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

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Diego Fabregat‐Traver1, Sodbo Sharapov2, Caroline Hayward3, Igor Rudan4, Harry Campbell5, Yurii S. Aulchenko6, Paolo Bientinesi1
1Aachen Institute for Advanced Study in Computational Engineering Science, Aachen, 52062, Germany.
2Institute of Cytology and Genetics, Siberian Division of the Russian Academy of Sciences, Novosibirsk, 630090, Russian Federation ; Novosibirsk State University, Novosibirsk, 630090, Russian Federation.
3MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XU, UK
4Centre for Population Health Sciences, University of Edinburgh, Edinburgh, EH8 9AG, UK ; Split University, Split, 21000, Croatia.
5Centre for Population Health Sciences, University of Edinburgh, Edinburgh, EH8 9AG, UK
6Institute of Cytology and Genetics, Siberian Division of the Russian Academy of Sciences, Novosibirsk, 630090, Russian Federation ; Novosibirsk State University, Novosibirsk, 630090, Russian Federation ; Centre for Population Health Sciences, University of Edinburgh, Edinburgh, EH8 9AG, UK.

Tóm tắt

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 GNUGPL v. 3 license as part of the GenABEL project for statistical genomics at http: //www.genabel.org/packages/OmicABEL.

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