rMVP: A Memory-efficient, Visualization-enhanced, and Parallel-accelerated Tool for Genome-wide Association Study
- Author:
Yin LILIN
1
,
2
;
Zhang HAOHAO
;
Tang ZHENSHUANG
;
Xu JINGYA
;
Yin DONG
;
Zhang ZHIWU
;
Yuan XIAOHUI
;
Zhu MENGJIN
;
Zhao SHUHONG
;
Li XINYUN
;
Liu XIAOLEI
Author Information
1. Key Laboratory of Agricultural Animal Genetics,Breeding and Reproduction,Ministry of Education&College of Animal Science and Technology,Huazhong Agricultural University,Wuhan 430070,China
2. Key Laboratory of Swine Genetics and Breeding,Ministry of Agriculture,Huazhong Agricultural University,Wuhan 430070,China
- Keywords:
Memory-efficient;
Visualization-enhanced;
Parallel-accelerated;
rMVP;
GWAS
- From:
Genomics, Proteomics & Bioinformatics
2021;19(4):619-628
- CountryChina
- Language:Chinese
-
Abstract:
Along with the develoipment 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 Efficient Mixed-Model Association eX-pedited (EMMAX), Factored Spectrally Transformed Linear Mixed Models (FaST-LMM), and Haseman-Elston (HE) regression algorithms, 4) implement parallel-accelerated association tests of markers using general linear model (GLM), mixed linear model (MLM), and fixed and random model circulating probability unification (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 significantly faster than PLINK, GEMMA, and FarmCPU_pkg. rMVP is freely available at https://github.com/xiaolei-lab/rMVP.
