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ARS Home » Northeast Area » Ithaca, New York » Robert W. Holley Center for Agriculture & Health » Plant, Soil and Nutrition Research » Research » Publications at this Location » Publication #346170

Research Project: Development and Application of Genetic, Genomic, and Bioinformatic Resources in Maize

Location: Plant, Soil and Nutrition Research

Title: GAPIT version 2: an enhanced integrated tool for genomic association and prediction

Author
item TANG, YANG - Northeast Agricultural University, China
item LIU, XIAOLEI - Huazhong Agricultural University
item WANG, JIABO - Northeast Agricultural University, China
item LI, MENG - Nanjing Agricultural University
item WANG, QISHAN - Shanghai Jiaotong University
item TIAN, FENG - China Agricultural University
item SU, ZHONGBIN - Northeast Agricultural University, China
item PAN, YUCHUN - Shanghai Jiaotong University
item LIU, DI - Heilongjiang University
item LIPKA, ALEXANDER - University Of Illinois
item Buckler, Edward - Ed
item ZHANG, ZHIWU - Washington State University

Submitted to: The Plant Genome
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/7/2016
Publication Date: 4/1/2016
Citation: Tang, Y., Liu, X., Wang, J., Li, M., Wang, Q., Tian, F., Su, Z., Pan, Y., Liu, D., Lipka, A., Buckler IV, E.S., Zhang, Z. 2016. GAPIT version 2: an enhanced integrated tool for genomic association and prediction. The Plant Genome. 7(7):2315-2326.

Interpretive Summary: The natural variation seen in trait across a species are the product of thousands of genes working together that are encoded by billions of base pairs of DNA. To identify the functional variants requires genetic mapping and sophisticated mathematical approaches to relate trait variation with DNA variation. GAPIT software package is widely used software package that runs on the R analysis platform, which is a mainstay of statistical and bioinformatic analyses. There have been tremendous innovation in developing novel statistical approaches for mapping the last 5 years, and these algorithms have been integrated in the new version of GAPIT. GAPIT is open source and freely available to the scientific community. This software will be widely used to dissect traits across numerous species.

Technical Abstract: Most human diseases and agriculturally important traits are complex. Dissecting their genetic architecture requires continued development of innovative and powerful statistical methods. Corresponding advances in computing tools are critical to efficiently use these statistical innovations and to enhance and accelerate biomedical and agricultural research and applications. The genome association and prediction integrated tool (GAPIT) was first released in 2012 and became widely used for genome-wide association studies (GWAS) and genomic prediction. The GAPIT implemented computationally efficient statistical methods, including the compressed mixed linear model (CMLM) and genomic prediction by using genomic best linear unbiased prediction (gBLUP). New state-of-the-art statistical methods have now been implemented in a new, enhanced version of GAPIT. These methods include factored spectrally transformed linear mixed models (FaST-LMM), enriched CMLM (ECMLM), FaST-LMM-Select, and settlement of mixed linear models under progressively exclusive relationship (SUPER). The genomic prediction methods implemented in this new release of the GAPIT include gBLUP based on CMLM, ECMLM, and SUPER. Additionally, the GAPIT was updated to improve its existing output display features and to add new data display and evaluation functions, including new graphing options and capabilities, phenotype simulation, power analysis, and cross-validation. These enhancements make the GAPIT a valuable resource for determining appropriate experimental designs and performing GWAS and genomic prediction. The enhanced R-based GAPIT software package uses state-of-the-art methods to conduct GWAS and genomic prediction. The GAPIT also provides new functions for developing experimental designs and creating publication-ready tabular summaries and graphs to improve the efficiency and application of genomic research.