Literature DB >> 34855050

Modeling Interaction and Dispersion Effects in the Analysis of Gene-by-Environment Interaction.

Benjamin W Domingue1, Klint Kanopka2, Travis T Mallard3, Sam Trejo4, Elliot M Tucker-Drob5.   

Abstract

Genotype-by-environment interaction (GxE) studies probe heterogeneity in response to risk factors or interventions. Popular methods for estimation of GxE examine multiplicative interactions between individual genetic and environmental measures. However, risk factors and interventions may modulate the total variance of an epidemiological outcome that itself represents the aggregation of many other etiological components. We expand the traditional GxE model to directly model genetic and environmental moderation of the dispersion of the outcome. We derive a test statistic, [Formula: see text], for inferring whether an interaction identified between individual genetic and environmental measures represents a more general pattern of moderation of the total variance in the phenotype by either the genetic or the environmental measure. We validate our method via extensive simulation, and apply it to investigate genotype-by-birth year interactions for Body Mass Index (BMI) with polygenic scores in the Health and Retirement Study (N = 11,586) and individual genetic variants in the UK Biobank (N = 380,605). We find that changes in the penetrance of a genome-wide polygenic score for BMI across birth year are partly representative of a more general pattern of expanding BMI variation across generations. Three individual variants found to be more strongly associated with BMI among later born individuals, were also associated with the magnitude of variability in BMI itself within any given birth year, suggesting that they may confer general sensitivity of BMI to a range of unmeasured factors beyond those captured by birth year. We introduce an expanded GxE regression model that explicitly models genetic and environmental moderation of the dispersion of the outcome under study. This approach can determine whether GxE interactions identified are specific to the measured predictors or represent a more general pattern of moderation of the total variance in the outcome by the genetic and environmental measures.
© 2021. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Entities:  

Keywords:  Gene × environment interaction, GxE; Gene-by-environment interaction; G×E; Heteroscedasticity; vQTL

Mesh:

Year:  2021        PMID: 34855050      PMCID: PMC8958844          DOI: 10.1007/s10519-021-10090-8

Source DB:  PubMed          Journal:  Behav Genet        ISSN: 0001-8244            Impact factor:   2.805


  34 in total

1.  Variance components models for gene-environment interaction in twin analysis.

Authors:  Shaun Purcell
Journal:  Twin Res       Date:  2002-12

2.  Biometric and developmental gene-environment interactions: looking back, moving forward.

Authors:  James Tabery
Journal:  Dev Psychopathol       Date:  2007

3.  Trends in Obesity Among Adults in the United States, 2005 to 2014.

Authors:  Katherine M Flegal; Deanna Kruszon-Moran; Margaret D Carroll; Cheryl D Fryar; Cynthia L Ogden
Journal:  JAMA       Date:  2016-06-07       Impact factor: 56.272

4.  Leveraging phenotypic variability to identify genetic interactions in human phenotypes.

Authors:  Andrew R Marderstein; Emily R Davenport; Scott Kulm; Cristopher V Van Hout; Olivier Elemento; Andrew G Clark
Journal:  Am J Hum Genet       Date:  2020-12-15       Impact factor: 11.025

5.  Identifying loci affecting trait variability and detecting interactions in genome-wide association studies.

Authors:  Alexander I Young; Fabian L Wauthier; Peter Donnelly
Journal:  Nat Genet       Date:  2018-10-15       Impact factor: 38.330

6.  Trends in Obesity Prevalence by Race and Hispanic Origin-1999-2000 to 2017-2018.

Authors:  Cynthia L Ogden; Cheryl D Fryar; Crescent B Martin; David S Freedman; Margaret D Carroll; Qiuping Gu; Craig M Hales
Journal:  JAMA       Date:  2020-09-22       Impact factor: 56.272

Review 7.  Gene × environment interaction studies have not properly controlled for potential confounders: the problem and the (simple) solution.

Authors:  Matthew C Keller
Journal:  Biol Psychiatry       Date:  2013-10-15       Impact factor: 13.382

8.  Comparison of Sociodemographic and Health-Related Characteristics of UK Biobank Participants With Those of the General Population.

Authors:  Anna Fry; Thomas J Littlejohns; Cathie Sudlow; Nicola Doherty; Ligia Adamska; Tim Sprosen; Rory Collins; Naomi E Allen
Journal:  Am J Epidemiol       Date:  2017-11-01       Impact factor: 4.897

9.  Genotype-by-environment interactions inferred from genetic effects on phenotypic variability in the UK Biobank.

Authors:  Huanwei Wang; Futao Zhang; Jian Zeng; Yang Wu; Kathryn E Kemper; Angli Xue; Min Zhang; Joseph E Powell; Michael E Goddard; Naomi R Wray; Peter M Visscher; Allan F McRae; Jian Yang
Journal:  Sci Adv       Date:  2019-08-14       Impact factor: 14.136

10.  Power and predictive accuracy of polygenic risk scores.

Authors:  Frank Dudbridge
Journal:  PLoS Genet       Date:  2013-03-21       Impact factor: 5.917

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.