| Literature DB >> 22796960 |
Alexander E Lipka1, Feng Tian, Qishan Wang, Jason Peiffer, Meng Li, Peter J Bradbury, Michael A Gore, Edward S Buckler, Zhiwu Zhang.
Abstract
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.Mesh:
Year: 2012 PMID: 22796960 DOI: 10.1093/bioinformatics/bts444
Source DB: PubMed Journal: Bioinformatics ISSN: 1367-4803 Impact factor: 6.937