposed test is illustrated by an example of identifying both mean and variances effects in body mass index using the Framingham Heart Study data.
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Affiliation(s)
- Chuan Hong
- Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA
| | - Yang Ning
- Department of Statistical Science, Cornell University, Ithaca, NY 14853, USA
| | - Peng Wei
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Ying Cao
- Department of Biostatistics, The University of Texas School of Public Health, Houston, TX 77030, USA
| | - Yong Chen
- Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA 19104, USA
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87
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Castaldi PJ, Cho MH, Liang L, Silverman EK, Hersh CP, Rice K, Aschard H. Screening for interaction effects in gene expression data. PLoS One 2017; 12:e0173847. [PMID: 28301596 PMCID: PMC5354413 DOI: 10.1371/journal.pone.0173847] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2016] [Accepted: 02/27/2017] [Indexed: 11/27/2022] Open
Abstract
Expression quantitative trait (eQTL) studies are a powerful tool for identifying genetic variants that affect levels of messenger RNA. Since gene expression is controlled by a complex network of gene-regulating factors, one way to identify these factors is to search for interaction effects between genetic variants and mRNA levels of transcription factors (TFs) and their respective target genes. However, identification of interaction effects in gene expression data pose a variety of methodological challenges, and it has become clear that such analyses should be conducted and interpreted with caution. Investigating the validity and interpretability of several interaction tests when screening for eQTL SNPs whose effect on the target gene expression is modified by the expression level of a transcription factor, we characterized two important methodological issues. First, we stress the scale-dependency of interaction effects and highlight that commonly applied transformation of gene expression data can induce or remove interactions, making interpretation of results more challenging. We then demonstrate that, in the setting of moderate to strong interaction effects on the order of what may be reasonably expected for eQTL studies, standard interaction screening can be biased due to heteroscedasticity induced by true interactions. Using simulation and real data analysis, we outline a set of reasonable minimum conditions and sample size requirements for reliable detection of variant-by-environment and variant-by-TF interactions using the heteroscedasticity consistent covariance-based approach.
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Affiliation(s)
- Peter J. Castaldi
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
- Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, Massachusetts, United States of America
| | - Michael H. Cho
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
- Pulmonary and Critical Care Division, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Liming Liang
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Edwin K. Silverman
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
- Pulmonary and Critical Care Division, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Craig P. Hersh
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
- Pulmonary and Critical Care Division, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Kenneth Rice
- Department of Biostatistics, University of Washington, Seattle, Washington, United States of America
| | - Hugues Aschard
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
- Centre de Bioinformatique, Biostatistique et Biologie Intégrative (C3BI), Institut Pasteur, Paris, France
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88
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Soave D, Sun L. A generalized Levene's scale test for variance heterogeneity in the presence of sample correlation and group uncertainty. Biometrics 2017; 73:960-971. [DOI: 10.1111/biom.12651] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2016] [Revised: 12/01/2016] [Accepted: 12/01/2016] [Indexed: 10/20/2022]
Affiliation(s)
- David Soave
- Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto; Toronto, Ontario M5T 3M7 Canada
- Program in Genetics and Genome Biology, Research Institute, The Hospital for Sick Children; Toronto, Ontario M5G 0A4 Canada
| | - Lei Sun
- Department of Statistical Sciences, University of Toronto; Toronto, Ontario M5S 3G3 Canada
- Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto; Toronto, Ontario M5T 3M7 Canada
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89
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Abstract
