Document Type
Article
Publication Date
9-2015
Publication Title
Nature Genetics
Volume
47
Issue
9
Pages
1091-1098
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.
Recommended Citation
Gamazon, Eric R.; Wheeler, Heather; Shah, Kaanan P.; Mozaffari, Sahar V.; Aquino-Michaels, Keston; Carroll, Robert J.; Eyler, Anne E.; Denny, Joshua C.; GTEx Consortium; Nicolae, Dan L.; Cox, Nancy J.; and Im, Hae Kyung. A Gene-Based Association Method for Mapping Traits Using Reference Transcriptome Data. Nature Genetics, 47, 9: 1091-1098, 2015. Retrieved from Loyola eCommons, Bioinformatics Faculty Publications, http://dx.doi.org/10.1038/ng.3367
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Copyright Statement
© 2015 The Authors.
Included in
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Comments
Author Posting. © The Authors, 2015. This is the author's version of the work. It is posted here by permission of Nature Publishing Group for personal use, not for redistribution. The definitive version was published in Nature Genetics, 47,1091–1098 (2015), http://dx.doi.org/10.1038/ng.3367.