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Tensor decomposition for multiple-tissue gene expression experiments

Lookup NU author(s): Dr Ana ViñuelaORCiD


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© 2016 Nature America, Inc. All rights reserved. Genome-wide association studies of gene expression traits and other cellular phenotypes have successfully identified links between genetic variation and biological processes. The majority of discoveries have uncovered cis-expression quantitative trait locus (eQTL) effects via mass univariate testing of SNPs against gene expression in single tissues. Here we present a Bayesian method for multiple-tissue experiments focusing on uncovering gene networks linked to genetic variation. Our method decomposes the 3D array (or tensor) of gene expression measurements into a set of latent components. We identify sparse gene networks that can then be tested for association against genetic variation across the genome. We apply our method to a data set of 845 individuals from the TwinsUK cohort with gene expression measured via RNA-seq analysis in adipose, lymphoblastoid cell lines (LCLs) and skin. We uncover several gene networks with a genetic basis and clear biological and statistical significance. Extensions of this approach will allow integration of different omics, environmental and phenotypic data sets.

Publication metadata

Author(s): Hore V, Viñuela A, Buil A, Knight J, McCarthy MI, Small K, Marchini J

Publication type: Article

Publication status: Published

Journal: Nature Genetics

Year: 2016

Volume: 48

Issue: 9

Pages: 1094-1100

Print publication date: 01/09/2016

Online publication date: 01/08/2016

Acceptance date: 22/06/2016

ISSN (print): 1061-4036

ISSN (electronic): 1546-1718

Publisher: Nature Publishing Group


DOI: 10.1038/ng.3624

PubMed id: 27479908


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