A large‐scale genomic association analysis identifies the candidate causal genes conferring stripe rust resistance under multiple field environments

Abstract

The incorporation of resistance genes into wheat commercial varieties is the ideal strategy to combat stripe or yellow rust (YR). In a search for novel resistance genes, we performed a largescale genomic association analysis with high-density 660K single nucleotide polymorphism (SNP) arrays to determine the genetic components of YR resistance in 411 spring wheat lines. Following quality control, 371 972 SNPs were screened, covering over 50% of the highconfidence annotated gene space. Nineteen stable genomic regions harbouring 292 significant SNPs were associated with adult-plant YR resistance across nine environments. Of these, 14 SNPs were localized in the proximity of known loci widely used in breeding. Obvious candidate SNP variants were identified in certain confidence intervals, such as the cloned gene Yr18 and the major locus on chromosome 2BL, despite a large extent of linkage disequilibrium. The number of causal SNP variants was refined using an independent validation panel and consideration of the estimated functional importance of each nucleotide polymorphism. Interestingly, four natural polymorphisms causing amino acid changes in the gene TraesCS2B01G513100 that encodes a serine/threonine protein kinase (STPK) were significantly involved in YR responses. Gene expression and mutation analysis confirmed that STPK played an important role in YR resistance. PCR markers were developed to identify the favourable TraesCS2B01G513100 haplotype for marker-assisted breeding. These results demonstrate that high-resolution SNP-based GWAS enables the rapid identification of putative resistance genes and can be used to improve the efficiency of marker-assisted selection in wheat disease resistance breeding.

Utilization of a Wheat50K SNP Microarray-Derived High-Density Genetic Map for QTL Mapping of Plant Height and Grain Traits in Wheat

“…The difference in physical location was 3 MB. Since subgroup D had a large linkage disequilibrium [72], it was impossible to determine whether these loci were the same one. Qtgw.nwafu-5D.1 controlling TGW and Qgl.nwafu-5D.1 controlling grain length were located in the region from 38.070293 MB-41.294446 MB, neither of which belonged to the same region of the 5D chromosome, compared with the results of previous studies [35,42,43,73].…”

Wheat Stripe Rust Grading by Deep Learning With Attention Mechanism and Images From Mobile Devices

“…In this substudy, part of the wheat plots (with a number of about 200) were used for image collection, where adult-plant resistance evaluations were carried out at Yangling (34°17’ N, 108°04’ E, altitude 519 m) in Shaanxi province during the winter wheat cropping season (2018–2019). More details about plant growth, management, and evaluation time are available in the previous publication ( Wu et al, 2020 ).…”

“…In original experiment design (Wu et al, 2020), a total panel of over 1,500 wheat accessions were used to evaluate stripe rust responses and the wheat lines Avocet S, Mingxian 169, and Xiaoyan 22 were used as the susceptible controls. In this substudy, part of the wheat plots (with a number of about 200) were used for image collection, where adult-plant resistance evaluations were carried out at Yangling (34°17' N, 108°04' E, altitude 519 m) in Shaanxi province during the winter wheat cropping season (2018-2019).…”

“…In this substudy, part of the wheat plots (with a number of about 200) were used for image collection, where adult-plant resistance evaluations were carried out at Yangling (34°17' N, 108°04' E, altitude 519 m) in Shaanxi province during the winter wheat cropping season (2018-2019). More details about plant growth, management, and evaluation time are available in the previous publication (Wu et al, 2020). In this field, a total of 5,242 winter wheat leaf images (adult wheat plants after wheat earing) were collected by different mobile devices (e.g., Huawei Honor 9, Huawei Honor V9, Huawei Honor 10, vivo X20A, vivo X9Plus, Oppo R15x, Oppo A83T, Xiaomi Redmi 2 Pro, iPhone 6) with different imaging distances and angles, which were further divided into 6 levels of disease infection via visual inspection by pathology experts.…”

Genomic Approaches to Identify Molecular Bases of Crop Resistance to Diseases and to Develop Future Breeding Strategies

“…It is intuitively plausible that the information provided by SNPs is most useful when several closely spaced SNPs completely defining haplotypes in the target region are examined, particularly when the causal locus is unknown because they can provide greater QTL detection power and mapping accuracy than single markers. Indeed, examples have been reported about significant associations between haplotypes and phenotypes that were not detectable by a single SNP analysis as well as increase of phenotypic variation explained [ 33 , 36 ]. However, these advantages depend on models relating genotype to phenotype, that is the genetic architecture of the target traits, and demographic scenarios, and thus that power of QTL mapping with haplotypes must be evaluated on a case-by-case basis [ 37 ].…”