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.

Local adaptation drives the diversification of effectors in the fungal wheat pathogen Parastagonospora nodorum in the United States

“…Association mapping was conducted using TASSEL v5 [77] and GAPIT [86, 87]. A naïve model, as well as a model including the first three components derived from a PCA as fixed effects were used in association analyses in TASSEL v5.…”

BLINK: A Package for Next Level of Genome Wide Association Studies with Both Individuals and Markers in Millions

“…We used the exact same datasets we used in our previous publication for the FarmCPU 22 method. These datasets covered five species including Arabidopsis thaliana, human, 23 maize 35 , mouse 36 , and pig 37 . Markers with a Minor Allele Frequency (MAF) of 5% or below 24 were filtered out from the original datasets.…”

Genome‐wide Association Study of Agronomic Traits in a Spring‐Planted North American Elite Hard Red Spring Wheat Panel

“…Genome-wide association analysis on the 17 agronomic traits was performed using the FarmCPU method , which was implemented in GAPIT2 (Tang et al, 2016). Genome-wide association analysis on the 17 agronomic traits was performed using the FarmCPU method , which was implemented in GAPIT2 (Tang et al, 2016).…”

“…A total of 19,192 SNP markers were used to detect MTAs. Genome-wide association analysis on the 17 agronomic traits was performed using the FarmCPU method , which was implemented in GAPIT2 (Tang et al, 2016). To control for population structure, principal components were fitted as covariates and the corresponding quantile-quantile plots were compared to select the best model.…”