Breeding for wheat stripe rust resistance: Understanding genetic architecture and evaluating genomic prediction in the uruguayan breeding program

Abstract

Wheat stripe (yellow) rust, caused by Puccinia striiformis f. sp. tritici (Pst), is one of the most devastating diseases of wheat worldwide. Stripe rust was a minor disease in Uruguay until 2016. Since the severe epidemic in 2017, stripe rust has become the most economically important wheat foliar disease in Uruguay. In the short term, local management of ongoing epidemics on susceptible wheat varieties is limited to fungicide spraying. However, the deployment of resistant wheat cultivars appears as the most environmentally friendly strategy without additional cost for producers. Until 2017, the Uruguayan National Institute for Agricultural Research Wheat Breeding Program (WBP) did not consider resistance to Pst as a priority, therefore little is known about the genetic architecture of the disease resistance in local germplasm, a key requirement for developing resistant varieties. With the general objective of contributing to the increase and sustainability of national wheat production through the development of stripe rust-resistant cultivars, we defined a diverse mapping population to 1) characterize wheat resistance architecture to stripe rust using genome-wide association studies (GWAS) and 2) predict wheat lines behavior to stripe rust using genomic prediction (GP) models. The GWAS population of 368 wheat lines, including WBP and the main cultivars grown locally, was field phenotyped in La Estanzuela Experimental Station for two years (2021 and 2022) under artificial inoculations. The area under the disease progress curve (AUDPC) was the response variable based on six evaluation dates each year. Adjusted means of both years were computed due to the substantial correlation observed between consecutive years, reaching a coefficient of 0.84. Additionally, the population was genotyped using genotyping by sequencing (GBS) at the Biotechnology Center of the University of Wisconsin. SNPs calling was performed using TASSEL 5, followed by imputation of missing data using Beagle. Over 100 thousand SNPs were obtained after filtering. GWAS analyses were performed with GWASpoly package. Seven genomic regions associated with resistance to Pst were identified on chromosomes 1B, 2B, 3A, 5B, 6B, and 7B, each explaining 3-6% of the phenotypic variance. These genomic regions appear to confer quantitative resistance, a trait anticipated within a program where all-stage resistance had not been deliberately employed. The QTL identified on chromosome 1B is located closely to the previously reported Yr29/Lr46 gene, while the remaining QTL are in the process of verification, to determine if these correspond to previously reported Yr genes or QTL. For GP, prediction accuracy was evaluated by computing the correlation between adjusted phenotypic means and predicted values using 10-fold cross-validation with the GBLUP model in the rrBLUP package. The prediction accuracy obtained was on average 0.64, which positions the GP as a promising tool for selecting resistant lines in the WBP. These results provide valuable knowledge and tools to improve YR genetic resistance in the WBP.