|FENG, TIAN - Cornell University|
|WANG, QISHAN - Shanghai Jiaotong University|
|PEIFFER, JASON - Cornell University|
|LI, MENG - Nanjing Agricultural University|
|Buckler, Edward - Ed|
|ZHANG, ZHIWU - Cornell University|
Submitted to: Bioinformatics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/8/2012
Publication Date: 7/13/2012
Citation: Lipka, A.E., Feng, T., Wang, Q., Peiffer, J., Li, M., Bradbury, P., Gore, M.A., Buckler IV, E.S., Zhang, Z. 2012. GAPIT: genome association and prediction integrated tool. Bioinformatics. 28(18):2397-2399.
Interpretive Summary: Newly-developed high-throughput genotypic and phenotypic technologies are enabling unprecedented insight into the genetic components that underlie important traits, as well as the ability to predict disease risk and phenotypic values of crops or livestock. Software programs that analyze data from these technologies need to efficiently conduct state-of-the-art statistical methodologies and produce detailed summaries with minimal user input. To address these challenges, we developed the Genomic Association and Prediction Integrated Tool (GAPIT) package in the highly flexible R programming language. This package, which implements advanced statistical and computational methods, can handle extremely large data sets while at the same time provide user-friendly access and high quality result graphics. GAPIT therefore makes it possible for researchers with little to no programming experience to conduct sophisticated analyses of big data sets on an average desktop computer.
Technical Abstract: Advances in high throughput sequencing have improved the detection of genes underlying important traits as well as the prediction accuracy of disease risk and breeding value of crop or livestock. Software programs developed to perform statistical genetic analysis that support these activities should maximize statistical power in genome-wide association studies, provide high accuracy in genomic prediction and selection, and run in a computationally efficient manner. To address these challenges, we developed an R package called Genomic Association and Prediction Integrated Tool (GAPIT). This package implements advanced statistical methods that have been developed over the past ten years, including the compressed mixed linear model (CMLM) and CMLM-based genomic prediction and selection. The GAPIT package can handle large data sets in excess of over ten thousand individuals and one million SNPs using approaches that maximize statistical power, while at the same time providing user-friendly access and high quality result graphics. The GAPIT R package is freely available to the public at www.maizegenetics.net/GAPIT.