In addition to characterizing the distribution of genetic features of populations (mutation and allele frequencies; measures of Hardy-Weinberg equilibrium), genetic epidemiology and statistical genetics aim to explore and define the role of genomic variation in risk of disease or variation in traits of interest. To facilitate this kind of exploration, genetic epidemiology and statistical genetics address a series of questions: 1. Does the disease tend to cluster in families more than expected by chance alone? 2. Does the disease appear to follow a particular genetic model of transmission in families? 3. Does variation at a particular genomic position tend to cosegregate with disease in families? 4. Do specific genetic variants tend to be carried more frequently by those with disease than by those without these variants in a given population (or across families)? The first question can be examined using studies of familial aggregation or correlation. An ancillary question: "how much of the susceptibility to disease (or variation in disease-related traits) might be accounted for by genetic factors?" is typically answered by estimating heritability, the proportion of variance in a trait or in risk to a disease attributable to genetics. The second question can be formally tested using pedigrees for which disease affection status or trait values are available through a modeling approach known as segregation analysis. The third question can be answered with data on genomic markers in pedigrees with affected members informative for linkage, where meiotic cross-over events are estimated or assessed. The fourth question is answerable using genotype data on genomic markers on unrelated affected and unaffected individuals and/or families with affected members and unaffected members. All of these questions can also be explored for quantitative (or continuously distributed) traits by examining variation in trait values between family members or between unrelated individuals. While each of these questions and the analytical approaches for answering them is explored extensively in subsequent chapters (heritability in Chapters 8 and 9 ; segregation in Chapter 12 ; linkage in Chapters 13 - 17 ; and association in Chapters 18 - 20 ), this chapter focuses on statistical methods to address questions of familial aggregation of qualitative phenotypes (e.g., disease status) or quantitative phenotypes.While studies exploring genotype-phenotype correlations are arguably the most important and common type of statistical genetic study performed, these studies are performed under the assumption that genetic contributors at least partially explain risk of a disease or a trait of interest. This may not always be the case, especially with diseases or traits known to be strongly influenced by environmental factors. For this reason, before any of the last three questions described above can be answered, it is important to ask first whether the disease clusters among family members more than unrelated persons, as this constitutes evidence of a possible heritable contribution to disease, justifying the pursuit of studies answering the other questions. In this chapter, the underlying principles of familial aggregation studies are addressed to provide an understanding and set of analytical tools to help answer the question if diseases or traits of interest are likely to be heritable and therefore justify subsequent statistical genetic studies to identify specific genetic causes.
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Affiliation(s)
- Adam C Naj
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, 229 Blockley Hall, 423 Guardian Drive, Philadelphia, PA, 19104, USA.
- Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, 229 Blockley Hall, 423 Guardian Drive, Philadelphia, PA, 19104, USA.
- Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania Perelman School of Medicine, 229 Blockley Hall, 423 Guardian Drive, Philadelphia, PA, 19104, USA.
| | - Terri H Beaty
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, 615 N. Wolfe Street, Room W6513, Baltimore, MD, 21205, USA
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90
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Aschard H. A perspective on interaction effects in genetic association studies. Genet Epidemiol 2016; 40:678-688. [PMID: 27390122 PMCID: PMC5132101 DOI: 10.1002/gepi.21989] [Citation(s) in RCA: 63] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2016] [Revised: 05/20/2016] [Accepted: 06/05/2016] [Indexed: 11/29/2022]
Abstract
The identification of gene–gene and gene–environment interaction in human traits and diseases is an active area of research that generates high expectation, and most often lead to high disappointment. This is partly explained by a misunderstanding of the inherent characteristics of standard regression‐based interaction analyses. Here, I revisit and untangle major theoretical aspects of interaction tests in the special case of linear regression; in particular, I discuss variables coding scheme, interpretation of effect estimate, statistical power, and estimation of variance explained in regard of various hypothetical interaction patterns. Linking this components it appears first that the simplest biological interaction models—in which the magnitude of a genetic effect depends on a common exposure—are among the most difficult to identify. Second, I highlight the demerit of the current strategy to evaluate the contribution of interaction effects to the variance of quantitative outcomes and argue for the use of new approaches to overcome this issue. Finally, I explore the advantages and limitations of multivariate interaction models, when testing for interaction between multiple SNPs and/or multiple exposures, over univariate approaches. Together, these new insights can be leveraged for future method development and to improve our understanding of the genetic architecture of multifactorial traits.
