Assessment of the Potential for Genomic Selection To Improve Husk Traits in Maize. Issue 10 (1st October 2020)

• Record Type: Journal Article
• Authors: Cui, Zhenhai; Dong, Haixiao; Zhang, Ao; Ruan, Yanye; He, Yan; Zhang, Zhiwu
• Is Part Of: G3. Volume 10:Issue 10(2020)
• Page Start: 3741
• Page End: 3749
• Publication Date: 2020-10-01
• Subjects: genomic selection -- husk -- population structure -- prediction accuracy -- maize -- gBLUP -- marker assisted selection -- breeding -- rrBLUP -- GAPIT -- GenPred -- Genomic -- Prediction -- Shared data resources; Genetics -- Research -- Periodicals; Genomics -- Periodicals; Genetics; Genomics; Genes; Genetics -- Research; Genomics; Electronic journals; Periodical; Periodicals; Fulltext; Internet Resources; Periodicals; 572.8
• DOI: 10.1534/g3.120.401600
• Languages: English
• ISSNs: 2160-1836

Abstract

Abstract: Husk has multiple functions such as protecting ears from diseases, infection, and dehydration during development. Additionally, husks comprised of fewer, shorter, thinner, and narrower layers allow faster moisture evaporation of kernels prior to harvest. Intensive studies have been conducted to identify appropriate husk architecture by understanding the genetic basis of related traits, including husk length, husk layer number, husk thickness, and husk width. However, marker-assisted selection is inefficient because the identified quantitative trait loci and associated genetic loci could only explain a small proportion of total phenotypic variation. Genomic selection (GS) has been used successfully on many species including maize on other traits. Thus, the potential of using GS for husk traits to directly identify superior inbred lines, without knowing the specific underlying genetic loci, is well worth exploring. In this study, we compared four GS models on a maize association population with 498 inbred lines belonging to four subpopulations, including 27 lines in stiff stalk, 67 lines in non-stiff stalk, 193 lines in tropical-subtropical, and 211 lines in mixture subpopulations. Genomic Best Linear Unbiased Prediction with principal components as cofactor, performed the best and was selected to examine the impact of interaction between sampling proportions and subpopulations. We found that predictions on inbred lines in a subpopulation were benefited from excluding individuals from other subpopulations for training if the training population within the subpopulation was large enough. Husk thickness exhibited the highest prediction accuracy among all husk traits. These results gave strategic insight to improve husk architecture.