Gene-Environment Interaction and the Mapping of Complex Traits: Some Statistical Models and Their Implications

Abstract Major depressive disorder (MDD) is responsible for an increasing individual and global health burden. Extensive research on the genetic disposition to develop MDD and to predict the response to antidepressant treatment has yet failed to identify strong genetic effects. The concept of gene × environment interaction takes into account that environmental factors have been identified as important components in the development of MDD and combines both, genetic predisposition and environmental exposure, to elucidate complex traits such as MDD. Here, we review the current research on gene × environment interactions with regard to the development of MDD as well as response to antidepressant treatment. We hypothesize that gene × environment interactions delineate specific biological subtypes of depression and that individuals with such pathophysiological distinct types of depression will likely respond to different treatments. The elucidation of gene × environment interactions may thus not only help to understand the pathophysiology of MDD but could also provide markers for a personalized antidepressant therapy.

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A data-smoothing approach to explore and test gene-environment interaction in case-parent trios

Statistical Applications in Genetics and Molecular Biology ◽

10.1515/sagmb-2013-0023 ◽

2014 ◽

Vol 13 (2) ◽

Author(s):

Ji-Hyung Shin ◽

Claire Infante-Rivard ◽

Brad McNeney ◽

Jinko Graham

Keyword(s):

Complex Traits ◽

Environment Interaction ◽

Data Smoothing ◽

Joint Effects ◽

Statistical Interaction ◽

Gene Environment Interaction ◽

Gene Environment ◽

Genes And Environment ◽

Test Gene

AbstractComplex traits result from an interplay between genes and environment. A better understanding of their joint effects can help refine understanding of the epidemiology of the trait. Various tests have been proposed to assess the statistical interaction between genes and the environment (

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Biometric and developmental gene–environment interactions: Looking back, moving forward

Development and Psychopathology ◽

10.1017/s0954579407000478 ◽

2007 ◽

Vol 19 (4) ◽

pp. 961-976 ◽

Cited By ~ 46

Author(s):

James Tabery

Keyword(s):

Complex Traits ◽

Environment Interaction ◽

Developmental Gene ◽

Statistical Interaction ◽

Gene Environment Interaction ◽

History Of Research ◽

Gene Environment ◽

Lancelot Hogben ◽

History Of ◽

Looking Back

AbstractA history of research on gene–environment interaction (G × E) is provided in this article, revealing the fact that there have actually been two distinct concepts of G × E since the very origins of this research. R. A. Fisher introduced what I call the biometric concept of G × E (G × EB), whereas Lancelot Hogben introduced what I call the developmental concept of G × E (G × ED). Much of the subsequent history of research on G × E has largely consisted of the separate legacies of these separate concepts, along with the (sometimes acrimonious) disputes that have arisen time and again when employers of each have argued over the appropriate way to conceptualize the phenomenon. With this history in place, more recent attempts to distinguish between different concepts of G × E are considered, paying particular attention to the commonly made distinction between “statistical interaction” and “interactionism,” and Michael Rutter's distinction between statistical interaction and “the biological concept of interaction.” I argue that the history of the separate legacies of G × EB and G × ED better supports Rutter's analysis of the situation and that this analysis best paves the way for an integrative relationship between the various scientists investigating the place of G × E in the etiology of complex traits.

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GEM: Scalable and flexible gene-environment interaction analysis in millions of samples

10.1101/2020.05.13.090803 ◽

2020 ◽

Cited By ~ 1

Author(s):

Kenneth E. Westerman ◽

Duy T. Pham ◽

Liang Hong ◽

Ye Chen ◽

Magdalena Sevilla-González ◽

...

Keyword(s):

Complex Traits ◽

Large Scale ◽

Interaction Analysis ◽

Computation Time ◽

Supplementary Information ◽

Environment Interaction ◽

Gene Environment Interaction ◽

Gene Environment ◽

Software Programs ◽

The Uk

ABSTRACTMotivationGene-environment interaction (GEI) studies are a general framework that can be used to identify genetic variants that modify the effects of environmental, physiological, lifestyle, or treatment effects on complex traits. Moreover, accounting for GEIs can enhance our understanding of the genetic architecture of complex diseases. However, commonly-used statistical software programs for GEI studies are either not applicable to testing certain types of GEI hypotheses or have not been optimized for use in large samples.ResultsHere, we develop a new software program, GEM (Gene-Environment interaction analysis in Millions of samples), which supports the inclusion of multiple GEI terms, adjustment for GEI covariates, and robust inference, while allowing multi-threading to reduce computation time. GEM can conduct GEI tests as well as joint tests of genetic effects for both continuous and binary phenotypes. Through simulations, we demonstrate that GEM scales to millions of samples while addressing limitations of existing software programs. We additionally conduct a gene-sex interaction analysis on waist-hip ratio in 352,768 unrelated individuals from the UK Biobank, identifying 39 novel loci in the joint test that have not previously been reported in combined or sex-specific analyses. Our results demonstrate that GEM can facilitate the next generation of large-scale GEI studies and help advance our understanding of genomic contributions to complex traits.AvailabilityGEM is freely available as an open source project at https://github.com/large-scale-gxe-methods/[email protected], [email protected] informationSupplementary data are available at Bioinformatics online.

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