Genomic prediction and genomic heritability of grain yield and its related traits in a safflower genebank collection.

Abstract

Safflower, a minor oilseed crop, is gaining increased attention for food and industrial uses. Safflower genebank collections are an important genetic resource for crop enhancement and future breeding programs. In this study, we investigated the population structure of a safflower collection sourced from the Australian Grain Genebank and assessed the potential of genomic prediction (GP) to evaluate grain yield and related traits using single and multi-site models. Prediction accuracies (PA) of genomic best linear unbiased prediction (GBLUP) from single site models ranged from 0.21 to 0.86 for all traits examined and were consistent with estimated genomic heritability (h2 ), which varied from low to moderate across traits. We generally observed a low level of genome × environment interactions (g × E). Multi-site g × E GBLUP models only improved PA for accessions with at least some phenotypes in the training set. We observed that relaxing quality filtering parameters for genotype-by-sequencing (GBS), such as missing genotype call rate, did not affect PA but upwardly biased h2 estimation. Our results indicate that GP is feasible in safflower evaluation and is potentially a cost-effective tool to facilitate fast introgression of desired safflower trait variation from genebank germplasm into breeding lines.