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


Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Hu, Y; Li, M; Lu, Q; Weng, H; Wang, J; Zekavat, SM; Yu, Z; Li, B; Gu, J; Muchnik, S; Shi, Y; Kunkle, BW; Mukherjee, S; Natarajan, P; Naj, A; Kuzma, A; Zhao, Y; Crane, PK; Alzheimer’s Disease Genetics Consortium,, ; Lu, H; Zhao, H

Published Date

  • March 2019

Published In

Volume / Issue

  • 51 / 3

Start / End Page

  • 568 - 576

PubMed ID

  • 30804563

Pubmed Central ID

  • 30804563

Electronic International Standard Serial Number (EISSN)

  • 1546-1718

Digital Object Identifier (DOI)

  • 10.1038/s41588-019-0345-7


  • eng

Conference Location

  • United States