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

Hu, Y; Li, M; Lu, Q; Weng, H; Wang, J; Zekavat, SM; Yu, Z; ... Yu, L; + view all (2019) A statistical framework for cross-tissue transcriptome-wide association analysis. Nature Genetics , 51 (3) pp. 568-576. 10.1038/s41588-019-0345-7. Green open access

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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.

Type: Article
Title: A statistical framework for cross-tissue transcriptome-wide association analysis
Open access status: An open access version is available from UCL Discovery
DOI: 10.1038/s41588-019-0345-7
Publisher version: https://doi.org/10.1038/s41588-019-0345-7
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Science & Technology, Life Sciences & Biomedicine, Genetics & Heredity, GENE-EXPRESSION, IDENTIFIES VARIANTS, INTEGRATIVE ANALYSIS, SUSCEPTIBILITY LOCI, COMMON VARIANTS, RISK PREDICTION, ALZHEIMERS, GWAS, METAANALYSIS, DISEASE
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Neurodegenerative Diseases
URI: https://discovery.ucl.ac.uk/id/eprint/10084678
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