DArTseq physical mapping of QTLs linked to Karnal bunt (Tilletia indica) resistance in two historical wheat populations - Emebiri, Livinus; Singh, Sukhwinder; Tan, Mui‐Keng (Plant pathology, 2021)
Karnal bunt, a disease of wheat, durum, rye, and triticale, is subject to strict quarantine restrictions worldwide. The disease is considered a major threat to food security, due to its use as a non‐tariff trade barrier by several wheat‐importing countries. In this paper, we analysed seven years of phenotypic data to search for quantitative trait loci (QTLs) associated with resistance in common wheat, validated the QTLs using an independent population, and assessed the potential of genomic selection as a tool for pre‐emptive breeding. The QTL study used phenotypic data collected from artificially inoculated field experiments involving two historical Karnal bunt resistance populations: WH542 × HD29 and WH542 × W485. QTL analyses detected four significant (p < 0.001) QTLs on chromosomes 1A, 3A, 4B, and 6B, which explained between 13.7% and 15.7% of the phenotypic variation. A panel of 130 cultivars was used to validate QTL effects. These were genotyped with the same DArTseq protocol, and two of the four QTLs were significantly (p < 0.001) associated with Karnal bunt resistance in the validation panel. The potential of genomic selection was investigated by comparing accuracies of a model trained with all available markers and a model based solely on validated QTL information from the biparental populations. Genomic prediction accuracy, based on the two scenarios, averaged 0.43 and 0.33, respectively, suggesting that even in situations where phenotyping is difficult due to quarantine restrictions, the prospects for pre‐emptive breeding against Karnal bunt are encouraging, and resources are now available that will reduce the cost burden. Physical map locations of main‐effect quantitative trait loci were identified for Karnal bunt resistance in two historical wheat biparental populations using seven environment‐years of phenotypic data.