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Bioinformatics (Oxford, England), Vol. 28, No. 18. (15 September 2012), pp. 2397-2399, doi:10.1093/bioinformatics/bts444 Key: citeulike:11219185
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SUMMARY: Software programs that conduct genome-wide association studies and genomic prediction and selection need to use methodologies that maximize statistical power, provide high prediction accuracy and run in a computationally efficient manner. We developed an R package called Genome Association and Prediction Integrated Tool (GAPIT) that implements advanced statistical methods including the compressed mixed linear model (CMLM) and CMLM-based genomic prediction and selection. The GAPIT package can handle large datasets in excess of 10 000 individuals and 1 million single-nucleotide polymorphisms with minimal computational time, while providing user-friendly access and concise tables and graphs to interpret results. Availability: http://www.maizegenetics.net/GAPIT. Contact: zhiwu.zhang@cornell.edu Supplementary Information: Supplementary data are available at Bioinformatics online.
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