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

• Published in: Nature genetics Vol. 47; no. 9; pp. 1091 - 1098
• Main Authors: Gamazon, Eric R, Wheeler, Heather E, Shah, Kaanan P, Mozaffari, Sahar V, Aquino-Michaels, Keston, Carroll, Robert J, Eyler, Anne E, Denny, Joshua C, Nicolae, Dan L, Cox, Nancy J, Im, Hae Kyung
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.09.2015; Nature Publishing Group
• Subjects: 631/114/794; 631/208/199; 631/208/205/2138; 631/208/457; Agriculture; Analysis; Animal Genetics and Genomics; Biomedicine; Cancer Research; Chromosome Mapping; Consortia; Datasets; Disease; Gene expression; Gene Expression Profiling; Gene Function; Genetic diversity; Genetic Predisposition to Disease; Genome-wide association studies; Genome-Wide Association Study - methods; Genomes; Genomics; Human Genetics; Humans; Identification and classification; Methods; Multifactorial traits; Phenotype; Polymorphism, Single Nucleotide; Prediction models; Studies; technical-report; United States
• ISSN: 1061-4036; 1546-1718
• DOI: 10.1038/ng.3367

Hae Kyung Im and colleagues report a method for predicting gene expression perturbations from genotype data after training on reference transcriptome data sets. Association of predicted gene expression with disease traits identifies known and new candidate disease genes. 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.