eQTL Mapping Study via Regularized Sparse Canonical Correlation Analysis

While genome-wide association studies (GWAS) have focused on discovering genetic loci mapped to a disease, expression quantitative trait loci (eQTL) studies combine microarray data and provide a powerful approach. Microarrays allow one to measure thousands of gene expressions simultaneously and the advances in eQTL studies enable one to capture the insight of the genetic architecture of gene expression. A number of multivariate methods have been recently proposed to identify genetic loci which are linked to gene expression taking into account joint effects and relationships between the units rather than the single locus alone independently. However, the previous research has limitations, such as the lack of supporting the cis/tran-eQTL model into being accepted as a general genetics model. We propose a novel regularized eQTL association mapping detection (Reg-AMADE) method. We have focused on the following three problems. First, we need to take into account co-expressed genes without using clustering or partitioning techniques, as well as detecting linkage disequilibrium and the joint effect of multiple genetic markers. Secondly, we need to build a regularized model to support the cis- and trans-eQTL model observed in most association studies. Lastly, we need to discover the significant genes underlying within diseases rather than a common component. We also propose a new simulation experiment method that implements practical situations so that the results can be evaluated in the true sense instead of the assessment with random samples generated from multivariate normal distributions that most research has mainly used. The power to detect both the joint effect and grouping effect of SNPs and gene expressions is assessed in the simulation study.