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Affiliation(s)
- Hugues Aschard
- Department of Epidemiology, Harvard T.H. School of Public Health, Boston, Massachusetts, United States of America
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91
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Abstract
The genome is often the conduit through which environmental exposures convey their effects on health and disease. Whilst not all diseases act by directly perturbing the genome, the phenotypic responses are often genetically determined. Hence, whilst diseases are often defined has having differing degrees of genetic determination, genetic and environmental factors are, with few exceptions, inseparable features of most diseases, not least type 2 diabetes. It follows that to optimize diabetes, prevention and treatment will require that the etiological roles of genetic and environmental risk factors be jointly considered. As we discuss here, studies focused on quantifying gene-environment and gene-treatment interactions are gathering momentum and may eventually yield data that helps guide health-related choices and medical interventions for type 2 diabetes and other complex diseases.
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Affiliation(s)
- Paul W Franks
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Center, Department of Clinical Sciences, Clinical Research Center, Skåne University Hospital Malmö, Lund University, Building 91, Level 10, Jan Waldenströms gata 35, 205 02, Malmö, Sweden.
- Department of Public Health and Clinical Medicine, Umeå University, 90188, Umeå, Sweden.
- Department of Nutrition, Harvard School of Public Health, Boston, MA, 02115, USA.
| | - Guillaume Paré
- Population Health Research Institute, McMaster University, Hamilton General Hospital Campus, DB-CVSRI, 237 Barton Street East, Room C3103, Hamilton, ON, L8L 2X2, Canada
- Department of Pathology and Molecular Medicine, McMaster University, Hamilton, ON, Canada
- Department of Clinical Epidemiology and Biostatistics, Population Genomics Program, McMaster University, Hamilton, ON, Canada
- Thrombosis and Atherosclerosis Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
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92
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Zhang P, Lewinger JP, Conti D, Morrison JL, Gauderman WJ. Detecting Gene-Environment Interactions for a Quantitative Trait in a Genome-Wide Association Study. Genet Epidemiol 2016; 40:394-403. [PMID: 27230133 DOI: 10.1002/gepi.21977] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2015] [Revised: 02/23/2016] [Accepted: 04/04/2016] [Indexed: 11/06/2022]
Abstract
A genome-wide association study (GWAS) typically is focused on detecting marginal genetic effects. However, many complex traits are likely to be the result of the interplay of genes and environmental factors. These SNPs may have a weak marginal effect and thus unlikely to be detected from a scan of marginal effects, but may be detectable in a gene-environment (G × E) interaction analysis. However, a genome-wide interaction scan (GWIS) using a standard test of G × E interaction is known to have low power, particularly when one corrects for testing multiple SNPs. Two 2-step methods for GWIS have been previously proposed, aimed at improving efficiency by prioritizing SNPs most likely to be involved in a G × E interaction using a screening step. For a quantitative trait, these include a method that screens on marginal effects [Kooperberg and Leblanc, 2008] and a method that screens on variance heterogeneity by genotype [Paré et al., 2010] In this paper, we show that the Paré et al. approach has an inflated false-positive rate in the presence of an environmental marginal effect, and we propose an alternative that remains valid. We also propose a novel 2-step approach that combines the two screening approaches, and provide simulations demonstrating that the new method can outperform other GWIS approaches. Application of this method to a G × Hispanic-ethnicity scan for childhood lung function reveals a SNP near the MARCO locus that was not identified by previous marginal-effect scans.
