Stem rust is one of the most important diseases of wheat in the world. When single stem rust resistance (Sr) genes are deployed in wheat, they are often rapidly overcome by the pathogen. To this end, we initiated a search for novel sources of resistance in diverse wheat relatives and identified the wild goatgrass species Aegilops sharonesis (Sharon goatgrass) as a rich reservoir of resistance to wheat stem rust. The objectives of this study were to discover and map novel Sr genes in Ae. sharonensis and to explore the possibility of identifying new Sr genes by genome-wide association study (GWAS). We developed two biparental populations between resistant and susceptible accessions of Ae. sharonensis and performed QTL and linkage analysis. In an F6 recombinant inbred line and an F2 population, two genes were identified that mapped to the short arm of chromosome 1Ssh, designated as Sr-1644-1Sh, and the long arm of chromosome 5Ssh, designated as Sr-1644-5Sh. The gene Sr-1644-1Sh confers a high level of resistance to race TTKSK (a member of the Ug99 race group), while the gene Sr-1644-5Sh conditions strong resistance to TRTTF, another widely virulent race found in Yemen. Additionally, GWAS was conducted on 125 diverse Ae. sharonensis accessions for stem rust resistance. The gene Sr-1644-1Sh was detected by GWAS, while Sr-1644-5Sh was not detected, indicating that the effectiveness of GWAS might be affected by marker density, population structure, low allele frequency and other factors.
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In durum wheat (Triticum turgidum subsp. durum), the gene Sr47 derived from Aegilopsspeltoides conditions resistance to race TTKSK (Ug99) of the stem rust pathogen (Puccinia graminis f. sp. tritici). Sr47 is carried on small interstitial translocation chromosomes (Ti2BL-2SL-2BL·2BS) in which the Ae. speltoides chromosome 2S segments are divided into four bins in genetic stocks RWG35, RWG36, and RWG37. Our objective was to physically map molecular markers to bins and to determine if any of the molecular markers would be useful in marker-assisted selection (MAS). Durum cultivar Joppa was used as the recurrent parent to produce three BC2F2 populations. Each BC2F2 plant was genotyped with markers to detect the segment carrying Sr47, and stem rust testing of BC2F3 progeny with race TTKSK confirmed the genotyping. Forty-nine markers from published sources, four new SSR markers, and five new STARP (semi-thermal asymmetric reverse PCR) markers, were evaluated in BC2F2 populations for assignment of markers to bins. Sr47 was mapped to bin 3 along with 13 markers. No markers were assigned to bin 1; however, 7 and 13 markers were assigned to bins 2 and 4, respectively. Markers Xrwgs38a, Xmag1729, Xwmc41, Xtnac3119, Xrwgsnp1, and Xrwgsnp4 were found to be useful for MAS of Sr47. However, STARP markers Xrwgsnp1 and Xrwgsnp4 can be used in gel-free systems, and are the preferred markers for high-throughput MAS. The physical mapping data from this study will also be useful for pyramiding Sr47 with other Sr genes on chromosome 2B.
The unceasing plant-pathogen arms race and ephemeral nature of some rust resistance genes have been challenging for wheat (Triticum aestivum L.) breeding programs and farmers. Hence, it is important to devise strategies for effective evaluation and exploitation of quantitative rust resistance. One promising approach that could accelerate gain from selection for rust resistance is ‘genomic selection’ which utilizes dense genome-wide markers to estimate the breeding values (BVs) for quantitative traits. Our objective was to compare three genomic prediction models including genomic best linear unbiased prediction (GBLUP), GBLUP A that was GBLUP with selected loci as fixed effects and reproducing kernel Hilbert spaces-markers (RKHS-M) with least-squares (LS) approach, RKHS-pedigree (RKHS-P), and RKHS markers and pedigree (RKHS-MP) to determine the BVs for seedling and/or adult plant resistance (APR) to leaf rust (LR), stem rust (SR), and stripe rust (YR). The 333 lines in the 45th IBWSN and the 313 lines in the 46th IBWSN were genotyped using genotyping-by-sequencing and phenotyped in replicated trials. The mean prediction accuracies ranged from 0.31–0.74 for LR seedling, 0.12–0.56 for LR APR, 0.31–0.65 for SR APR, 0.70–0.78 for YR seedling, and 0.34–0.71 for YR APR. For most datasets, the RKHS-MP model gave the highest accuracies, while LS gave the lowest. GBLUP, GBLUP A, RKHS-M, and RKHS-P models gave similar accuracies. Using genome-wide marker-based models resulted in an average of 42% increase in accuracy over LS. We conclude that GS is a promising approach for improvement of quantitative rust resistance and can be implemented in the breeding pipeline.