The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
rMVP: A Memory-Efficient, Visualization-Enhanced, and Parallel-Accelerated Tool for Genome-Wide Association Study
Open accessAbstract
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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Cites
PLINK: a tool set for whole-genome association and population-based linkage analyses.
Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A. R., Bender, D., Maller, J., Sklar, P., de Bakker, P. I. W., Daly, M. J., et al. (2007) · The American Journal of Human Genetics · doi:10.1086/519795 · Cited by 34,089
Inference of Population Structure Using Multilocus Genotype Data
Pritchard, J. K., Stephens, M., & Donnelly, P. (2000) · Genetics · doi:10.1093/genetics/155.2.945 · Cited by 33,908
genalex 6: genetic analysis in Excel. Population genetic software for teaching and research
PEAKALL, R., & SMOUSE, P. E. (2006) · Molecular Ecology Notes · doi:10.1111/j.1471-8286.2005.01155.x · Cited by 16,589
Principal components analysis corrects for stratification in genome-wide association studies
Price, A. L., Patterson, N. J., Plenge, R. M., Weinblatt, M. E., Shadick, N. A., & Reich, D. (2006) · Nature Genetics · doi:10.1038/ng1847 · Cited by 10,248
GCTA: a tool for genome-wide complex trait analysis
Yang, J., Lee, S. H., Goddard, M. E., & Visscher, P. M. (2011) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2010.11.011 · Cited by 8,279
TASSEL: software for association mapping of complex traits in diverse samples
Bradbury, P. J., Zhang, Z., Kroon, D. E., Casstevens, T. M., Ramdoss, Y., & Buckler, E. S. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm308 · Cited by 7,759
Self-citationEfficient Methods to Compute Genomic Predictions
VanRaden, P. M. (2008) · Journal of Dairy Science · doi:10.3168/jds.2007-0980 · Cited by 6,050
Yu, J., Pressoir, G., Briggs, W. H., Vroh Bi, I., Yamasaki, M., Doebley, J. F., McMullen, M. D., Gaut, B. S., Nielsen, D. M., Holland, J. B., et al. (2006) · Nature Genetics · doi:10.1038/ng1702 · Cited by 4,133
10 Years of GWAS Discovery: Biology, Function, and Translation
Visscher, P. M., Wray, N. R., Zhang, Q., Sklar, P., McCarthy, M. I., Brown, M. A., & Yang, J. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.06.005 · Cited by 3,719
Genome-wide efficient mixed-model analysis for association studies
Zhou, X., & Stephens, M. (2012) · Nature Genetics · doi:10.1038/ng.2310 · Cited by 3,627 · cited 1×
“Various software packages have also been developed for the implementation of these algorithms, including TASSEL [12], GAPIT [13,14], GenABEL [15], Efficient Mixed-Model Association eXpedited (EMMAX) [16], GEMMA [17], and GCTA [18].”
Cited by
sc2GWAS: a comprehensive platform linking single cell and GWAS traits of human
Yin, M., Feng, C., Yu, Z., Zhang, Y., Li, Y., Wang, X., Song, C., Guo, M., & Li, C. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkae1008 · Cited by 51
Gorman, B. R., Francis, M., Nealon, C. L., Halladay, C. W., Duro, N., Markianos, K., Genovese, G., Hysi, P. G., Choquet, H., Afshari, N. A., et al. (2024) · Communications Biology · doi:10.1038/s42003-024-06046-3 · Cited by 23
Wang, H., Su, B., Butts, I. A. E., Dunham, R. A., & Wang, X. (2022) · GigaScience · doi:10.1093/gigascience/giac070 · Cited by 18
Liu, H.-S., Wang, W.-H., Song, Y.-F., Yang, Y., Zhang, D.-Y., Bai, W.-N., & Zhang, B.-W. (2025) · Current Biology · doi:10.1016/j.cub.2025.09.017 · Cited by 5
Metadata
- Authors
- Yin, L., Zhang, H., Tang, Z., Xu, J., Yin, D., Zhang, Z., Yuan, X., Zhu, M., Zhao, S., Li, X., & Liu, X.
- Venue
- Genomics, Proteomics & Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2021-03-02
- Volume
- 19
- Issue
- 4
- Pages
- 619-628
- ISSN
- 1672-0229 (ISSN-L)
- 2210-3244
- OpenAlex
- W3134360847
- Cited by
- 1,250
- References
- 30
- FWCI
- 89.64
- Type
- journal article
- Language
- en
- Open access
- gold
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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. https://doi.org/10.1016/j.gpb.2020.10.007
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