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

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

doi:10.1038/s41588-019-0345-7

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

Journal article

Open access

Peer reviewed

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

Yiming Hu, Mo Li, Qiongshi Lu, Haoyi Weng, Jiawei Wang, Seyedeh M Zekavat, Zhaolong Yu, Boyang Li, Jianlei Gu, Sydney Muchnik, …

Nature genetics, Vol.51(3), pp.568-576

2019-03

DOI: https://doi.org/10.1038/s41588-019-0345-7

PMCID: PMC6788740

PMID: 30804563

Abstract

Gene Expression - genetics

Humans

Polymorphism, Single Nucleotide - genetics

Gene Expression Profiling - methods

Genotype

Models, Genetic

Transcriptome - genetics

Genome-Wide Association Study - methods

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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Collaboration types: Industry collaboration; Domestic collaboration; International collaboration
Citation topics: 1 Clinical & Life Sciences; 1.189 Genome Studies; 1.189.455 Genome-Wide Association Studies
Web Of Science research areas: Genetics & Heredity
ESI research areas: Molecular Biology & Genetics

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Title: A statistical framework for cross-tissue transcriptome-wide association analysis
Creators: Yiming Hu - Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
Mo Li - Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
Qiongshi Lu - Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA
Haoyi Weng - Division of Biostatistics, The Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Shatin, NT, Hong Kong
Jiawei Wang - Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
Seyedeh M Zekavat - Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
Zhaolong Yu - Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
Boyang Li - Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
Jianlei Gu - SJTU-Yale Joint Center for Biostatistics, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiaotong University, Shanghai, China
Sydney Muchnik - Department of Genetics, Yale School of Medicine, New Haven, CT, USA
Yu Shi - Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
Brian W Kunkle - John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
Shubhabrata Mukherjee - Department of Medicine, University of Washington, Seattle, WA, USA
Pradeep Natarajan - Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
Adam Naj - Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
Amanda Kuzma - Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
Yi Zhao - Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
Paul K Crane - Department of Medicine, University of Washington, Seattle, WA, USA
Hui Lu - SJTU-Yale Joint Center for Biostatistics, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiaotong University, Shanghai, China
Hongyu Zhao - Department of Genetics, Yale School of Medicine, New Haven, CT, USA. hongyu.zhao@yale.edu
Alzheimer’s Disease Genetics Consortium
Publication Details: Nature genetics, Vol.51(3), pp.568-576
Grant note: G-0907 / Parkinson's UK Wellcome Trust N01AG12100 / NIA NIH HHS K25 AG055620 / NIA NIH HHS G0901254 / Medical Research Council G0701075 / Medical Research Council U01 AG016976 / NIA NIH HHS R01 GM059507 / NIGMS NIH HHS U24 AG021886 / NIA NIH HHS R01 HL105756 / NHLBI NIH HHS R01 AG033193 / NIA NIH HHS R01 AG042437 / NIA NIH HHS R01 AG054060 / NIA NIH HHS R01 AG057508 / NIA NIH HHS R01 HL142711 / NHLBI NIH HHS UL1 TR002373 / NCATS NIH HHS U01 AG032984 / NIA NIH HHS MR/N026004/1 / Medical Research Council U01 AG006781 / NIA NIH HHS P30 AG021342 / NIA NIH HHS UL1 TR000427 / NCATS NIH HHS T32 GM007205 / NIGMS NIH HHS U24 AG041689 / NIA NIH HHS MR/K01417X/1 / Medical Research Council
Academic Unit: Miller School of Medicine; John P. Hussman Institute for Human Genomics; UMMG Dept of Human Genetics (Dr. John T. Macdonald Foundation)
Language: English
Resource Type: Journal article
PMID: 30804563
PMCID: PMC6788740
Record Identifier: 991031599188502976