Comparative genomic prediction of resistance to Fusarium wilt (Fusarium oxysporum f. sp. niveum race 2) in watermelon: parametric and nonparametric approaches.

Abstract

Complex traits influenced by multiple genes pose challenges for marker-assisted selection (MAS) in breeding. Genomic selection (GS) is a promising strategy for achieving higher genetic gains in quantitative traits by stacking favorable alleles into elite cultivars. Resistance to Fusarium oxysporum f. sp. niveum (Fon) race 2 in watermelon is a polygenic trait with moderate heritability. This study evaluated GS as an additional approach to quantitative trait loci (QTL) analysis/marker-assisted selection (MAS) for enhancing Fon race 2 resistance in elite watermelon cultivars. Objectives were to: (1) assess the accuracy of genomic prediction (GP) models for predicting Fon race 2 resistance in a F_2:3 versus a recombinant inbred line (RIL) population, (2) rank and select families in each population based on genomic estimated breeding values (GEBVs) for developing testing populations, and (3) determined how many of the most superior families based on GEBV also have all QTL associated with Fon race 2 resistance. GBS-SNP data from genotyping-by-sequencing (GBS) for two populations were used, and parental line genome sequences were used as references. The GBLUP and random forest outperformed the other three parametric (GBLUP, Bayes B, Bayes LASSO) and three nonparametric AI (random forest, SVM linear, and SVM radial) models, with correlations of 0.48 and 0.68 in the F_2:3 and RIL population, respectively. Selection intensities (SI) of 10%, 20%, and 30% showed that superior families with highest GEBV can also comprise all QTL associated with Fon race 2 resistance, highlighting GP efficacy in improving elite watermelon cultivars with polygenic traits of disease resistance.