Simple F Test Reveals Gene-Gene Interactions in Case-Control Studies
Author(s) -
Guanjie Chen,
Ao Yuan,
Jie Zhou,
Amy R. Bentley,
Adebowale Adeyemo,
Charles N. Rotimi
Publication year - 2012
Publication title -
bioinformatics and biology insights
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.556
H-Index - 23
ISSN - 1177-9322
DOI - 10.4137/bbi.s9867
Subject(s) - genome wide association study , genetics , gene , computational biology , missing heritability problem , biology , heritability , genetic association , test statistic , single nucleotide polymorphism , statistical hypothesis testing , statistics , mathematics , genotype
Missing heritability is still a challenge for Genome Wide Association Studies (GWAS). Gene-gene interactions may partially explain this residual genetic influence and contribute broadly to complex disease. To analyze the gene-gene interactions in case-control studies of complex disease, we propose a simple, non-parametric method that utilizes the F-statistic. This approach consists of three steps. First, we examine the joint distribution of a pair of SNPs in cases and controls separately. Second, an F-test is used to evaluate the ratio of dependence in cases to that of controls. Finally, results are adjusted for multiple tests. This method was used to evaluate gene-gene interactions that are associated with risk of Type 2 Diabetes among African Americans in the Howard University Family Study. We identified 18 gene-gene interactions (P < 0.0001). Compared with the commonly-used logistical regression method, we demonstrate that the F-ratio test is an efficient approach to measuring gene-gene interactions, especially for studies with limited sample size.
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