rMVP: A Memory-efficient, Visualization-enhanced, and Parallel-accelerated tool for Genome-Wide Association Study.

Abstract Along with the development of high-throughput sequencing technologies, both sample size and SNP number are increasing rapidly in Genome-Wide Association Studies (GWAS), and the associated computation is more challenging than ever. Here, we present a Memory-efficient, Visualization-enhanced, and Parallel-accelerated R package called “rMVP” to address the need for improved GWAS computation. rMVP can 1) effectively process large GWAS data, 2) rapidly evaluate population structure, 3) efficiently estimate variance components by EMMAX, FaST-LMM, and HE regression algorithms, 4) implement parallel-accelerated association tests of markers using GLM, MLM, and FarmCPU methods, 5) compute fast with a globally efficient design in the GWAS processes, and 6) generate various visualizations of GWAS related information. Accelerated by block matrix multiplication strategy and multiple threads, the association test methods embedded in rMVP are approximately 5–20 times faster than PLINK, GEMMA, and FarmCPU_pkg. rMVP is freely available at https://github.com/xiaolei-lab/rMVP .