GAPIT Version 3: Boosting Power and Accuracy for Genomic Association and Prediction

Abstract

Genome-Wide Association Study (GWAS) and Genomic Prediction/Selection (GP/GS) are the two essential enterprises in genomic research. Due to the great magnitude and complexity of genomic data, analytical methods and their associated software packages are frequently advanced. GAPIT is a widely used Genomic Association and Prediction Integrated Tool. The first version was released to the public in 2012 with the implementation of the general linear model (GLM), mixed linear model (MLM), compressed MLM, and genomic Best Linear Unbiased Prediction (gBLUP). The second version was released in 2016 with several new implementations, including Enriched Compressed MLM and Settlement of mixed linear models Under Progressively Exclusive Relationship (SUPER). All the GWAS methods are based on the single locus test. For the first time, in the current release of GAPIT, version 3 implemented three multiple loci test methods, including Multiple Loci Mixed Model (MLMM), Fixed and random model Circulating Probability Unification (FarmCPU), and Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK). Additionally, two GP/GS methods were implemented based on Compressed MLM, named compressed BLUP, and SUPER, named SUPER BLUP. These new implementations not only boost statistical power for GWAS and prediction accuracy for GP/GS, but also improve computing speed and increase the capacity to analyze big genomic data. Here, we document the current upgrade of GAPIT by describing the selection of the recently developed methods, their implementation, and potential impact. All documents, including source code, user manual, demo data, and tutorials, are freely available at the GAPIT website (http://zzlab.net/GAPIT).

Genome-Wide Association Study of Brown Rot (Monilinia spp.) Tolerance in Peach

“…The GWAS was performed using both single- and multi-locus models, by GAPIT version 3 ( Wang and Zhang, 2020 ), mrMLM 4.0 ( Zhang et al, 2020 ), FarmCPU ( Liu et al, 2016 ) and G model ( Bernardo, 2013 ; Table 1 ). G model was carried out using GModel2 software 1 .…”

“…All the other GWAS approaches were performed for the same individuals used in G model analysis without excluding the markers with missing data and applying LD pruning, and analyses were performed in R V4.0.1 with the corresponding packages. Six different models, including four single-locus models (MLM, GLM, CMLM and SUPER) and two multi-locus models (BLINK and MLMM), were applied using GAPIT version 3 ( Wang and Zhang, 2020 ). The six multi-locus GWAS methods (mrMLM, FASTmrMLM, FASTmrEMMA, pLARmEB, pKWmEB, and ISIS EM-BLASSO) from mrMLM 4.0 ( Zhang et al, 2020 ) were also used in this study.…”

“…GAPIT is an R package widely used for GWAS analysis, and the recently updated version 3 ( Wang and Zhang, 2020 ) implemented 3 multi-locus methods, including Multiple Loci Mixed Model (MLMM) ( Segura et al, 2012 ), Fixed and random model Circulating Probability Unification (FarmCPU; Liu et al, 2016 ), and Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK) ( Huang et al, 2019 ), in addition to the previously implemented single-locus methods.…”

Discovery of three loci increasing resistance to charcoal rot caused by Macrophomina phaseolina in octoploid strawberry

“…To detect loci and their effects across representative genetic backgrounds, it is advantageous to simultaneously analyze pedigree-connected families that represent the full genetic variability of a population ( Mangandi et al 2017 ). Pedigree-based quantitative trait loci(QTL) analysis using FlexQTL™ software ( Bink et al 2014 ) and genome-wide association study (GWAS) approaches ( Wang and Zhang 2020 ) can both be applied to detect loci in complex breeding populations. FlexQTL™ utilizes Bayesian analysis methods with Markov chain Monte Carlo (MCMC) algorithms to estimate chromosomal location, number, mode, and magnitude of QTL in unbalanced population sets.…”

Genetic Analysis of Walnut (Juglans regia L.) Pellicle Pigment Variation Through a Novel, High-Throughput Phenotyping Platform

“…Genome-wide associations were conducted using the Fixed and Random Model Circulating Probability Unification (FarmCPU) algorithm ( Liu et al 2016 ) and the multi-locus mixed linear model (MLMM) algorithm ( Segura et al 2012 ) in the R package genomic association and prediction integrated tool (GAPIT v3) ( Wang and Zhang 2018 ). The MLMM is a multi-locus mixed linear model (MLM) ( Yu et al 2006 ) where both Q (population structure) + K (kinship matrix) are fitted to the model as random effects, so that type 1 errors are reduced from spurious associations due to relatedness and population structure.…”