Accuracy of Selection in Early Generations of Field Pea Breeding Increases by Exploiting the Information Contained in Correlated Traits

Accuracy of predicted breeding values (PBV) for low heritability traits may be increased in early generations by exploiting the information available in correlated traits. We compared the accuracy of PBV for 10 correlated traits with low to medium narrow-sense heritability (h(2)) in a genetically diverse field pea (Pisum sativum L.) population after univariate or multivariate linear mixed model (MLMM) analysis with pedigree information. In the contra-season, we crossed and selfed S-1 parent plants, and in the main season we evaluated spaced plants of S-0 cross progeny and S2+ (S-2 or higher) self progeny of parent plants for the 10 traits. Stem strength traits included stem buckling (SB) (h(2) = 0.05), compressed stem thickness (CST) (h(2) = 0.12), internode length (IL) (h(2) = 0.61) and angle of the main stem above horizontal at first flower (EAngle) (h(2) = 0.46). Significant genetic correlations of the additive effects occurred between SB and CST (0.61), IL and EAngle (-0.90) and IL and CST (-0.36). The average accuracy of PBVs in S-0 progeny increased from 0.799 to 0.841 and in S2+ progeny increased from 0.835 to 0.875 in univariate vs MLMM, respectively. An optimized mating design was constructed with optimal contribution selection based on an index of PBV for the 10 traits, and predicted genetic gain in the next cycle ranged from 1.4% (SB), 5.0% (CST), 10.5% (EAngle) and -10.5% (IL), with low achieved parental coancestry of 0.12. MLMM improved the potential genetic gain in annual cycles of early generation selection in field pea by increasing the accuracy of PBV.

Automated summary

The accuracy of predicted breeding values (PBV) for low heritability traits can be increased by utilizing information from correlated traits. A study compared the accuracy of PBV for 10 correlated traits in a genetically diverse field pea population using univariate or multivariate linear mixed model (MLMM) analysis. The MLMM approach improved the accuracy of PBV and showed potential for increasing genetic gains in early generation selection cycles.