An Algorithm for Gene-Gene Interaction via Deviance of Independence
Date Issued
2012
Date
2012
Author(s)
Wang, Pin-Cian
Abstract
Genome-wide association studies (GWAS) are commonly used study designs in genetic epidemiology to identify the genetic factors associated with diseases. Most of GWAS adopted single-locus strategy to analyze the association between individual single nucleotide polymorphism (SNP) and diseases. However, complex diseases may cause by one single gene but the gene-gene or gene- environment interactions. Exhaustive search methods, such as multifactor dimensionality reduction (MDR), are popular for detecting gene-gene interactions. Such kinds of methods require enormous computations and therefore are only feasible for small number of SNPs. As a result, this study aims to construct a filtering criterion for a candidate SNP set from large number of SNPs based on the independency of SNPs, called the deviance of independent (DOI). We apply DOI in GWAS data to filter those SNPs without marginal effect individually but have better ability to discriminate between cases and controls when they pool together. We use simulation and real data to examine DOI performance. The simulation results show that SNPs with interactions are along with higher DOI values. In addition, the 2-way and 3-way gene-gene interactions in a real data are examined as well. And the results demonstrate that possible interactions can be identified after using DOI value as filter criteria. In sum, DOI algorithm is a powerful tool to filter a candidate gene set for further interaction analysis.
Subjects
Single nucleotide polymorphism
genome-wide association study
gene-gene interaction
deviance of independence
Type
thesis
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