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

TitleA statistical framework for cross-tissue transcriptome-wide association analysis.
Publication TypeJournal Article
Year of Publication2019
AuthorsHu, Y, Li, M, Lu, Q, Weng, H, Wang, J, Zekavat, SM, Yu, Z, Li, B, Gu, J, Muchnik, S, Shi, Y, Kunkle, BW, Mukherjee, S, Natarajan, P, Naj, A, Kuzma, A, Zhao, Y, Crane, PK, Lu, H, Zhao, H
Corporate AuthorsAlzheimer’s Disease Genetics Consortium,
JournalNat Genet
Volume51
Issue3
Pagination568-576
Date Published2019 03
ISSN1546-1718
KeywordsGene Expression, Gene Expression Profiling, Genome-Wide Association Study, Genotype, Humans, Models, Genetic, Polymorphism, Single Nucleotide, Transcriptome
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.

DOI10.1038/s41588-019-0345-7
Alternate JournalNat Genet
PubMed ID30804563
PubMed Central IDPMC6788740
Grant ListR01 AG054060 / AG / NIA NIH HHS / United States
UL1 TR000427 / TR / NCATS NIH HHS / United States
U24 AG021886 / AG / NIA NIH HHS / United States
U01 AG032984 / AG / NIA NIH HHS / United States
K25 AG055620 / AG / NIA NIH HHS / United States
R01 AG033193 / AG / NIA NIH HHS / United States
T32 GM007205 / GM / NIGMS NIH HHS / United States
U01 AG006781 / AG / NIA NIH HHS / United States
R01 AG057508 / AG / NIA NIH HHS / United States
UL1 TR002373 / TR / NCATS NIH HHS / United States
/ WT_ / Wellcome Trust / United Kingdom
G0701075 / MRC_ / Medical Research Council / United Kingdom
G0901254 / MRC_ / Medical Research Council / United Kingdom
R01 GM059507 / GM / NIGMS NIH HHS / United States
MR/K01417X/1 / MRC_ / Medical Research Council / United Kingdom
N01AG12100 / AG / NIA NIH HHS / United States
U01 AG016976 / AG / NIA NIH HHS / United States
G-0907 / PUK_ / Parkinson's UK / United Kingdom
R01 HL105756 / HL / NHLBI NIH HHS / United States
MR/N026004/1 / MRC_ / Medical Research Council / United Kingdom
R01 HL142711 / HL / NHLBI NIH HHS / United States
P30 AG021342 / AG / NIA NIH HHS / United States
R01 AG042437 / AG / NIA NIH HHS / United States
U24 AG041689 / AG / NIA NIH HHS / United States