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Nat Genet. 2019 Mar;51(3):568-576. doi: 10.1038/s41588-019-0345-7. Epub 2019 Feb 25.

A statistical framework for cross-tissue transcriptome-wide association analysis.

Author information

1
Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA.
2
Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA.
3
Division of Biostatistics, The Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Shatin, NT, Hong Kong.
4
Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA.
5
Yale School of Medicine, New Haven, CT, USA.
6
Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA.
7
Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
8
SJTU-Yale Joint Center for Biostatistics, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiaotong University, Shanghai, China.
9
Department of Genetics, Yale School of Medicine, New Haven, CT, USA.
10
John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA.
11
Department of Medicine, University of Washington, Seattle, WA, USA.
12
Department of Medicine, Harvard Medical School, Boston, MA, USA.
13
Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
14
Center for Clinical Epidemiology and Biostatistic, and the Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
15
Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
16
Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA. hongyu.zhao@yale.edu.
17
Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA. hongyu.zhao@yale.edu.
18
SJTU-Yale Joint Center for Biostatistics, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiaotong University, Shanghai, China. hongyu.zhao@yale.edu.
19
Department of Genetics, Yale School of Medicine, New Haven, CT, USA. hongyu.zhao@yale.edu.

Abstract

Transcriptome-wide association analysis is a powerful approach to studying the genetic architecture of complex traits. A key component of this approach is to build a model to impute gene expression levels from genotypes by using samples with matched genotypes and gene expression data in a given tissue. However, it is challenging to develop robust and accurate imputation models with a limited sample size for any single tissue. Here, we first introduce a multi-task learning method to jointly impute gene expression in 44 human tissues. Compared with single-tissue methods, our approach achieved an average of 39% improvement in imputation accuracy and generated effective imputation models for an average of 120% more genes. We describe a summary-statistic-based testing framework that combines multiple single-tissue associations into a powerful metric to quantify the overall gene-trait association. We applied our method, called UTMOST (unified test for molecular signatures), to multiple genome-wide-association results and demonstrate its advantages over single-tissue strategies.

PMID:
30804563
DOI:
10.1038/s41588-019-0345-7
[Indexed for MEDLINE]
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