Journal
BIOINFORMATICS
Volume 28, Issue 18, Pages 2397-2399Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/bts444
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Funding
- National Science Foundation [DBI-0321467, DBI0820619, DBI-0922493]
- United States Department of Agriculture - Agricultural Research Service
- Direct For Biological Sciences
- Division Of Integrative Organismal Systems [0820619] Funding Source: National Science Foundation
- Division Of Integrative Organismal Systems
- Direct For Biological Sciences [922493] Funding Source: National Science Foundation
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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.
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