计算机科学
R包
软件
数据挖掘
选择(遗传算法)
基因组
机器学习
计算生物学
生物
遗传学
计算科学
程序设计语言
基因
作者
Alexander E. Lipka,Feng Tian,Qishan Wang,Jason A. Peiffer,Meng Li,Peter J. Bradbury,Michael A. Gore,Edward S. Buckler,Zhiwu Zhang
出处
期刊:Bioinformatics
[Oxford University Press]
日期:2012-07-13
卷期号:28 (18): 2397-2399
被引量:1609
标识
DOI:10.1093/bioinformatics/bts444
摘要
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.http://www.maizegenetics.net/GAPIT.zhiwu.zhang@cornell.eduSupplementary data are available at Bioinformatics online.
科研通智能强力驱动
Strongly Powered by AbleSci AI