title: GAPIT Version 2: An Enhanced Integrated Tool for Genomic Association and Prediction.
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.
authors.forename: You
authors.initials: Y
authors.lastname: Tang
authors.forename: Xiaolei
authors.initials: X
authors.lastname: Liu
authors.forename: Jiabo
authors.initials: J
authors.lastname: Wang
authors.forename: Meng
authors.initials: M
authors.lastname: Li
authors.forename: Qishan
authors.initials: Q
authors.lastname: Wang
authors.forename: Feng
authors.initials: F
authors.lastname: Tian
authors.forename: Zhongbin
authors.initials: Z
authors.lastname: Su
authors.forename: Yuchun
authors.initials: Y
authors.lastname: Pan
authors.forename: Di
authors.initials: D
authors.lastname: Liu
authors.forename: Alexander E
authors.initials: AE
authors.lastname: Lipka
authors.forename: Edward S
authors.initials: ES
authors.lastname: Buckler
authors.forename: Zhiwu
authors.initials: Z
authors.lastname: Zhang
mesh_terms: D016678:Genome; D055106:Genome-Wide Association Study / Q000379:methods*; D023281:Genomics / Q000379:methods* / Q000639:trends; D006801:Humans; D016014:Linear Models; D008957:Models, Genetic; D010641:Phenotype; D012984:Software