High-Performance Mixed Models Based Genome-Wide Association Analysis with omicABEL software

Diego Fabregat-Traver, Sodbo Zh Sharapov, Caroline Hayward, Igor Rudan, Harry Campbell, Yurii Aulchenko, Paolo Bientinesi

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

To raise the power of genome-wide association studies (GWAS) and avoid false-positive results in structured populations, one can rely on mixed model based tests. When large samples are used, and when multiple traits are to be studied in the 'omics' context, this approach becomes computationally challenging. Here we consider the problem of mixed-model based GWAS for arbitrary number of traits, and demonstrate that for the analysis of single-trait and multiple-trait scenarios different computational algorithms are optimal. We implement these optimal algorithms in a high-performance computing framework that uses state-of-the-art linear algebra kernels, incorporates optimizations, and avoids redundant computations, increasing throughput while reducing memory usage and energy consumption. We show that, compared to existing libraries, our algorithms and software achieve considerable speed-ups. The OmicABEL software described in this manuscript is available under the GNU GPL v. 3 license as part of the GenABEL project for statistical genomics at http: //www.genabel.org/packages/OmicABEL.

Original languageEnglish
Pages (from-to)200
JournalF1000Research
Volume3
DOIs
Publication statusPublished - 20 Aug 2014

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