Harnessing genomic prediction in Brassica napus through a nested association mapping population

Genomic prediction (GP) significantly enhances genetic gain by improving selection efficiency and shortening crop breeding cycles. Using a nested association mapping population, a set of diverse scenarios were assessed to evaluate GP for important agronomic traits in Brassica napus, including plant height, days to flowering, 1000-kernel weight, and yield. GP accuracy was examined on each trait by employing eight different models, eight marker sets, varying population sizes and marker densities, and incorporating trait-associated markers identified through genome-wide association study analysis. Eight models, including linear and semi-parametric approaches, were tested. The choice of model minimally impacted GP accuracy across traits. Employing a training population of 1500 lines or more resulted in increased prediction accuracies. Inclusion of single nucleotide absence polymorphism markers with single-nucleotide polymorphism markers significantly improved prediction accuracy, with gains of up to 15%. The study provided estimates of GPs for major agronomic traits through varied prediction scenarios, shedding light on achievable genetic gains. These insights, coupled with marker application, can advance the breeding cycle acceleration in B. napus.