Proper Use of Allele-Specific Expression Improves Statistical Power for -eQTL Mapping with RNA-Seq Data.

Studies of expression quantitative trait loci (eQTLs) offer insight into the molecular mechanisms of loci that were found to be associated with complex diseases and the mechanisms can be classified into - and -acting regulation. At present, high-throughput RNA sequencing (RNA-seq) is rapidly replacing expression microarrays to assess gene expression abundance. Unlike microarrays that only measure the total expression of each gene, RNA-seq also provides information on allele-specific expression (ASE), which can be used to distinguish -eQTLs from -eQTLs and, more importantly, enhance -eQTL mapping. However, assessing the -effect of a candidate eQTL on a gene requires knowledge of the haplotypes connecting the candidate eQTL and the gene, which cannot be inferred with certainty. The existing two-stage approach that first phases the candidate eQTL against the gene and then treats the inferred phase as observed in the association analysis tends to attenuate the estimated -effect and reduce the power for detecting a -eQTL. In this article, we provide a maximum-likelihood framework for -eQTL mapping with RNA-seq data. Our approach integrates the inference of haplotypes and the association analysis into a single stage, and is thus unbiased and statistically powerful. We also develop a pipeline for performing a comprehensive scan of all local eQTLs for all genes in the genome by controlling for false discovery rate, and implement the methods in a computationally efficient software program. The advantages of the proposed methods over the existing ones are demonstrated through realistic simulation studies and an application to empirical breast cancer data from The Cancer Genome Atlas project.