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

Publication ,  Journal Article
Hu, 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 ...
Published in: Nat Genet
March 2019

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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Published In

Nat Genet

DOI

EISSN

1546-1718

Publication Date

March 2019

Volume

51

Issue

3

Start / End Page

568 / 576

Location

United States

Related Subject Headings

  • Transcriptome
  • Polymorphism, Single Nucleotide
  • Models, Genetic
  • Humans
  • Genotype
  • Genome-Wide Association Study
  • Gene Expression Profiling
  • Gene Expression
  • Developmental Biology
  • 3105 Genetics
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Hu, Y., Li, M., Lu, Q., Weng, H., Wang, J., Zekavat, S. M., … Zhao, H. (2019). A statistical framework for cross-tissue transcriptome-wide association analysis. Nat Genet, 51(3), 568–576. https://doi.org/10.1038/s41588-019-0345-7
Hu, Yiming, Mo Li, Qiongshi Lu, Haoyi Weng, Jiawei Wang, Seyedeh M. Zekavat, Zhaolong Yu, et al. “A statistical framework for cross-tissue transcriptome-wide association analysis.Nat Genet 51, no. 3 (March 2019): 568–76. https://doi.org/10.1038/s41588-019-0345-7.
Hu Y, Li M, Lu Q, Weng H, Wang J, Zekavat SM, et al. A statistical framework for cross-tissue transcriptome-wide association analysis. Nat Genet. 2019 Mar;51(3):568–76.
Hu, Yiming, et al. “A statistical framework for cross-tissue transcriptome-wide association analysis.Nat Genet, vol. 51, no. 3, Mar. 2019, pp. 568–76. Pubmed, doi:10.1038/s41588-019-0345-7.
Hu 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, Alzheimer’s Disease Genetics Consortium, Lu H, Zhao H. A statistical framework for cross-tissue transcriptome-wide association analysis. Nat Genet. 2019 Mar;51(3):568–576.

Published In

Nat Genet

DOI

EISSN

1546-1718

Publication Date

March 2019

Volume

51

Issue

3

Start / End Page

568 / 576

Location

United States

Related Subject Headings

  • Transcriptome
  • Polymorphism, Single Nucleotide
  • Models, Genetic
  • Humans
  • Genotype
  • Genome-Wide Association Study
  • Gene Expression Profiling
  • Gene Expression
  • Developmental Biology
  • 3105 Genetics