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Simple F Test Reveals Gene-Gene Interactions in Case-Control Studies

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...

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Detalles Bibliográficos
Autores principales: Chen, Guanjie, Yuan, Ao, Zhou, Jie, Bentley, Amy R., Adeyemo, Adebowale, Rotimi, Charles N.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Libertas Academica 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3399554/
https://www.ncbi.nlm.nih.gov/pubmed/22837643
http://dx.doi.org/10.4137/BBI.S9867
Descripción
Sumario: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.