Comparison of sequencing-based and array-based genotyping platforms for genomic prediction of maize hybrid performance  

作　　者：Guangning Yu Yanru Cui Yuxin Jiao Kai Zhou Xin Wang Wenyan Yang Yiyi Xu Kun Yang Xuecai Zhang Pengcheng Li Zefeng Yang Yang Xu Chenwu Xu 

机构地区：[1]Key Laboratory of Plant Functional Genomics of the Ministry of Education/Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding/Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops,College of Agriculture,Yangzhou University,Yangzhou 225009,Jiangsu,China [2]State Key Laboratory of North China Crop Improvement and Regulation,Hebei Agricultural University,Baoding 071001,Hebei,China [3]International Maize and Wheat Improvement Center(CIMMYT),06600 Mexico D.F.,Mexico

出　　处：《The Crop Journal》2023年第2期490-498,共9页作物学报（英文版）

基　　金：supported by grants from the National Natural Science Foundation of China(32061143030,32170636,32100448);the Key Research and Development Program of Jiangsu Province(BE2022343);the Seed Industry Revitalization Project of Jiangsu Province(JBGS[2021]009);Project of Hainan Yazhou Bay Seed Lab(B21HJ0223);the State Key Laboratory of North China Crop Improvement and Regulation(NCCIR2021KF-5,NCCIR2021ZZ-4);Jiangsu Province Agricultural Science and Technology Independent Innovation(CX(21)1003);the Independent Scientific Research Project of the Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding(PLR202102);the Open Funds of the Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding(PL202005);Yangzhou University High-end Talent Support Program,and the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD).

摘　　要：Genomic selection(GS)is a powerful tool for improving genetic gain in maize breeding.However,its routine application in large-scale breeding pipelines is limited by the high cost of genotyping platforms.Although sequencing-based and array-based genotyping platforms have been used for GS,few studies have compared prediction performance among platforms.In this study,we evaluated the predictabilities of four agronomic traits in 305 maize hybrids derived from 149 parental lines subjected to genotyping by sequencing(GBS),a 40K SNP array,and target sequence capture(TSC)using eight GS models.The GBS marker dataset yielded the highest predictabilities for all traits,followed by TSC and SNP array datasets.We investigated the effect of marker density and statistical models on predictability among genotyping platforms and found that 1K SNPs were sufficient to achieve comparable predictabilities to 10K and all SNPs,and BayesB,GBLUP,and RKHS performed well,while XGBoost performed poorly in most cases.We also selected significant SNP subsets using genome-wide association study(GWAS)analyses in three panels to predict hybrid performance.GWAS facilitated selecting effective SNP subsets for GS and thus reduced genotyping cost,but depended heavily on the GWAS panel.We conclude that there is still room for optimization of the existing SNP array,and using genotyping by target sequencing(GBTS)techniques to integrate a few functional markers identified by GWAS into the 1K SNP array holds great promise of being an effective strategy for developing desirable GS breeding arrays.

关 键 词：Genomic selection Maize GBS SNP array Marker density 

分 类 号：S513[农业科学—作物学]