Abstract
Environment has long been known to play an important part in disease etiology. However, not many genome-wide association studies take environmental factors into consideration. There is also a need for new methods to identify the gene-environment interactions. In this study, we propose a 2-step approach incorporating an influence measure that capturespure gene-environment effect. We found that pure gene-age interaction has a stronger association than considering the genetic effect alone for systolic blood pressure, measured by counting the number of single-nucleotide polymorphisms (SNPs)reaching a certain significance level. We analyzed the subjects by dividing them into two age groups and found no overlap in the top identified SNPs between them. This suggested that age might have a nonlinear effect on genetic association. Furthermore, the scores of the top SNPs for the two age subgroups were about 3times those obtained when using all subjects for systolic blood pressure. In addition, the scores of the older age subgroup were much higher than those for the younger group. The results suggest that genetic effects are stronger in older age and that genetic association studies should take environmental effects into consideration, especially age.
Highlights
Gene-environment interactions (G × E) have long been known to play an important role in complex disease etiology
For systolic blood pressure (SBP), the pure gene-age interaction was stronger than the main effect of single-nucleotide polymorphisms (SNPs) alone
Detecting pure gene-environment effects Pure gene-age association is stronger than SNP main effect for SBP Using the 2-step approach, the I-score of the pure interactions of G × E was calculated after the main effects of both SNP and environmental factorswere removed; p values were obtained by permuting the phenotype 107
Summary
Gene-environment interactions (G × E) have long been known to play an important role in complex disease etiology. Understanding these will reduce the bias in variable selection because of different cohort exposure to theenvironment [1]. Previous methods of studying G × E effects have mainly included candidate genes, case-only design, and family-based association studies [1,2]. These methods have made their respective assumptions in terms of biological knowledge, independence of gene and environment, and kinship information. With the emergence of genome-wide association studies (GWAS), data mining methods, such as
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