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June 2021 A covariance-enhanced approach to multitissue joint eQTL mapping with application to transcriptome-wide association studies
Aaron J. Molstad, Wei Sun, Li Hsu
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Ann. Appl. Stat. 15(2): 998-1016 (June 2021). DOI: 10.1214/20-AOAS1432


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 multitissue joint eQTL mapping, our approach incorporates tissue-tissue expression correlation which allows us to more efficiently handle missing expression measurements and to 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 multitissue-based S-MultiXcan (PLoS Genet. 15 (2019) e1007889) 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

Funding Statement

The work in this article is funded, in part, by the National Institutes of Health (CA189532, CA195789, GM105785). The Genotype-Tissue Expression (GTEx) Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health and by NCI, NHGRI, NHLBI, NIDA, NIMH and NINDS.


The data used for the analyses described in this manuscript were obtained from the GTEx Portal (, v7) on 04/25/2018.


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Aaron J. Molstad. Wei Sun. Li Hsu. "A covariance-enhanced approach to multitissue joint eQTL mapping with application to transcriptome-wide association studies." Ann. Appl. Stat. 15 (2) 998 - 1016, June 2021.


Received: 1 January 2020; Revised: 1 December 2020; Published: June 2021
First available in Project Euclid: 12 July 2021

Digital Object Identifier: 10.1214/20-AOAS1432

Keywords: expression quantitative trait loci , GTEx , multitissue integrative analysis , multivariate regression , Transcriptome-wide association studies

Rights: Copyright © 2021 Institute of Mathematical Statistics


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Vol.15 • No. 2 • June 2021
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