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Affiliation(s)
- Pingye Zhang
- Department of Preventive Medicine, University of Southern California, Los Angeles, United States of America
| | - Juan Pablo Lewinger
- Department of Preventive Medicine, University of Southern California, Los Angeles, United States of America
| | - David Conti
- Department of Preventive Medicine, University of Southern California, Los Angeles, United States of America
| | - John L Morrison
- Department of Preventive Medicine, University of Southern California, Los Angeles, United States of America
| | - W James Gauderman
- Department of Preventive Medicine, University of Southern California, Los Angeles, United States of America
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93
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Wei WH, Bowes J, Plant D, Viatte S, Yarwood A, Massey J, Worthington J, Eyre S. Major histocompatibility complex harbors widespread genotypic variability of non-additive risk of rheumatoid arthritis including epistasis. Sci Rep 2016; 6:25014. [PMID: 27109064 PMCID: PMC4842957 DOI: 10.1038/srep25014] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2016] [Accepted: 04/08/2016] [Indexed: 11/10/2022] Open
Abstract
Genotypic variability based genome-wide association studies (vGWASs) can identify potentially interacting loci without prior knowledge of the interacting factors. We report a two-stage approach to make vGWAS applicable to diseases: firstly using a mixed model approach to partition dichotomous phenotypes into additive risk and non-additive environmental residuals on the liability scale and secondly using the Levene's (Brown-Forsythe) test to assess equality of the residual variances across genotype groups per marker. We found widespread significant (P < 2.5e-05) vGWAS signals within the major histocompatibility complex (MHC) across all three study cohorts of rheumatoid arthritis. We further identified 10 epistatic interactions between the vGWAS signals independent of the MHC additive effects, each with a weak effect but jointly explained 1.9% of phenotypic variance. PTPN22 was also identified in the discovery cohort but replicated in only one independent cohort. Combining the three cohorts boosted power of vGWAS and additionally identified TYK2 and ANKRD55. Both PTPN22 and TYK2 had evidence of interactions reported elsewhere. We conclude that vGWAS can help discover interacting loci for complex diseases but require large samples to find additional signals.
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Affiliation(s)
- Wen-Hua Wei
- Arthritis Research UK Centre for Genetics and Genomics, Institute of Inflammation and Repair, Faculty of Medical and Human Sciences, Manchester Academic Health Science Centre, University of Manchester, Oxford Road, Manchester M13 9PT, UK.,Department of Women's and Children's Health, Dunedin School of Medicine, University of Otago, Dunedin 9016, New Zealand
| | - John Bowes
- Arthritis Research UK Centre for Genetics and Genomics, Institute of Inflammation and Repair, Faculty of Medical and Human Sciences, Manchester Academic Health Science Centre, University of Manchester, Oxford Road, Manchester M13 9PT, UK
| | - Darren Plant
- Arthritis Research UK Centre for Genetics and Genomics, Institute of Inflammation and Repair, Faculty of Medical and Human Sciences, Manchester Academic Health Science Centre, University of Manchester, Oxford Road, Manchester M13 9PT, UK
| | - Sebastien Viatte
- Arthritis Research UK Centre for Genetics and Genomics, Institute of Inflammation and Repair, Faculty of Medical and Human Sciences, Manchester Academic Health Science Centre, University of Manchester, Oxford Road, Manchester M13 9PT, UK
| | - Annie Yarwood
- Arthritis Research UK Centre for Genetics and Genomics, Institute of Inflammation and Repair, Faculty of Medical and Human Sciences, Manchester Academic Health Science Centre, University of Manchester, Oxford Road, Manchester M13 9PT, UK
| | - Jonathan Massey
- Arthritis Research UK Centre for Genetics and Genomics, Institute of Inflammation and Repair, Faculty of Medical and Human Sciences, Manchester Academic Health Science Centre, University of Manchester, Oxford Road, Manchester M13 9PT, UK
| | - Jane Worthington
- Arthritis Research UK Centre for Genetics and Genomics, Institute of Inflammation and Repair, Faculty of Medical and Human Sciences, Manchester Academic Health Science Centre, University of Manchester, Oxford Road, Manchester M13 9PT, UK.,NIHR Manchester Musculoskeletal Biomedical Research Unit, Central Manchester NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
| | - Stephen Eyre
- Arthritis Research UK Centre for Genetics and Genomics, Institute of Inflammation and Repair, Faculty of Medical and Human Sciences, Manchester Academic Health Science Centre, University of Manchester, Oxford Road, Manchester M13 9PT, UK.,NIHR Manchester Musculoskeletal Biomedical Research Unit, Central Manchester NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
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94
