Title | A statistical framework for cross-tissue transcriptome-wide association analysis. |
Publication Type | Journal Article |
Year of Publication | 2019 |
Authors | 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, Lu H, Zhao H |
Corporate Authors | Alzheimer’s Disease Genetics Consortium, |
Journal | Nat Genet |
Volume | 51 |
Issue | 3 |
Pagination | 568-576 |
Date Published | 2019 03 |
ISSN | 1546-1718 |
Keywords | Gene 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. |
DOI | 10.1038/s41588-019-0345-7 |
Pubmed Link | https://www.ncbi.nlm.nih.gov/pubmed/30804563?dopt=Abstract |
page_expo | External |
Alternate Journal | Nat. Genet. |
PubMed ID | 30804563 |
PubMed Central ID | PMC6788740 |
Grant List | N01AG12100 / 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 U01 AG016976 / AG / NIA NIH HHS / United States R01 HL105756 / 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 R01 AG033193 / AG / NIA NIH HHS / United States U01 AG006781 / AG / NIA NIH HHS / United States R01 AG057508 / AG / NIA NIH HHS / United States / / Wellcome Trust / United Kingdom R01 GM059507 / GM / NIGMS NIH HHS / United States |
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