Accelerated LD-based selective sweep detection using GPUs and FPGAs

Abstract

Selective sweep detection carries theoretical significance and has several practical implications, from explaining the adaptive evolution of a species in an environment to understanding the emergence of viruses from animals, such as SARS-CoV-2, and their transmission from human to human. The plethora of available genomic data for population genetic analyses, however, poses various computational challenges to existing methods and tools, leading to prohibitively long analysis times. In this work, we accelerate LD (Linkage Disequilibrium) - based selective sweep detection using GPUs and FPGAs on personal computers and datacenter infrastructures. LD has been previously efficiently accelerated with both GPUs and FPGAs. However, LD alone cannot serve as an indicator of selective sweeps. Here, we complement previous research with dedicated accelerators for the ω statistic, which is a direct indicator of a selective sweep. We evaluate performance of our accelerator solutions for computing the $w$ statistic and for a complete sweep detection method, as implemented by the open-source software OmegaPlus. In comparison with a single CPU core, the FPGA accelerator delivers up to 57.1× and 61.7× faster computation of the ω statistic and the complete sweep detection analysis, respectively. The respective attained speedups by the GPU-accelerated version of OmegaPlus are 2.9× and 12.9×. The GPU-accelerated implementation is available for download here: https://github.com/MrKzn/omegaplus.git.