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

By Yiming Hu, Mo Li, Qiongshi Lu, Haoyi Weng, Jiawei Wang, Seyedeh Maryam Zekavat, Zhaolong Yu, Boyang Li, Jianlei Gu, Sydney Muchnik, Yu Shi, Brian W. Kunkle, Shubhabrata Mukherjee, Pradeep Natarajan, Adam C Naj, Amanda Kuzma, Yi Zhao, Paul K Crane, Alzheimer's Disease Genetics Consortium, Hui Lu, Hongyu Zhao

Posted 21 Mar 2018
bioRxiv DOI: 10.1101/286013 (published DOI: 10.1038/s41588-019-0345-7)

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 predict (impute) gene expression levels from genotypes from samples with matched genotypes and expression levels in a specific tissue. However, it is challenging to develop robust and accurate imputation models with limited sample sizes for any single tissue. Here, we first introduce a multi-task learning approach to jointly impute gene expression in 44 human tissues. Compared with single-tissue methods, our approach achieved an average 39% improvement in imputation accuracy and generated effective imputation models for an average 120% (range 13%-339%) more genes in each tissue. We then describe a summary statistic-based testing framework that combines multiple single-tissue associations into a single powerful metric to quantify overall gene-trait association at the organism level. When our method, called UTMOST, was applied to analyze genome wide association results for 50 complex traits (N_total=4.5 million), we were able to identify considerably more genes in tissues enriched for trait heritability, and cross-tissue analysis significantly outperformed single-tissue strategies (p=1.7e-8). Finally, we performed a cross-tissue genome-wide association study for late-onset Alzheimer's disease (LOAD) and replicated our findings in two independent datasets (N_total=175,776). In total, we identified 69 significant genes, many of which are novel, leading to novel insights on LOAD etiologies.

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