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F1000Research, F1000Research

DOI: 10.12688/f1000research.4867.1

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

This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Abstract

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