Expression Quantitative Trait Locus (eQTL) Mapping

With the recent advances in genomewide expression microarray technology, combining the power of gene expression profiling and genetics is a natural step forward. Jansen and Nap⁵ first formally proposed a new research area termed “genetical genomics”, which describes the combined study of expression variations and DNA variations in segregating populations. The gene expression levels (i.e. mRNA transcript abundance) are treated as quantitative traits potentially affected by multiple genes and other factors. Traditional methods for detecting quantitative trait loci (QTLs) could be utilized to detect chromosomal regions affecting expression levels; these regions are referred to as expression quantitative trait loci (eQTLs). Recent studies have demonstrated the utility of this approach in unraveling many features of the genetic basis of variation in gene expression. Despite its great potential, there are many limitations to the current eQTL methods that demand statistical and computational novelties. Some of the issues are inherited from the traditional multiple-trait methods in QTL analysis and microarray technology. More importantly, the unique challenge is the joint consideration of tens of thousands of correlated phenotypes (i.e. transcription levels) with hundreds or thousands of genotypes. In this context, the issue of multiple testing needs to be better addressed not only to control the overall rate of false discoveries, but also to fully take advantage of the correlated expression patterns. In this chapter, we will summarize statistical methods that have been applied to eQTL studies, review the knowledge and patterns emerging from these studies, and discuss future research directions.