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 and phenotypic data, analytical methods and their associated software packages are frequently advanced. GAPIT is a widely-used genomic association and prediction integrated tool as an R package. 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 (CMLM), and genomic best linear unbiased prediction (gBLUP). The second version was released in 2016 with several new implementations, including enriched CMLM (ECMLM) and settlement of MLMs 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 multi-locus 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 CMLM (named compressed BLUP; cBLUP) and SUPER (named SUPER BLUP; sBLUP). 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 implementations, 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).

全基因组关联研究 ( GWAS ) 和基因组预测/选择 (GP/GS) 是基因组研究中的两个重要业务。由于基因组和表型数据的巨大和复杂性，分析方法及其相关软件包经常得到改进。GAPIT是一种广泛使用的基因组关联和预测集成工具，作为R包裹。第一个版本于 2012 年向公众发布，实现了一般线性模型 (GLM)、混合线性模型 (MLM)、压缩 MLM (CMLM) 和基因组最佳线性无偏预测 (gBLUP)。第二个版本于 2016 年发布，有几个新的实现，包括丰富的 CMLM (ECMLM) 和逐步排他关系下的 MLM 结算 (SUPER)。所有 GWAS 方法均基于单位点测试。在 GAPIT 的当前版本中，第 3 版首次实现了三种多位点测试方法，包括多位点混合模型（MLMM）、固定和随机模型循环概率统一（FarmCPU）以及贝叶斯信息和连锁不平衡迭代嵌套键槽 (BLINK)。此外，基于 CMLM 实现了两种 GP/GS 方法（命名为压缩 BLUP；cBLUP）和 SUPER（命名为 SUPER BLUP；sBLUP）。这些新的实施不仅提高了 GWAS 的统计能力和 GP/GS 的预测准确性，还提高了计算速度和分析大基因组数据的能力。在这里，我们通过描述最近开发的方法的选择、它们的实现和潜在影响来记录当前的 GAIT 升级。所有文档，包括源代码、用户手册、演示数据和教程，都可以在 GAIT 网站 (http://zzlab.net/GAIT) 上免费获得。我们通过描述最近开发的方法的选择、它们的实现和潜在影响来记录当前的 GAIT 升级。所有文档，包括源代码、用户手册、演示数据和教程，都可以在 GAIT 网站 (http://zzlab.net/GAIT) 上免费获得。我们通过描述最近开发的方法的选择、它们的实现和潜在影响来记录当前的 GAIT 升级。所有文档，包括源代码、用户手册、演示数据和教程，都可以在 GAIT 网站 (http://zzlab.net/GAIT) 上免费获得。