DATA - GENOMIC RECURRENT SELECTION: MAXIMIZING THE EFFICIENCY OF PASSIFLORA BREEDING

This study aimed to implement and evaluate the potential of genomic selection within a recurrent breeding program for passion fruit, with the goal of developing a new cultivar resistant to cowpea aphid-borne mosaic virus (CABMV) and exhibiting strong agronomic performance. Data from the initial selection cycle of a Brazilian passion fruit breeding program were utilized. The experiment was laid out in a randomized block design consisting of 90 full-sib families, each with three replicates and three plants per plot. A total of 156 passion fruit plants were assessed, exhibiting varying levels of virus symptom severity. The traits analyzed included mean area under the disease progress curve (MAUDPC), number of fruits per plant, total production per plant, fruit length, fruit diameter, average fruit weight, pulp weight, peel thickness, soluble solids content, and pulp yield. The Random Regression/Ridge Regression Best Linear Unbiased Predictor (RR-BLUP) method was employed for genomic selection. MAUDPC demonstrated the highest heritability. The information derived from genomic breeding values for important passion fruit trait, using the RR-BLUP approach, illustrated a notable potential for more precise estimation of breeding values, thus expediting the selection of superior individuals. This contributes to the implementation of genomic recurrent selection, enabling the development of more effective strategies and enhancing the efficiency of UENF's passion fruit breeding program.