Open AccesseQTL mapping using allele-specific count data is computationally feasible, powerful, and provides individual-specific estimates of genetic effects
Author(s) -
Vasyl Zhabotynsky,
Licai Huang,
Paul Little,
YiJuan Hu,
Fernando Pardo-Manuel de Villena,
Fei Zou,
Wei Sun
Publication year - 2022
Publication title -
plos genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.587
H-Index - 233
eISSN - 1553-7404
pISSN - 1553-7390
DOI - 10.1371/journal.pgen.1010076
Subject(s) - expression quantitative trait loci , biology , covariate , statistical power , computational biology , quantitative trait locus , genetics , computer science , genotype , gene , statistics , machine learning , mathematics , single nucleotide polymorphism
Using information from allele-specific gene expression (ASE) can improve the power to map gene expression quantitative trait loci (eQTLs). However, such practice has been limited, partly due to computational challenges and lack of clarification on the size of power gain or new findings besides improved power. We have developed geoP, a computationally efficient method to estimate permutation p-values, which makes it computationally feasible to perform eQTL mapping with ASE counts for large cohorts. We have applied geoP to map eQTLs in 28 human tissues using the data from the Genotype-Tissue Expression (GTEx) project. We demonstrate that using ASE data not only substantially improve the power to detect eQTLs, but also allow us to quantify individual-specific genetic effects, which can be used to study the variation of eQTL effect sizes with respect to other covariates. We also compared two popular methods for eQTL mapping with ASE: TReCASE and RASQUAL. TReCASE is ten times or more faster than RASQUAL and it provides more robust type I error control.