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.