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

Citation
Liang, Y., et al. “A Scalable Unified Framework Of Total And Allele-Specific Counts For Cis-Qtl, Fine-Mapping, And Prediction.”. Nature Communications, p. 1424.
Center University of Chicago
Author Yanyu Liang, François Aguet, Alvaro N Barbeira, Kristin Ardlie, Hae Kyung Im
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 coverage. To showcase the potential of mixQTL, we apply it to 49 GTEx tissues and find 20% additional eQTLs (FDR < 0.05, per tissue) that are significantly more enriched among trait associated variants and candidate cis-regulatory elements comparing to the standard approach.

Year of Publication
2021
Journal
Nature communications
Volume
12
Issue
1
Number of Pages
1424
Date Published
12/2021
ISSN Number
2041-1723
DOI
10.1038/s41467-021-21592-8
Alternate Journal
Nat Commun
PMID
33658504
PMCID
PMC7930098
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