A General Framework for eQTL Mapping Using RNA-Seq Data

• Activity Number: 495
• Type: Topic Contributed
• Date/Time: Wednesday, August 1, 2012 : 10:30 AM to 12:20 PM
• Sponsor: Biometrics Section
• Author(s): Yijuan Hu*+ and Wei Sun and Jung-Ying Tzeng
• Companies: Emory University and The University of North Carolina at Chapel Hill and North Carolina State University
• Keywords: Maximum likelihood estimation ; Allele-specific expression ; cis-eQTL ; haplotype

Abstract

RNA sequencing (RNA-seq) is replacing microarrays for gene expression Quantitative Trait Locus (eQTL) studies. RNA-seq not only provides total read count (TReC) mapped to a gene as the gene expression but also provides information on the allele-specific expression (ASE). It has been shown that ASE can be used to distinguish cis- and trans-eQTL and combining TReC and ASE can improve the power of cis-eQTL mapping. However, ASE is not directly observed because the haplotypes connecting the target SNP and the gene body are unknown. The existing methods first infer the haplotypes by a phasing algorithm and then uses the inferred haplotypes as the truth in the downstream eQTL mapping. This approach may leads to biased estimation of the SNP effect and reduced power for eQTL mapping. We propose a maximum-likelihood method that integrates the prediction of haplotypes and estimation of effect parameters into a single framework and thus accounts for haplotype ambiguity. Our method yields consistent and efficient estimators of SNP effects. We conduct simulation studies and an analysis of the HapMap data to evaluate the performance of our method and to compare with the existing methods.