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Sell-Kubiak E, Duijvesteijn N, Lopes MS, Janss LLG, Knol EF, Bijma P, Mulder HA. Genome-wide association study reveals novel loci for litter size and its variability in a Large White pig population. BMC Genomics 2015; 16:1049. [PMID: 26652161 PMCID: PMC4674943 DOI: 10.1186/s12864-015-2273-y] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2015] [Accepted: 12/03/2015] [Indexed: 01/11/2023] Open
Abstract
Background In many traits, not only individual trait levels are under genetic control, but also the variation around that level. In other words, genotypes do not only differ in mean, but also in (residual) variation around the genotypic mean. New statistical methods facilitate gaining knowledge on the genetic architecture of complex traits such as phenotypic variability. Here we study litter size (total number born) and its variation in a Large White pig population using a Double Hierarchical Generalized Linear model, and perform a genome-wide association study using a Bayesian method. Results In total, 10 significant single nucleotide polymorphisms (SNPs) were detected for total number born (TNB) and 9 SNPs for variability of TNB (varTNB). Those SNPs explained 0.83 % of genetic variance in TNB and 1.44 % in varTNB. The most significant SNP for TNB was detected on Sus scrofa chromosome (SSC) 11. A possible candidate gene for TNB is ENOX1, which is involved in cell growth and survival. On SSC7, two possible candidate genes for varTNB are located. The first gene is coding a swine heat shock protein 90 (HSPCB = Hsp90), which is a well-studied gene stabilizing morphological traits in Drosophila and Arabidopsis. The second gene is VEGFA, which is activated in angiogenesis and vasculogenesis in the fetus. Furthermore, the genetic correlation between additive genetic effects on TNB and on its variation was 0.49. This indicates that the current selection to increase TNB will also increase the varTNB. Conclusions To the best of our knowledge, this is the first study reporting SNPs associated with variation of a trait in pigs. Detected genomic regions associated with varTNB can be used in genomic selection to decrease varTNB, which is highly desirable to avoid very small or very large litters in pigs. However, the percentage of variance explained by those regions was small. The SNPs detected in this study can be used as indication for regions in the Sus scrofa genome involved in maintaining low variability of litter size, but further studies are needed to identify the causative loci.
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Affiliation(s)
- E Sell-Kubiak
- Animal Breeding and Genomics Center, Wageningen University, P.O. Box 338, 6700, Wageningen, AH, The Netherlands.
| | - N Duijvesteijn
- Topigs Norsvin Research Center B.V, P.O. Box 43, 6640, Beuningen, AA, The Netherlands.
| | - M S Lopes
- Topigs Norsvin Research Center B.V, P.O. Box 43, 6640, Beuningen, AA, The Netherlands.
| | - L L G Janss
- Department of Molecular Biology and Genetics, Aarhus University, P.O. Box 50, 8830, Tjele, Denmark.
| | - E F Knol
- Topigs Norsvin Research Center B.V, P.O. Box 43, 6640, Beuningen, AA, The Netherlands.
| | - P Bijma
- Animal Breeding and Genomics Center, Wageningen University, P.O. Box 338, 6700, Wageningen, AH, The Netherlands.
| | - H A Mulder
- Animal Breeding and Genomics Center, Wageningen University, P.O. Box 338, 6700, Wageningen, AH, The Netherlands.
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95
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Abstract
Whole-genome prediction (WGP) models that use single-nucleotide polymorphism marker information to predict genetic merit of animals and plants typically assume homogeneous residual variance. However, variability is often heterogeneous across agricultural production systems and may subsequently bias WGP-based inferences. This study extends classical WGP models based on normality, heavy-tailed specifications and variable selection to explicitly account for environmentally-driven residual heteroskedasticity under a hierarchical Bayesian mixed-models framework. WGP models assuming homogeneous or heterogeneous residual variances were fitted to training data generated under simulation scenarios reflecting a gradient of increasing heteroskedasticity. Model fit was based on pseudo-Bayes factors and also on prediction accuracy of genomic breeding values computed on a validation data subset one generation removed from the simulated training dataset. Homogeneous vs. heterogeneous residual variance WGP models were also fitted to two quantitative traits, namely 45-min postmortem carcass temperature and loin muscle pH, recorded in a swine resource population dataset prescreened for high and mild residual heteroskedasticity, respectively. Fit of competing WGP models was compared using pseudo-Bayes factors. Predictive ability, defined as the correlation between predicted and observed phenotypes in validation sets of a five-fold cross-validation was also computed. Heteroskedastic error WGP models showed improved model fit and enhanced prediction accuracy compared to homoskedastic error WGP models although the magnitude of the improvement was small (less than two percentage points net gain in prediction accuracy). Nevertheless, accounting for residual heteroskedasticity did improve accuracy of selection, especially on individuals of extreme genetic merit.
