title: Predicting rice hybrid performance using univariate and multivariate GBLUP models based on North Carolina mating design II.
abstract: Genomic selection (GS) is more efficient than traditional phenotype-based methods in hybrid breeding. The present study investigated the predictive ability of genomic best linear unbiased prediction models for rice hybrids based on the North Carolina mating design II, in which a total of 115 inbred rice lines were crossed with 5 male sterile lines. Using 8 traits of the 575 (115 × 5) hybrids from two environments, both univariate (UV) and multivariate (MV) prediction analyses, including additive and dominance effects, were performed. Using UV models, the prediction results of cross-validation indicated that including dominance effects could improve the predictive ability for some traits in rice hybrids. Additionally, we could take advantage of GS even for a low-heritability trait, such as grain yield per plant (GY), because a modest increase in the number of top selection could generate a higher, more stable mean phenotypic value for rice hybrids. Thus this strategy was used to select superior potential crosses between the 115 inbred lines and those between the 5 male sterile lines and other genotyped varieties. In our MV research, an MV model (MV-ADV) was developed utilizing a MV relationship matrix constructed with auxiliary variates. Based on joint analysis with multi-trait (MT) or with multi-environment, the prediction results confirmed the superiority of MV-ADV over an UV model, particularly in the MT scenario for a low-heritability target trait (such as GY), with highly correlated auxiliary traits. For a high-heritability trait (such as thousand-grain weight), MT prediction is unnecessary, and UV prediction is sufficient.
authors.affiliation: Jiangsu Provincial Key Laboratory of Crop Genetics and Physiology/Co-Innovation Center for Modern Production Technology of Grain Crops, Key Laboratory of Plant Functional Genomics of Ministry of Education, Yangzhou University, Yangzhou, China.
authors.forename: X
authors.initials: X
authors.lastname: Wang
authors.affiliation: Hunan Provincial Key Laboratory for Biology and Control of Plant Disease and Insect Pests, College of Plant Protection, Hunan Agricultural University, Changsha, China.
authors.forename: L
authors.initials: L
authors.lastname: Li
authors.affiliation: Jiangsu Provincial Key Laboratory of Crop Genetics and Physiology/Co-Innovation Center for Modern Production Technology of Grain Crops, Key Laboratory of Plant Functional Genomics of Ministry of Education, Yangzhou University, Yangzhou, China.
authors.forename: Z
authors.initials: Z
authors.lastname: Yang
authors.affiliation: State Key Laboratory of Hybrid Rice, College of Life Science, Wuhan University, Wuhan, China.
authors.forename: X
authors.initials: X
authors.lastname: Zheng
authors.affiliation: College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China.
authors.forename: S
authors.initials: S
authors.lastname: Yu
authors.affiliation: Jiangsu Provincial Key Laboratory of Crop Genetics and Physiology/Co-Innovation Center for Modern Production Technology of Grain Crops, Key Laboratory of Plant Functional Genomics of Ministry of Education, Yangzhou University, Yangzhou, China.
authors.forename: C
authors.initials: C
authors.lastname: Xu
authors.affiliation: State Key Laboratory of Hybrid Rice, College of Life Science, Wuhan University, Wuhan, China.
authors.forename: Z
authors.initials: Z
authors.lastname: Hu
mesh_terms: D023281:Genomics / Q000379:methods; D005838:Genotype; D006824:Hybridization, Genetic; D008957:Models, Genetic; D009657:North Carolina; D012275:Oryza / Q000235:genetics*; D010641:Phenotype; D000069600:Plant Breeding; D012641:Selection, Genetic