Genomic selection (GS) is a powerful tool for crop variety improvement. GS can predict the breeding values of individuals early in the breeding cycle to reduce the generation interval and thereby increase genetic gain per unit of time. Genomic Estimated Breeding Values (GEBVs) are used to make informed selections in breeding and select best performing parents for cross hybridization to generate genetic stocks with improved combination of beneficial alleles.

In this webinar, Dr. Kaur will describe recent advances in the integration of genomic predictions in pulse pre-breeding (chickpea) and breeding (lentil and field pea). Building on 10+ years of historical data in lentil and field pea breeding, genomic prediction equations for key breeding traits including yield, seed weight, size and protein content, and multiple abiotic and biotic stress tolerances have been derived with moderate to high prediction accuracies (0.3-0.8). Genomic estimated breeding values, selection indices and crossing schemes designed using optimal parental combinations have been delivered to breeders. Stochastic computer simulations investigating the benefit of GS optimization strategies for disease resistance, grain weight and yield in multiple environments with different levels of genotype-by-environment interactions have indicated increased genetic gain per unit of time compared to phenotypic selection.

More recently, GS has been applied in chickpea pre-breeding for increasing resistance to ascochyta blight (AB). Results indicate that the severity of ascochyta blight infection in chickpeas can be remarkably reduced by c. 28% per breeding cycle using genomic selection. A high impact of genomic prediction application in chickpea pre-breeding as well as lentil and field pea breeding programs is anticipated.