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


Journal

Nature genetics
ISSN: 1546-1718
Titre abrégé: Nat Genet
Pays: United States
ID NLM: 9216904

Informations de publication

Date de publication:
03 2019
Historique:
received: 20 03 2018
accepted: 09 01 2019
pubmed: 26 2 2019
medline: 25 4 2019
entrez: 27 2 2019
Statut: ppublish

Résumé

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.

Identifiants

pubmed: 30804563
doi: 10.1038/s41588-019-0345-7
pii: 10.1038/s41588-019-0345-7
pmc: PMC6788740
mid: NIHMS1052791
doi:

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

568-576

Subventions

Organisme : NIA NIH HHS
ID : R01 AG054060
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR000427
Pays : United States
Organisme : NIA NIH HHS
ID : U24 AG021886
Pays : United States
Organisme : NIA NIH HHS
ID : U01 AG032984
Pays : United States
Organisme : NIA NIH HHS
ID : K25 AG055620
Pays : United States
Organisme : NIA NIH HHS
ID : R01 AG033193
Pays : United States
Organisme : NIGMS NIH HHS
ID : T32 GM007205
Pays : United States
Organisme : NIA NIH HHS
ID : U01 AG006781
Pays : United States
Organisme : NIA NIH HHS
ID : R01 AG057508
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR002373
Pays : United States
Organisme : Wellcome Trust
Pays : United Kingdom
Organisme : Medical Research Council
ID : G0701075
Pays : United Kingdom
Organisme : Medical Research Council
ID : G0901254
Pays : United Kingdom
Organisme : NIGMS NIH HHS
ID : R01 GM059507
Pays : United States
Organisme : Medical Research Council
ID : MR/K01417X/1
Pays : United Kingdom
Organisme : NIA NIH HHS
ID : N01AG12100
Pays : United States
Organisme : NIA NIH HHS
ID : U01 AG016976
Pays : United States
Organisme : Parkinson's UK
ID : G-0907
Pays : United Kingdom
Organisme : NHLBI NIH HHS
ID : R01 HL105756
Pays : United States
Organisme : Medical Research Council
ID : MR/N026004/1
Pays : United Kingdom
Organisme : NHLBI NIH HHS
ID : R01 HL142711
Pays : United States
Organisme : NIA NIH HHS
ID : P30 AG021342
Pays : United States
Organisme : NIA NIH HHS
ID : R01 AG042437
Pays : United States
Organisme : NIA NIH HHS
ID : U24 AG041689
Pays : United States

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Auteurs

Yiming Hu (Y)

Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA.

Mo Li (M)

Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA.

Qiongshi Lu (Q)

Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA.

Haoyi Weng (H)

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 (J)

Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA.

Seyedeh M Zekavat (SM)

Yale School of Medicine, New Haven, CT, USA.
Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA.
Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.

Zhaolong Yu (Z)

Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA.

Boyang Li (B)

Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA.

Jianlei Gu (J)

SJTU-Yale Joint Center for Biostatistics, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiaotong University, Shanghai, China.

Sydney Muchnik (S)

Department of Genetics, Yale School of Medicine, New Haven, CT, USA.

Yu Shi (Y)

Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA.

Brian W Kunkle (BW)

John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA.

Shubhabrata Mukherjee (S)

Department of Medicine, University of Washington, Seattle, WA, USA.

Pradeep Natarajan (P)

Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA.
Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Department of Medicine, Harvard Medical School, Boston, MA, USA.
Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.

Adam Naj (A)

Center for Clinical Epidemiology and Biostatistic, and the Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.

Amanda Kuzma (A)

Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.

Yi Zhao (Y)

Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.

Paul K Crane (PK)

Department of Medicine, University of Washington, Seattle, WA, USA.

Hui Lu (H)

SJTU-Yale Joint Center for Biostatistics, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiaotong University, Shanghai, China.

Hongyu Zhao (H)

Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA. hongyu.zhao@yale.edu.
Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA. hongyu.zhao@yale.edu.
SJTU-Yale Joint Center for Biostatistics, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiaotong University, Shanghai, China. hongyu.zhao@yale.edu.
Department of Genetics, Yale School of Medicine, New Haven, CT, USA. hongyu.zhao@yale.edu.

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