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

Abstract

Genome prediction (GP) significantly enhances genetic gain by improving selection efficiency and shortening crop breeding cycles. Using a nested association mapping (NAM) population a set of diverse scenarios were assessed to evaluate GP for vital agronomic traits in B. napus. GP accuracy was examined by employing different models, marker sets, population sizes, marker densities, and incorporating genome-wide association (GWAS) markers. Eight models, including linear and semi-parametric approaches, were tested. The choice of model minimally impacted GP accuracy across traits. Notably, two models, rrBLUP and RKHS, consistently yielded the highest prediction accuracies. Employing a training population of 1500 lines or more resulted in increased prediction accuracies. Inclusion of single nucleotide absence polymorphism (SNaP) markers significantly improved prediction accuracy, with gains of up to 15%. Utilizing the Brassica 60K Illumina SNP array, our study effectively revealed the genetic potential of the B. napus NAM panel. It provided estimates of genomic predictions for crucial 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. Core ideasO_LIGenome prediction (GP) enhances genetic gains by improving selection efficiency and shortening breeding cycles. C_LIO_LIFactors influencing GP accuracy include model choice, marker types, and population size. C_LIO_LIInclusion of SNaP markers and highly significant GWAS markers improves prediction accuracy, shedding light on achievable genetic gains. C_LI Plain SummaryGenome prediction (GP) is a powerful tool that helps us improve crops more efficiently. In this study, we assessed how well GP works for predicting important traits in Brassica napus plants. We tested different models and marker sets to see which ones were most accurate. We found that two models, rrBLUP and RKHS, were consistently the best. Also, including certain types of genetic markers, like SNaP markers and highly significant GWAS markers, improved the predictions. Overall, our study shows that GP can help us understand the genetic potential of B. napus plants and improve breeding strategies, which can be exploited to develop better varieties more quickly, which is good news for farmers and the food supply.