A gene-based association method for mapping traits using reference transcriptome data View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2015-09

AUTHORS

Eric R. Gamazon, Heather E. Wheeler, Kaanan P. Shah, Sahar V. Mozaffari, Keston Aquino-Michaels, Robert J. Carroll, Anne E. Eyler, Joshua C. Denny, , Dan L. Nicolae, Nancy J. Cox, Hae Kyung Im

ABSTRACT

Genome-wide association studies (GWAS) have identified thousands of variants robustly associated with complex traits. However, the biological mechanisms underlying these associations are, in general, not well understood. We propose a gene-based association method called PrediXcan that directly tests the molecular mechanisms through which genetic variation affects phenotype. The approach estimates the component of gene expression determined by an individual's genetic profile and correlates 'imputed' gene expression with the phenotype under investigation to identify genes involved in the etiology of the phenotype. Genetically regulated gene expression is estimated using whole-genome tissue-dependent prediction models trained with reference transcriptome data sets. PrediXcan enjoys the benefits of gene-based approaches such as reduced multiple-testing burden and a principled approach to the design of follow-up experiments. Our results demonstrate that PrediXcan can detect known and new genes associated with disease traits and provide insights into the mechanism of these associations. More... »

PAGES

1091-1098

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/ng.3367

    DOI

    http://dx.doi.org/10.1038/ng.3367

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1005364973

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/26258848


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