Detection and validation of genomic regions associated with resistance to rust diseases in a worldwide hexaploid wheat landrace collection using BayesR and mixed linear model approaches

Abstract

BayesR and MLM association mapping approaches in common wheat landraces were used to identify genomic regions conferring resistance to Yr, Lr, and Sr diseases. Deployment of rust resistant cultivars is the most economically effective and environmentally friendly strategy to control rust diseases in wheat. However, the highly evolving nature of wheat rust pathogens demands continued identification, characterization, and transfer of new resistance alleles into new varieties to achieve durable rust control. In this study, we undertook genome-wide association studies (GWAS) using a mixed linear model (MLM) and the Bayesian multilocus method (BayesR) to identify QTL contributing to leaf rust (Lr), stem rust (Sr), and stripe rust (Yr) resistance. Our study included 676 pre-Green Revolution common wheat landrace accessions collected in the 1920-1930s by A.E. Watkins. We show that both methods produce similar results, although BayesR had reduced background signals, enabling clearer definition of QTL positions. For the three rust diseases, we found 5 (Lr), 14 (Yr), and 11 (Sr) SNPs significant in both methods above stringent false-discovery rate thresholds. Validation of marker-trait associations with known rust QTL from the literature and additional genotypic and phenotypic characterisation of biparental populations showed that the landraces harbour both previously mapped and potentially new genes for resistance to rust diseases. Our results demonstrate that pre-Green Revolution landraces provide a rich source of genes to increase genetic diversity for rust resistance to facilitate the development of wheat varieties with more durable rust resistance.

Characterization of molecular diversity and genome-wide association study of stripe rust resistance at the adult plant stage in Northern Chinese wheat landraces

“…Variation in the prevalent pathogenic races in different trials and genotype by environment interactions may have led to difference in the number of resistant accessions across environments. Such findings have been reported in many previous studies [61,62]. Genetic diversity is the probability of two randomly chosen alleles from the population being different; PIC estimates the detection power and informativeness of the molecular markers [63,64].…”

Characterizing and Validating Stripe Rust Resistance Loci in US Pacific Northwest Winter Wheat Accessions (Triticum aestivum L.) by Genome‐wide Association and Linkage Mapping

“…Nevertheless, a major concern of GWAS results is incorrectly identifying linked MTAs (Type I error). A prudent method to identify and reduce these errors is to validate the GWAS results using other populations or other mapping methods (Korte and Farlow, 2013; Pasam et al, 2017) prior to using the markers for MAS. We used a second PNW GWAS population (Panel‐2), which consisted of more historical PNW winter wheat accessions, and validated the all‐stage resistance MTAs on chromosome 1B and field resistance MTAs on chromosomes 2A and 2B.…”

Genome-wide association mapping of black point reaction in common wheat (Triticum aestivum L.)

“…Thus, GWAS has become a powerful alternative approach for linkage mapping [ 29 ]. GWAS has been applied to investigate a range of traits, including disease resistance [ 30 , 31 ], end-use quality [ 32 ], and yield components [ 33 – 35 ].…”