As a promising tool for dissecting the genetic basis of common diseases, expression quantitative trait loci (eQTL) study has attracted increasing research interest. The traditional eQTL methods focus on testing the associations between individual single-nucleotide polymorphisms (SNPs) and gene expression traits. A major drawback of this approach is that it cannot model the joint effect of a set of SNPs on a set of genes, which may correspond to biological pathways. In this chapter, we study the problem of identifying group-wise associations in eQTL mapping. Based on the intuition of group-wise association, we examine how the integration of heterogeneous prior knowledge on the correlation structures between SNPs, and between genes can improve the robustness and the interpretability of eQTL mapping.
|Original language||English (US)|
|Title of host publication||Big Data Analytics in Genomics|
|Publisher||Springer International Publishing|
|Number of pages||64|
|State||Published - Jan 1 2016|
All Science Journal Classification (ASJC) codes
- Computer Science(all)