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96
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Liu Q, Chen LS, Nicolae DL, Pierce BL. A unified set-based test with adaptive filtering for gene-environment interaction analyses. Biometrics 2015; 72:629-38. [PMID: 26496228 DOI: 10.1111/biom.12428] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2014] [Revised: 07/01/2015] [Accepted: 09/01/2015] [Indexed: 12/16/2022]
Abstract
In genome-wide gene-environment interaction (GxE) studies, a common strategy to improve power is to first conduct a filtering test and retain only the SNPs that pass the filtering in the subsequent GxE analyses. Inspired by two-stage tests and gene-based tests in GxE analysis, we consider the general problem of jointly testing a set of parameters when only a few are truly from the alternative hypothesis and when filtering information is available. We propose a unified set-based test that simultaneously considers filtering on individual parameters and testing on the set. We derive the exact distribution and approximate the power function of the proposed unified statistic in simplified settings, and use them to adaptively calculate the optimal filtering threshold for each set. In the context of gene-based GxE analysis, we show that although the empirical power function may be affected by many factors, the optimal filtering threshold corresponding to the peak of the power curve primarily depends on the size of the gene. We further propose a resampling algorithm to calculate P-values for each gene given the estimated optimal filtering threshold. The performance of the method is evaluated in simulation studies and illustrated via a genome-wide gene-gender interaction analysis using pancreatic cancer genome-wide association data.
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Affiliation(s)
| | - Lin S Chen
- Department of Public Health Sciences, The University of Chicago, Chicago, Illinois 60637, U.S.A
| | - Dan L Nicolae
- Department of Medicine, Statistics, and Human Genetics, The University of Chicago, Chicago, Illinois 60637, U.S.A
| | - Brandon L Pierce
- Department of Public Health Sciences, The University of Chicago, Chicago, Illinois 60637, U.S.A.,Comprehensive Cancer Center, The University of Chicago, Chicago, Illinois 60637, U.S.A
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97
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Topless RK, Flynn TJ, Cadzow M, Stamp LK, Dalbeth N, Black MA, Merriman TR. Association of SLC2A9 genotype with phenotypic variability of serum urate in pre-menopausal women. Front Genet 2015; 6:313. [PMID: 26528330 PMCID: PMC4604317 DOI: 10.3389/fgene.2015.00313] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2015] [Accepted: 10/02/2015] [Indexed: 12/28/2022] Open
Abstract
The SLC2A9 gene, that encodes a renal uric acid reuptake transporter, has genetic variants that explain ∼3% of variance in urate levels. There are previous reports of non-additive interaction between SLC2A9 genotype and environmental factors which influence urate control. Therefore, our aim was to further investigate the general phenomenon that such non-additive interactions contribute to genotype-specific association with variance at SLC2A9. Data from 14135 European individuals were used in this analysis. The measure of variance was derived from a ranked inverse normal transformation of residuals obtained by regressing known urate-influencing factors (sex, age, and body mass index) against urate. Variant rs6449173 showed the most significant effect on serum urate variance at SLC2A9 (P = 7.9 × 10-14), which was maintained after accounting for the effect on average serum urate levels (P = 0.022). Noting the stronger effect in a sub-cohort that consisted of pre-menopausal women and younger men, the participants were stratified into males and pre-menopausal and post-menopausal women. This revealed a strong effect on variance in pre-menopausal women (P = 3.7 × 10-5) with a weak effect in post-menopausal women (P = 0.032) and no effect in men (P = 0.22). The T-allele of rs6449173, which associates with increased urate levels, was associated with the greater variance in urate. There was a non-additive interaction between rs6449173 genotype and female gender in control of serum urate levels that was driven by a greater increase in urate levels associated with the T-allele in women. Female hormones, and/or other factors they influence or are associated with (such as iron levels, temperature, testosterone) interact with SLC2A9 genotype in women to determine urate levels. The association of SLC2A9 with greater variance in pre-menopausal women may reflect the cyclical changes resulting from menstruation.
