A scalable unified framework of total and allele-specific counts for cis-QTL, fine-mapping, and prediction.

ABSTRACT: Genetic studies of the transcriptome help bridge the gap between genetic variation and phenotypes. To maximize the potential of such studies, efficient methods to identify expression quantitative trait loci (eQTLs) and perform fine-mapping and genetic prediction of gene expression traits are needed. Current methods that leverage both total read counts and allele-specific expression to identify eQTLs are generally computationally intractable for large transcriptomic studies. Here, we describe a unified framework that addresses these needs and is scalable to thousands of samples. Using simulations and data from GTEx, we demonstrate its calibration and performance. For example, mixQTL shows a power gain equivalent to a 29% increase in sample size for genes with sufficient allele-specific read

SUBMITTER: Liang Y 

PROVIDER: S-EPMC7930098 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature