Multi‐Locus Mixed Model Analysis Of Stem Rust Resistance In Winter Wheat

Abstract

Genome-wide association mapping is a powerful tool for dissecting the relationship between phenotypes and genetic variants in diverse populations. With the improved cost efficiency of high-throughput genotyping platforms, association mapping is a desirable method of mining populations for favorable alleles that hold value for crop improvement. Stem rust, caused by the fungus f. sp. is a devastating disease that threatens wheat ( L.) production worldwide. Here, we explored the genetic basis of stem rust resistance in a global collection of 1411 hexaploid winter wheat accessions genotyped with 5390 single nucleotide polymorphism markers. To facilitate the development of resistant varieties, we characterized marker-trait associations underlying field resistance to North American races and seedling resistance to the races TTKSK (Ug99), TRTTF, TTTTF, and BCCBC. After evaluating several commonly used linear models, a multi-locus mixed model provided the maximum statistical power and improved the identification of loci with direct breeding application. Ten high-confidence resistance loci were identified, including SNP markers linked to and and at least three newly discovered resistance loci that are strong candidates for introgression into modern cultivars. In the present study, we assessed the power of multi-locus association mapping while providing an in-depth analysis for its practical ability to assist breeders with the introgression of rare alleles into elite varieties.

A Genome-Wide Association Study of Field and Seedling Response to Individual Stem Rust Pathogen Races Reveals Combinations of Race-Specific Genes in North American Spring Wheat

“…The haplotype of IWB1190 in combination with Sr6 gene-linked marker IWA2415 also explained only 16.94% of phenotypic variation for race TPMKC seedling response. Moreover, the SNP marker IWA840 (integrated map position 222.71 cM) recently reported to be linked with Sr28 (by Mihalyov et al, 2017 ) is about 20 cM proximal to the QTL-linked marker IWB1190 , and its associations with all four races are not significant in the current work.…”

“…Though several studies have evaluated seedling and/or field response of wheat association mapping panels to Pgt isolates (Yu et al, 2011 , 2012 ; Letta et al, 2014 ; Zhang et al, 2014 ; Gao et al, 2017 ; Mihalyov et al, 2017 ; Muleta et al, 2017 ), only one has evaluated field response of the wheat population studied to more than one Pgt race (Chao et al, 2017 ). In that study, field response of a bulk of six Pgt isolates in Minnesota was compared to response to a bulk of Pgt isolates in Ethiopia, and differences between these datasets could be due to environment and/or race.…”

“…We also detected a moderate QTL conferring resistance against QFCSC on chromosome arm 1BL (integrated map position 114.13 cM) with marker IWB40197 . The SNP marker IWA435 (1B, integrated map position 60.13 cM) identified as a reliable marker to discriminate between Sr31 (1BL.1RS) and Sr1RS Amigo (1AL.1RS) by Mihalyov et al ( 2017 ). We used the data from this marker to postulate the presence of Sr31 (Supplemental Table 8 ).…”

Genome-Wide Association Studies in Diverse Spring Wheat Panel for Stripe, Stem, and Leaf Rust Resistance

“…Genotypes with COI scores of 0 to 20 were considered as resistant, with score ≥ 60 as susceptible and the remaining as moderate ( Table 1). COI was assumed to be suitable for GWAS and as a primary trait for the identification of significant marker-trait associations (MTAs) since it combines both the information from DS and IR for rust response (Yu et al, 2012;Gao et al, 2016Gao et al, , 2017Mihalyov et al, 2017).…”

Association mapping of resistance to emerging stem rust pathogen races in spring wheat using genotyping‐by‐sequencing

“…The extended Bayesian information criterion (EBIC) was used for model selection criteria in the MLMM. Both SLMM and MLMM analyses were included as a form of validation of the results from both methods as has been effectively used in other studies of wheat stem rust resistance association mapping (Edae et al, 2018;Mihalyov, Nichols, Bulli, Rouse, & Pumphrey, 2017). In addition to validation of results from SLMM, the MLMM proved useful for detecting QTL that would not have been detected without taking into account the most significant QTL (Mihalyov et al, 2017).…”