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Affiliation(s)
- Ruth K Topless
- Department of Biochemistry, University of Otago Dunedin, New Zealand
| | - Tanya J Flynn
- Department of Biochemistry, University of Otago Dunedin, New Zealand
| | - Murray Cadzow
- Department of Biochemistry, University of Otago Dunedin, New Zealand
| | - Lisa K Stamp
- Department of Medicine, University of Otago Christchurch, New Zealand
| | - Nicola Dalbeth
- Department of Medicine, University of Auckland Auckland, New Zealand
| | - Michael A Black
- Department of Biochemistry, University of Otago Dunedin, New Zealand
| | - Tony R Merriman
- Department of Biochemistry, University of Otago Dunedin, New Zealand
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98
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99
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Soave D, Corvol H, Panjwani N, Gong J, Li W, Boëlle PY, Durie PR, Paterson AD, Rommens JM, Strug LJ, Sun L. A Joint Location-Scale Test Improves Power to Detect Associated SNPs, Gene Sets, and Pathways. Am J Hum Genet 2015; 97:125-38. [PMID: 26140448 PMCID: PMC4572492 DOI: 10.1016/j.ajhg.2015.05.015] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2015] [Accepted: 05/26/2015] [Indexed: 11/28/2022] Open
Abstract
Gene-based, pathway, and other multivariate association methods are motivated by the possibility of GxG and GxE interactions; however, accounting for such interactions is limited by the challenges associated with adequate modeling information. Here we propose an easy-to-implement joint location-scale (JLS) association testing framework for single-variant and multivariate analysis that accounts for interactions without explicitly modeling them. We apply the JLS method to a gene-set analysis of cystic fibrosis (CF) lung disease, which is influenced by multiple environmental and genetic factors. We identify and replicate an association between the constituents of the apical plasma membrane and CF lung disease (p = 0.0099 and p = 0.0180, respectively) and highlight a role for the SLC9A3-SLC9A3R1/2-EZR complex in contributing to CF lung disease. Many association studies could benefit from re-analysis with the JLS method that leverages complex genetic architecture for SNP, gene, and pathway identification. Analytical verification, simulation, and additional proof-of-principle applications support our approach.
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Affiliation(s)
- David Soave
- Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5T 3M7, Canada; Program in Genetics and Genome Biology, Research Institute, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada
| | - Harriet Corvol
- Assistance Publique-Hôpitaux de Paris (AP-HP), Trousseau Hospital, Pediatric Pulmonology Department; Institut National de la Santé et la Recherche Médicale (INSERM), UMR_S 938, CDR Saint-Antoine, 75012 Paris, France; Sorbonne Universités, Université Pierre et Marie Curie (UPMC) Paris 06, 75005 Paris, France
| | - Naim Panjwani
- Program in Genetics and Genome Biology, Research Institute, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada
| | - Jiafen Gong
- Program in Genetics and Genome Biology, Research Institute, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada
| | - Weili Li
- Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5T 3M7, Canada; Program in Genetics and Genome Biology, Research Institute, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada
| | - Pierre-Yves Boëlle
- Sorbonne Universités, Université Pierre et Marie Curie (UPMC) Paris 06, 75005 Paris, France; AP-HP, Saint-Antoine Hospital, Biostatistics Department, INSERM, UMR_S 1136, 75012 Paris, France
| | - Peter R Durie
- Program in Physiology and Experimental Medicine, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada; Department of Pediatrics, University of Toronto, Toronto, ON M5S 1A1, Canada
| | - Andrew D Paterson
- Program in Genetics and Genome Biology, Research Institute, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada; Division of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5T 3M7, Canada
| | - Johanna M Rommens
- Program in Genetics and Genome Biology, Research Institute, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Lisa J Strug
- Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5T 3M7, Canada; Program in Genetics and Genome Biology, Research Institute, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada.
| | - Lei Sun
- Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5T 3M7, Canada; Department of Statistical Sciences, University of Toronto, Toronto, ON M5S 3G3, Canada.
