Multifactor dimendionality reduction analysis for gene-gene interaction of multiple binary traits

Recent advances in genotyping technology have facilitated the use of genome-wide association studies (GWAS) to successfully identify genetic variants that are associated with common complex traits. Following the successes in identification of single variants, joint identification including gene-gene interaction has been studied vigorously and produced many novel results. However, most genome-wide association studies have been conducted by focusing on one trait of interest for identifying genetic variants associated with common complex traits. Since many complex diseases having severe influences on the public health are pleiotropic, simple univariate analysis focusing on a single trait does not well detect full genetic architecture of complex diseases. For example, hyperlipidemia is diagnosed by four multiple traits: Total cholesterol (Tchl), High density lipoprotein (HDL) cholesterol, Low density lipoprotein (LDL), and cholesterol and Triglycerides (TG). Surprisingly, however, only few studies handle multiple traits simultaneously so far. Therefore, in order to improve power and reflect biological association more expansively, we investigate a multivariate approach which considers multiple traits simultaneously. Especially for the gene-gene interaction analysis for the multiple traits, we extend original multifactor dimensionality reduction (MDR) to handle multiple traits. We then demonstrate its superiority to univariate analysis through simulation studies. We confirm that the multivariate approach provides more stable and precise accuracy measures compared to univariate analysis. We applied the multivariate MDR approach to a GWA dataset of 8,842 Korean individuals and detected genetic variants associated with hypertension traits using systolic blood pressure (SBP) and Diastolic blood pressure (DBP).