Modeling and simulation of recurrent phenotypic and genomic selections in plant breeding under the presence of epistasis

作     者：Mohsin Ali Luyan Zhang Ian DeLacy Vivi Arief Mark Dieters Wolfgang H.Pfeiffer Jiankang Wang Huihui Li Mohsin Ali;Luyan Zhang;Ian DeLacy;Vivi Arief;Mark Dieters;Wolfgang H. Pfeiffer;Jiankang Wang;Huihui Li

作者机构：National Key Facility for Crop Gene Resources and Genetic ImprovementInstitute of Crop SciencesChinese Academy of Agricultural SciencesBeijing 100081China School of Agriculture and Food SciencesThe University of QueenslandBrisbaneQLD 4072Australia HarvestPlus Challenge Programmec/o International Food Policy Research Institute(IFPRI)1201 Eye StNWWashingtonDC 20005USA 

出 版 物：《The Crop Journal》 (作物学报（英文版）)

年 卷 期：2020年第8卷第5期

页      面：866-877页

核心收录：

学科分类：0710[理学-生物学] 07[理学] 08[工学] 09[农学] 071007[理学-遗传学] 0901[农学-作物学] 0902[农学-园艺学] 0836[工学-生物工程] 090102[农学-作物遗传育种] 

基　　金：This work was financially supported by the National Key Research and Development Program of China(2015BAD02B01-2-2) the HarvestPlus Challenge Program(www.harvestplus.org) 

主　　题：breeding elite additive 

摘      要：Recurrent selection is an important breeding method for population improvement and selecting elite inbreds or fixed lines from the improved ***,a computer simulation tool called QuMARS has been developed,which allows the simulation and optimization of various recurrent selection *** major objective in this study was to use the QuMARS tool to compare phenotypic recurrent,marker-assisted recurrent,and genomic selections(abbreviated respectively as PS,MARS and GS)for both short-and long-termbreeding ***,twomarker selection models were considered,i.e.,stepwise(Rstep)and forward regressions(Forward).For GS,three prediction models were considered,i.e.,genomic best linear unbiased predictors(GBLUP),ridge regression(Ridge),and regression by Moore-Penrose general inverse(InverseMP).To generate genotypes and phenotypes for a given individual during simulation,one additive and two epistasis genetic models were considered with three levels of *** demonstrated that selection responses from GBLUP-based GS and MARS(Forward)were consistently greater than those from PS under the additive model,particularly in early selection *** contrast,selection response from PS was consistently superior over MARS and GS under epistatic *** the two epistasis models,total genetic variance and the additive variance component were increased in some cases after *** simulation,we concluded that GS and PS were effective recurrent selection methods for improved breeding of targeted traits controlled by additive and epistatic quantitative trait loci(QTL).QuMARS provides an opportunity for breeders to compare,optimize and integrate new technology into their conventional breeding programs.