Abstract
We present SNPInt-GPU, a software providing several methods for statistical epistasis testing. SNPInt-GPU supports GPU acceleration using the Nvidia CUDA framework, but can also be used without GPU hardware. The software implements logistic regression (as in PLINK epistasis testing), BOOST, log-linear regression, mutual information (MI), and information gain (IG) for pairwise testing as well as mutual information and information gain for third-order tests. Optionally, r2 scores for testing for linkage disequilibrium (LD) can be calculated on-the-fly. SNPInt-GPU is publicly available at GitHub. The software requires a Linux-based operating system and CUDA libraries. This chapter describes detailed installation and usage instructions as well as examples for basic preliminary quality control and analysis of results.
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Wienbrandt, L., Kässens, J.C., Ellinghaus, D. (2021). SNPInt-GPU: Tool for Epistasis Testing with Multiple Methods and GPU Acceleration. In: Wong, KC. (eds) Epistasis. Methods in Molecular Biology, vol 2212. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-0947-7_2
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DOI: https://doi.org/10.1007/978-1-0716-0947-7_2
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