Open AccessA covariance-enhanced approach to multitissue joint eQTL mapping with application to transcriptome-wide association studies
Author(s) -
Aaron J. Molstad,
Wei Sun,
Li Hsu
Publication year - 2021
Publication title -
annals of applied statistics/the annals of applied statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.674
H-Index - 75
eISSN - 1941-7330
pISSN - 1932-6157
DOI - 10.1214/20-aoas1432
Subject(s) - expression quantitative trait loci , computer science , computational biology , covariance , transcriptome , artificial intelligence , data mining , biology , gene , gene expression , genetics , statistics , mathematics , genotype , single nucleotide polymorphism
Transcriptome-wide association studies based on genetically predicted gene expression have the potential to identify novel regions associated with various complex traits. It has been shown that incorporating expression quantitative trait loci (eQTLs) corresponding to multiple tissue types can improve power for association studies involving complex etiology. In this article, we propose a new multivariate response linear regression model and method for predicting gene expression in multiple tissues simultaneously. Unlike existing methods for multi-tissue joint eQTL mapping, our approach incorporates tissue-tissue expression correlation, which allows us to more efficiently handle missing expression measurements and more accurately predict gene expression using a weighted summation of eQTL genotypes. We show through simulation studies that our approach performs better than the existing methods in many scenarios. We use our method to estimate eQTL weights for 29 tissues collected by GTEx, and show that our approach significantly improves expression prediction accuracy compared to competitors. Using our eQTL weights, we perform a multi-tissue-based S-MultiXcan [2] transcriptome-wide association study and show that our method leads to more discoveries in novel regions and more discoveries overall than the existing methods. Estimated eQTL weights and code for implementing the method are available for download online at github.com/ajmolstad/MTeQTLResults.