Complex traits in plants are influenced by many genes, each having a small impact. Using marker-assisted selection (MAS) alone is not sufficient to improve these traits in elite cultivars. Genomic selection (GS) is a promising breeding approach for enhancing complex traits like resistance to Fusarium oxysporum f. sp. niveum (Fon) race 2 and increasing sugar levels (brix content) in watermelon. In our study, we wanted to see how well GS can predict disease resistance and sugar levels in an interspecific citron melon (Citrullus amarus) by cultivated watermelon (Citrullus lanatus) population. We created an F2:3 population by crossing USVL252-FR2 (resistant to Fon race 2, low brix; C. amarus) with ‘Sugar Baby’ (susceptible to Fon race 2, high brix; C. lanatus). We tested disease resistance in a growth chamber and measured sugar levels in a field trial using a randomized complete block design of the F3 families. We resequenced the DNA of 150 F2 plants to identify genetic differences. Disease response was assessed 28 days after inoculation, and sugar levels were measured with a brix meter to gauge sweetness. To predict disease resistance, we used two genomic models - Random Forest and GBLUP - which we found to be effective in previous studies. We compared the performance of univariate models (looking at disease and sugar levels separately) and bivariate models (looking at disease and sugar levels together) to identify the best approach for selecting superior cultivars based on these traits. We assessed model performance using ten-fold cross-validation. Our goal is to focus on these important polygenic traits and select superior genotypes early in breeding to develop watermelon cultivars with improved disease resistance and high sugar content.
