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


Wienbrandt L, Kässens JC, Hübenthal M, Ellinghaus D

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Journal (long):

Springer, Cham

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PubMed: -

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.

doi: 10.1007/978-3-319-93701-4_28

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