Genomic selection for target traits in the Australian lentil breeding program

Abstract

Genomic selection (GS) uses associations between markers and phenotypes to predict the breeding values of individuals. It can be applied early in the breeding cycle to reduce the cross-to-cross generation interval and thereby increase genetic gain per unit of time. The development of cost-effective, high-throughput genotyping platforms has revolutionized plant breeding programs by enabling the implementation of GS at the scale required to achieve impact. As a result, GS is becoming routine in plant breeding, even in minor crops such as pulses. Here we examined 2,081 breeding lines from Agriculture Victoria’s national lentil breeding program for a range of target traits including grain yield, ascochyta blight resistance, botrytis grey mould resistance, salinity and boron stress tolerance, 100-grain weight, seed size index and protein content. A broad range of narrow-sense heritabilities was observed across these traits (0.24-0.66). Genomic prediction models were developed based on 64,781 genome-wide SNPs using Bayesian methodology and genomic estimated breeding values (GEBVs) were calculated. Forward cross-validation was applied to examine the prediction accuracy of GS for these targeted traits. The accuracy of GEBVs was consistently higher (0.34-0.83) than BLUP estimated breeding values (EBVs) (0.22-0.54), indicating a higher expected rate of genetic gain with GS. GS-led parental selection using early generation breeding materials also resulted in higher genetic gain compared to BLUP-based selection performed using later generation breeding lines. Our results show that implementing GS in lentil breeding will fast track the development of high-yielding cultivars with increased resistance to biotic and abiotic stresses, as well as improved seed quality traits.

Genomic prediction of yield components in soybean under Asian rust pressure for early-generation selection

“…Several studies on various crops have shown that genomic prediction and selection hold the potential to increase e ciency, allowing not only the optimization of existing resources, but also the reduction of the preceding requirements (Duangjit et al 2016;Matei et al 2018;Gebremedhin et al 2024). Adopting this methodology will reduce the cost of genotyping, allow the development and use of statistical models capable of tting genome-wide marker data, and the increase data processing capacity (Wartha and Lorenz 2021; Covarrubias-Pazaran 2022).…”