TY - JOUR T1 - PrediXcan: Trait Mapping Using Human Transcriptome Regulation JF - bioRxiv DO - 10.1101/020164 SP - 020164 AU - Eric R. Gamazon AU - Heather E. Wheeler AU - Kaanan P. Shah AU - Sahar V. Mozaffari AU - Keston Aquino-Michaels AU - Robert J. Carroll AU - Anne E. Eyler AU - Joshua C. Denny AU - GTEx Consortium AU - Dan L. Nicolae AU - Nancy J. Cox AU - Hae Kyung Im Y1 - 2015/01/01 UR - http://biorxiv.org/content/early/2015/06/17/020164.abstract N2 - 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 the “imputed” gene expression with the phenotype under investigation to identify genes involved in the etiology of the phenotype. The genetically regulated gene expression is estimated using whole-genome tissue-dependent prediction models trained with reference transcriptome datasets. PrediXcan enjoys the benefits of gene-based approaches such as reduced multiple testing burden, more comprehensive annotation of gene function compared to that derived from single variants, and a principled approach to the design of follow-up experiments while also integrating knowledge of regulatory function. Since no actual expression data are used in the analysis of GWAS data - only in silico expression - reverse causality problems are largely avoided. PrediXcan harnesses reference transcriptome data for disease mapping studies. Our results demonstrate that PrediXcan can detect known and novel genes associated with disease traits and provide insights into the mechanism of these associations. ER -