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Catanzaro D, Rancan S, Orso G, Dall'Acqua S, Brun P, Giron MC, Carrara M, Castagliuolo I, Ragazzi E, Caparrotta L, Montopoli M. Boswellia serrata Preserves Intestinal Epithelial Barrier from Oxidative and Inflammatory Damage. PLoS One 2015. [PMID: 23209806 DOI: 10.1371/journal] [Citation(s) in RCA: 457] [Impact Index Per Article: 50.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Aminosalicylates, corticosteroids and immunosuppressants are currently the therapeutic choices in inflammatory bowel diseases (IBD), however, with limited remission and often serious side effects. Meanwhile complementary and alternative medicine (CAM) use is increasing, particularly herbal medicine. Boswellia serrata is a traditional Ayurvedic remedy with anti-inflammatory properties, of interest for its usefulness in IBDs. The mechanism of this pharmacological potential of Boswellia serrata was investigated in colonic epithelial cell monolayers exposed to H2O2 or INF-γ+TNF-α, chosen as in vitro experimental model of intestinal inflammation. The barrier function was evaluated by the transepithelial electrical resistance (TEER) and paracellular permeability assay, and by the tight junction proteins (zonula occludens-1, ZO-1 and occludin) immunofluorescence. The expression of phosphorylated NF-κB and reactive oxygen species (ROS) generation were determined by immunoblot and cytofluorimetric assay, respectively. Boswellia serrata oleo-gum extract (BSE) and its pure derivative acetyl-11-keto-β-boswellic acid (AKBA), were tested at 0.1-10 μg/ml and 0.027 μg/ml, respectively. BSE and AKBA safety was demonstrated by no alteration of intestinal cell viability and barrier function and integrity biomarkers. H2O2 or INF-γ+TNF-α treatment of Caco-2 cell monolayers significantly reduced TEER, increased paracellular permeability and caused the disassembly of tight junction proteins occludin and ZO-1. BSE and AKBA pretreatment significantly prevented functional and morphological alterations and also the NF-κB phosphorylation induced by the inflammatory stimuli. At the same concentrations BSE and AKBA counteracted the increase of ROS caused by H2O2 exposure. Data showed the positive correlation of the antioxidant activity with the mechanism involved in the physiologic maintenance of the integrity and function of the intestinal epithelium. This study elucidates the pharmacological mechanisms mediated by BSE, in protecting intestinal epithelial barrier from inflammatory damage and supports its use as safe adjuvant in patients affected by IBD.
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Affiliation(s)
- Daniela Catanzaro
- Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Largo E. Meneghetti 2, 35131, Padova, Italy
| | - Serena Rancan
- Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Largo E. Meneghetti 2, 35131, Padova, Italy
| | | | - Stefano Dall'Acqua
- Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Largo E. Meneghetti 2, 35131, Padova, Italy
| | - Paola Brun
- Department of Molecular Medicine, University of Padova, via Gabelli 63, 35121, Padova, Italy
| | - Maria Cecilia Giron
- Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Largo E. Meneghetti 2, 35131, Padova, Italy
| | - Maria Carrara
- Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Largo E. Meneghetti 2, 35131, Padova, Italy
| | - Ignazio Castagliuolo
- Department of Molecular Medicine, University of Padova, via Gabelli 63, 35121, Padova, Italy
| | - Eugenio Ragazzi
- Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Largo E. Meneghetti 2, 35131, Padova, Italy
| | - Laura Caparrotta
- Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Largo E. Meneghetti 2, 35131, Padova, Italy
| | - Monica Montopoli
- Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Largo E. Meneghetti 2, 35131, Padova, Italy
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