GAPIT Version 2: An Enhanced Integrated Tool for Genomic Association and Prediction.

Area of Science:

• Genomics
• Bioinformatics
• Statistical Genetics

Background:

• Complex human diseases and agricultural traits have intricate genetic architectures.
• Developing innovative statistical methods and computing tools is crucial for advancing biomedical and agricultural research.
• The Genome Association and Prediction Integrated Tool (GAPIT) has been a widely adopted platform for genome-wide association studies (GWAS) and genomic prediction since 2012.

Purpose of the Study:

• To introduce a new, enhanced version of the GAPIT software package.
• To implement state-of-the-art statistical methods for GWAS and genomic prediction.
• To improve data display, evaluation functions, and experimental design capabilities within GAPIT.

Main Methods:

• Incorporation of advanced statistical models: 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).
• Integration of enhanced genomic prediction methods including gBLUP based on CMLM, ECMLM, and SUPER.
• Update of existing output display features and addition of new data visualization and analysis functions (graphing, phenotype simulation, power analysis, cross-validation).

Main Results:

• The enhanced GAPIT provides computationally efficient and powerful tools for GWAS and genomic prediction.
• New functionalities improve experimental design, data visualization, and analysis capabilities.
• The updated software facilitates the acceleration of biomedical and agricultural research.

Conclusions:

• The enhanced GAPIT is a valuable resource for researchers studying complex genetic traits.
• The software improves the efficiency and application of genomic research through state-of-the-art methods and enhanced features.
• GAPIT aids in determining appropriate experimental designs and performing robust GWAS and genomic prediction.