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

  • Author(s): Hu, Yiming
  • Li, Mo
  • Lu, Qiongshi
  • Weng, Haoyi
  • Wang, Jiawei
  • Zekavat, Seyedeh M
  • Yu, Zhaolong
  • Li, Boyang
  • Gu, Jianlei
  • Muchnik, Sydney
  • Shi, Yu
  • Kunkle, Brian W
  • Mukherjee, Shubhabrata
  • Natarajan, Pradeep
  • Naj, Adam
  • Kuzma, Amanda
  • Zhao, Yi
  • Crane, Paul K
  • Alzheimer’s Disease Genetics Consortium,
  • Lu, Hui
  • Zhao, Hongyu
  • et al.
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.

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