GAPIT: genome association and prediction integrated tool

TLDR

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. The purpose of this work is to develop an R package, GAPIT, that implements advanced statistical methods for genome-wide association studies and genomic prediction. GAPIT implements advanced statistical methods including the compressed mixed linear model (.

Abstract

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: [e-mail] Supplementary Information: Supplementary data are available at Bioinformatics online.