Assessing Gene-Environment Interactions in Genome-Wide Association Studies: Statistical Approaches

Abstract

This PDF document was made available from www.rti.org as a public service of RTI International. More information about RTI Press can be found at http://www.rti.org/rtipress. RTI International is an independent, nonprofit research organization dedicated to improving the human condition by turning knowledge into practice. The RTI Press mission is to disseminate information about RTI research, analytic tools, and technical expertise to a national and international audience. RTI Press publications are peer-reviewed by at least two independent substantive experts and one or more Press editors. AbstractIn this report, we address a scenario that uses synthetic genotype case-control data that is influenced by environmental factors in a genome-wide association study (GWAS) context. The precise way the environmental influence contributes to a given phenotype is typically unknown. Therefore, our study evaluates how to approach a GWAS that may have an environmental component. Specifically, we assess different statistical models in the context of a GWAS to make association predictions when the form of the environmental influence is questionable. We used a simulation approach to generate synthetic data corresponding to a variety of possible environmental-genetic models, including a "main effects only" model as well as a "main effects with interactions" model. Our method takes into account the strength of the association between phenotype and both genotype and environmental factors, but we focus on low-risk genetic and environmental risks that necessitate using large sample sizes (N = 10,000 and 200,000) to predict associations with high levels of confidence. We also simulated different Mendelian gene models, and we analyzed how the collection of factors influences statistical power in the context of a GWAS. Using simulated data provides a "truth set" of known outcomes such that the association-affecting factors can be unambiguously determined. We also test different statistical methods to determine their performance properties. Our results suggest that the chances of predicting an association in a GWAS is reduced if an environmental effect is present and the statistical model does not adjust for that effect. This is especially true if the environmental effect and genetic marker do not have an interaction effect. The functional form of the statistical model also matters. The more accurately the form of the environmental influence is portrayed by the statistical model, the more accurate the prediction will be. Finally, even with very large samples sizes, association predictions involving recessive markers with low risk can be poor.

Suitability of GWAS as a Tool to Discover SNPs Associated with Tick Resistance in Cattle: A Review

“…GxE interaction contributes to the genetic architecture of complex traits and it affects the chances of discovering a true association between phenotype and genotype in GWASs. According to [107], failure to adjust for environmental effects results in the reduced chances of predicting an association. This has been proven through assimilation studies, intensively in human epidemiological studies [108][109][110] and plant studies [111,112].…”

The Joint Link of the rs1051730 and rs1902341 Polymorphisms and Cigarette Smoking to Peripheral Artery Disease and Atherosclerotic Lesions of Different Arterial Beds

“…However, non-replication of original findings discovered by GWAS and the inconsistence of their results have been common features of complex multifactorial diseases, including PAD [ 9 , 10 , 11 ]. Although genetic differences between populations may explain non-replication and inconsistent GWAS results, environmental risk factors and gene-environment interactions may account for a significant portion of such differences [ 12 ]. The joint effects of cigarette smoking, a major risk factor of PAD, and GWAS loci on disease susceptibility have not been systematically investigated.…”