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

By Yiming Hu, Mo Li, Q. Lu, Haoyi Weng, Jiawei Wang, S. Zekavat, Zhaolong Yu, Boyang Li, Jianlei Gu, S. Muchnik and 10 more. Nature Genetics, 2018

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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. UTMOST (unified test for molecular signatures) is a method for cross-tissue gene expression imputation for transcriptome-wide association analyses. Cross-tissue TWAS using UTMOST identifies new candidate genes for late-onset Alzheimer’s disease.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/37ec24f173e59f726020c78fb9a2f0afd5a186de

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