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

2007 ◽  
Vol 19 (4) ◽  
pp. 961-976 ◽  
Author(s):  
James Tabery

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.

Author(s):  
Ji-Hyung Shin ◽  
Claire Infante-Rivard ◽  
Brad McNeney ◽  
Jinko Graham

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 (


2013 ◽  
Vol 16 (3) ◽  
pp. 701-711 ◽  
Author(s):  
Torsten Klengel ◽  
Elisabeth B. Binder

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.


Author(s):  
Daniel A. Briley

As a field, behavior genetics has a long and often underappreciated focus on environmental and situational factors. This chapter describes the methodological details and empirical findings of this line of work, as well as what situation research can gain from behavior genetics and vice versa. Genetically informative designs offer tools to quantify the extent to which people actively create their situational experiences as opposed to randomly encountering them, and novel advances in situation research have the potential to clarify the scattered history of environmental variables in behavioral genetics. Current progress in personality psychology will be highlighted. Parallels between behavior genetics and personality work can be found both in terms of mechanisms (e.g., gene-environment correlation and gene × environment interaction contrasting with selection effects and person × situation effects) and explanatory pitfalls. Researchers interested in delineating the pathways from situations to behavior would do well to draw from and build upon work in behavior genetics.


Author(s):  
Kenneth E. Westerman ◽  
Duy T. Pham ◽  
Liang Hong ◽  
Ye Chen ◽  
Magdalena Sevilla-González ◽  
...  

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.


2021 ◽  
Vol 12 ◽  
Author(s):  
Qi Yang ◽  
Na Pu ◽  
Xiao-Yao Li ◽  
Xiao-Lei Shi ◽  
Wei-Wei Chen ◽  
...  

The etiology of hypertriglyceridemia (HTG) and acute pancreatitis (AP) is complex. Herein, we dissected the underlying etiology in a patient with HTG and AP. The patient had a 20-year history of heavy alcohol consumption and an 8-year history of mild HTG. He was hospitalized for alcohol-triggered AP, with a plasma triglyceride (TG) level up to 21.4 mmol/L. A temporary rise in post-heparin LPL concentration (1.5–2.5 times of controls) was noted during the early days of AP whilst LPL activity was consistently low (50∼70% of controls). His TG level rapidly decreased to normal in response to treatment, and remained normal to borderline high during a ∼3-year follow-up period during which he had abstained completely from alcohol. Sequencing of the five primary HTG genes (i.e., LPL, APOC2, APOA5, GPIHBP1 and LMF1) identified two heterozygous variants. One was the common APOA5 c.553G > T (p.Gly185Cys) variant, which has been previously associated with altered TG levels as well as HTG-induced acute pancreatitis (HTG-AP). The other was a rare variant in the LPL gene, c.756T > G (p.Ile252Met), which was predicted to be likely pathogenic and found experimentally to cause a 40% loss of LPL activity without affecting either protein synthesis or secretion. We provide evidence that both a gene-gene interaction (between the common APOA5 variant and the rare LPL variant) and a gene-environment interaction (between alcohol and digenic inheritance) might have contributed to the development of mild HTG and alcohol-triggered AP in the patient, thereby improving our understanding of the complex etiology of HTG and HTG-AP.


1997 ◽  
Vol 78 (01) ◽  
pp. 457-461 ◽  
Author(s):  
S E Humphries ◽  
A Panahloo ◽  
H E Montgomery ◽  
F Green ◽  
J Yudkin

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