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Author: Alvaro Barbeira
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Research area: bioinformatics
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MetaXcan: Summary Statistics Based Gene-Level Association Method Infers Accurate PrediXcan Results


Created on 23rd March 2016

Alvaro Barbeira; Kaanan P Shah; Jason M Torres; Heather E Wheeler; Eric S Torstenson; Todd Edwards; Tzintzuni Garcia; Graeme I Bell; Dan Nicolae; Nancy J Cox; Hae Kyung Im;


To gain biological insight into the discoveries made by GWAS and meta-analysis studies, effective integration of functional data generated by large-scale efforts such as the GTEx Project is needed. PrediXcan is a gene-level approach that addresses this need by estimating the genetically determined component of gene expression. These predicted expression traits can then be tested for association with phenotype in order to test for mediating role of gene expression levels. Furthermore, due to the polygenic nature of many complex traits, efforts to aggregate multiple GWAS studies and conduct meta-analyses have successfully increased our ability to identify variants of small effect sizes. To take advantage of the results generated by these efforts and to avoid the problems associated with accessing and handling individual-level data (e.g. consent limitations, large computational/storage costs) we have developed an extension of PrediXcan. The new method, MetaXcan, infers the results of PrediXcan using only summary statistics from large-scale GWAS or meta-analyses. Here we show that the concordance between PrediXcan and MetaXcan is excellent when the right reference population is used (R2 > 0.95) and robust to population mismatches (R2 > 0.85). We provide open source local and web-based software for easy implementation through (https://github.com/hakyimlab/MetaXcan)

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