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CC BY-NC-ND V4.0, Open Access
Journal article
GAPIT Version 2: An Enhanced Integrated Tool for Genomic Association and Prediction
The plant genome, Vol.9(2), pp.1-9
07/2016
Handle:
https://hdl.handle.net/2376/109681
PMID: 27898829
Abstract
Core Ideas
Genome‐wide association study
Genomic prediction
Simulation and experimental design
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.
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Details
- Title
- GAPIT Version 2: An Enhanced Integrated Tool for Genomic Association and Prediction
- Creators
- You Tang - College of Electrical and Information, Northeast Agricultural UnivXiaolei Liu - Huazhong Agricultural UniversityJiabo Wang - Institute of Animal Husbandry, Heilongjiang Academy of Agricultural ScienceMeng Li - College of Horticulture, Nanjing Agricultural UnivQishan Wang - Shanghai Jiaotong UnivFeng Tian - National Maize Improvement Center of China, China Agricultural UnivZhongbin Su - College of Electrical and Information, Northeast Agricultural UnivYuchun Pan - Shanghai Jiaotong UnivDi Liu - Animal Science Research InstituteAlexander E Lipka - Univ. of IllinoisEdward S Buckler - USDA‐ ARSZhiwu Zhang - Washington State University
- Publication Details
- The plant genome, Vol.9(2), pp.1-9
- Academic Unit
- Department of Crop and Soil Sciences
- Publisher
- Crop Science Society of America
- Number of pages
- 9
- Grant note
- 1238014 / Division Of Integrative Organismal Systems; Direct For Biological Sciences; National Science Foundation (NSF); NSF - Directorate for Biological Sciences (BIO); NSF - Division of Integrative Organismal Systems (IOS) 0922493; 1238014 / NSF; National Science Foundation (NSF) 31301748 / National Natural Science Foundation of China; National Natural Science Foundation of China (NSFC) USDA-ARS; United States Department of Agriculture (USDA); USDA Agricultural Research Service Agricultural Research Center at Washington State University 2014M551607 / China Postdoctoral Science Foundation College of Agricultural, Human, and Natural Resource Sciences 126593 / Washington Grain Commission
- Identifiers
- 99900547385101842
- Language
- English
- Resource Type
- Journal article