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

作　　者：Jiabo Wang Zhiwu Zhang 

机构地区：[1]Key Laboratory of Qinghai-Tibetan Plateau Animal Genetic Resource Reservation and Utilization,Sichuan Province and Ministry of Education,Southwest Minzu University,Chengdu 610041,China [2]Department of Crop and Soil Sciences,Washington State University,Pullman,WA 99164,USA

出　　处：《Genomics, Proteomics & Bioinformatics》2021年第4期629-640,共12页基因组蛋白质组与生物信息学报（英文版）

基　　金：partially funded by National Science Foundation,the United States(Grant Nos.DBI 1661348 and ISO 2029933);the United States Department of Agriculture–National Institute of Food and Agriculture,the United States(Hatch Project No.1014919,Grant Nos.2018-70005-28792,2019-67013-29171,and 2020-67021-32460);the Washington Grain Commission,the United States(Endowment and Grant Nos.126593 and 134574);Sichuan Science and Technology Program,China(Grant Nos.2021YJ0269 and 2021YJ0266);the Program of Chinese National Beef Cattle and Yak Industrial Technology System,China(Grant No.CARS-37);Fundamental Research Funds for the Central Universities,China(Southwest Minzu University,Grant No.2020NQN26)。

摘　　要：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(g BLUP).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(Farm CPU),and Bayesian-information and linkage-disequilibrium iteratively nested keyway(BLINK).Additionally,two GP/GS methods were implemented based on CMLM(named compressed BLUP;c BLUP)and SUPER(named SUPER BLUP;s 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 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).

关 键 词：GWAS Genomic selection SOFTWARE R GAPIT 

分 类 号：Q811.4[生物学—生物工程]