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Lilin Yin, Haohao Zhang, Zhenshuang Tang, Jingya Xu, Dong Yin, Zhiwu Zhang, Xiaohui Yuan, Mengjin Zhu, Shuhong Zhao, Xinyun Li, Xiaolei Liu
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 Efficient Mixed-Model Association eXpedited (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.
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Yin, L., Zhang, H., Tang, Z., Xu, J., Yin, D., Zhang, Z., Yuan, X., Zhu, M., Zhao, S., Li, X., & Liu, X. (2021). rMVP: A Memory-Efficient, Visualization-Enhanced, and Parallel-Accelerated Tool for Genome-Wide Association Study. Genomics Proteomics & Bioinformatics, 19(4), 619–628. https://doi.org/10.1016/j.gpb.2020.10.007
Yin, Lilin, et al. "rMVP: A Memory-Efficient, Visualization-Enhanced, and Parallel-Accelerated Tool for Genome-Wide Association Study." Genomics Proteomics & Bioinformatics, vol. 19, no. 4, 2021, pp. 619–628, https://doi.org/10.1016/j.gpb.2020.10.007.
Yin, Lilin, Haohao Zhang, Zhenshuang Tang, Jingya Xu, Dong Yin, Zhiwu Zhang, Xiaohui Yuan, et al. "rMVP: A Memory-Efficient, Visualization-Enhanced, and Parallel-Accelerated Tool for Genome-Wide Association Study." Genomics Proteomics & Bioinformatics 19, no. 4 (2021): 619–628. https://doi.org/10.1016/j.gpb.2020.10.007.
Yin L., Zhang H., Tang Z. [и др.] rMVP: A Memory-Efficient, Visualization-Enhanced, and Parallel-Accelerated Tool for Genome-Wide Association Study // Genomics Proteomics & Bioinformatics. — 2021. — Vol. 19, № 4. — P. 619–628. — URL: https://doi.org/10.1016/j.gpb.2020.10.007
@article{yin2021rmvp,
title = {{rMVP: A Memory-Efficient, Visualization-Enhanced, and Parallel-Accelerated Tool for Genome-Wide Association Study}},
author = {Lilin Yin and Haohao Zhang and Zhenshuang Tang and Jingya Xu and Dong Yin and Zhiwu Zhang and Xiaohui Yuan and Mengjin Zhu and Shuhong Zhao and Xinyun Li and Xiaolei Liu},
journal = {Genomics Proteomics \& Bioinformatics},
year = {2021},
volume = {19},
number = {4},
pages = {619--628},
doi = {10.1016/j.gpb.2020.10.007},
url = {https://doi.org/10.1016/j.gpb.2020.10.007}
}TY - JOUR AU - Yin, Lilin AU - Zhang, Haohao AU - Tang, Zhenshuang AU - Xu, Jingya AU - Yin, Dong AU - Zhang, Zhiwu AU - Yuan, Xiaohui AU - Zhu, Mengjin AU - Zhao, Shuhong AU - Li, Xinyun AU - Liu, Xiaolei TI - rMVP: A Memory-Efficient, Visualization-Enhanced, and Parallel-Accelerated Tool for Genome-Wide Association Study JO - Genomics Proteomics & Bioinformatics VL - 19 IS - 4 SP - 619 EP - 628 PY - 2021 DA - 2021/03/01 DO - 10.1016/j.gpb.2020.10.007 UR - https://doi.org/10.1016/j.gpb.2020.10.007 ER -
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