Performing Genome-Wide Association Studies with Multiple Models Using GAPIT

Genome-wide association study (GWAS) is based on the linkage disequilibrium (LD) between phenotypes and genetic markers covering the whole genome. Besides the genetic linkage between the genetic markers and the causal mutations, many other factors contribute to the LD, including selection and nonrandom mating formatting population structure. Many methods have been developed with accompany of corresponding software such as multiple loci mixed model (MLMM). There are software packages that implement multiple methods to reduce the learning curve. One of them is the Genomic Association and Prediction Integrated Tool (GAPIT), which implemented eight models including GLM (General Linear Model), Mixed Linear Model (MLM), Compressed MLM, MLMM, SUPER (Settlement of mixed linear models Under Progressively Exclusive Relationship), FarmCPU (Fixed and random model Circulating Probability Unification), and BLINK (Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway). Besides the availability of multiple models, GAPIT provides comprehensive functions for data quality control, data visualization, and publication-ready quality graphic outputs, such as Manhattan plots in rectangle and circle formats, quantile-quantile (QQ) plots, principal component plots, scatter plot of minor allele frequency against GWAS signals, plots of LD between associated markers and the adjacent markers. GAPIT developers and users established a community through the GAPIT forum ( https://groups.google.com/g/gapit-forum ) with over 600 members for asking questions, making comments, and sharing experiences. In this chapter, we detail the GAPIT functions, input data frame, output files, and example codes for each GWAS model. We also interpret parameters, functional algorithms, and modules of GAPIT implementation.