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2018 | OriginalPaper | Chapter

1,000x Faster Than PLINK: Genome-Wide Epistasis Detection with Logistic Regression Using Combined FPGA and GPU Accelerators

Authors : Lars Wienbrandt, Jan Christian Kässens, Matthias Hübenthal, David Ellinghaus

Published in: Computational Science – ICCS 2018

Publisher: Springer International Publishing

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Abstract

Logistic regression as implemented in PLINK is a powerful and commonly used framework for assessing gene-gene (GxG) interactions. However, fitting regression models for each pair of markers in a genome-wide dataset is a computationally intensive task. Performing billions of tests with PLINK takes days if not weeks, for which reason pre-filtering techniques and fast epistasis screenings are applied to reduce the computational burden.

Here, we demonstrate that employing a combination of a Xilinx UltraScale KU115 FPGA with an Nvidia Tesla P100 GPU leads to runtimes of only minutes for logistic regression GxG tests on a genome-wide scale. In particular, a dataset of 53,000 samples genotyped at 130,000 SNPs was analyzed in 8 min, resulting in a speedup of more than 1,000 when compared to PLINK v1.9 using 32 threads on a server-grade computing platform. Furthermore, on-the-fly calculation of test statistics, p-values and LD-scores in double-precision make commonly used pre-filtering strategies obsolete.

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Title: 1,000x Faster Than PLINK: Genome-Wide Epistasis Detection with Logistic Regression Using Combined FPGA and GPU Accelerators
Authors: Lars Wienbrandt
Jan Christian Kässens
Matthias Hübenthal
David Ellinghaus
Publisher: Springer International Publishing
Book: Computational Science – ICCS 2018
Print ISBN: 978-3-319-93700-7

Electronic ISBN: 978-3-319-93701-4

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-319-93701-4_28

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