eQTL mapping using allele-specific count data is computationally feasible, powerful, and provides individual-specific estimates of genetic effects Public Deposited

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Creator
  • Zhabotynsky, Vasyl
    • Affiliation: Gillings School of Global Public Health, Department of Biostatistics
  • Huang, Licai
    • Other Affiliation: The University of Texas MD Anderson Cancer Center
  • Little, Paul
    • Other Affiliation: Fred Hutchinson Cancer Research Center
  • Hu, Yi-Juan
    • Other Affiliation: Emory University
  • de Villena, Fernando Pardo-Manuel
    • Affiliation: School of Medicine, Department of Genetics
  • Zou, Fei
    • Affiliation: Gillings School of Global Public Health, Department of Biostatistics
  • Sun, Wei
    • Affiliation: Gillings School of Global Public Health, Department of Biostatistics
Abstract
  • 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.
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  • Article
Rights statement
  • In Copyright
License
  • Attribution 4.0 International
Journal title
  • PLOS Genetics
Journal volume
  • 18
Journal issue
  • 3
Page start
  • e1010076
Language
  • English
Version
  • Postprint
ISSN
  • 1553-7404
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