2017
DOI: 10.1007/s00122-016-2851-7
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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 th… Show more

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Cited by 37 publications
(36 citation statements)
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“…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].…”
Section: Discussionsupporting
confidence: 76%
“…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].…”
Section: Discussionsupporting
confidence: 76%
“…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.…”
Section: Discussionmentioning
confidence: 99%
“…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 ].…”
Section: Introductionmentioning
confidence: 99%