Race Ug99 of the fungus Puccinia graminis tritici that causes stem or black rust disease on wheat was first detected in Uganda in 1998. Seven races belonging to the Ug99 lineage are now known and have spread to various wheat-growing countries in the eastern African highlands, as well as Zimbabwe, South Africa, Sudan, Yemen, and Iran. Because of the susceptibility of 90% of the wheat varieties grown worldwide, the Ug99 group of races was recognized as a major threat to wheat production and food security. Its spread, either wind-mediated or human-aided, to other countries in Africa, Asia, and beyond is evident. Screening in Kenya and Ethiopia has identified a low frequency of resistant wheat varieties and breeding materials. Identification and transfer of new sources of race-specific resistance from various wheat relatives is underway to enhance the diversity of resistance. Although new Ug99-resistant varieties that yield more than current popular varieties are being released and promoted, major efforts are required to displace current Ug99 susceptible varieties with varieties that have diverse race-specific or durable resistance and mitigate the Ug99 threat.

Microsatellite DNA markers are consistently found to be more informative than other classes of markers in hexaploid wheat. The objectives of this research were to develop new primers flanking wheat microsatellites and to position the associated loci on the wheat genome map by genetic linkage mapping in the ITMI W7984 x Opata85 recombinant inbred line (RIL) population and/or by physical mapping with cytogenetic stocks. We observed that the efficiency of marker development could be increased in wheat by creating libraries from sheared rather than enzyme-digested DNA fragments for microsatellite screening, by focusing on microsatellites with the [ATT/TAA]n motif, and by adding an untemplated G-C clamp to the 5'-end of primers. A total of 540 microsatellite-flanking primer pairs were developed, tested, and annotated from random genomic libraries. Primer pairs and associated loci were assigned identifiers prefixed with BARC (the acronym for the USDA-ARS Beltsville Agricultural Research Center) or Xbarc, respectively. A subset of 315 primer sets was used to map 347 loci. One hundred and twenty-five loci were localized by physical mapping alone. Of the 222 loci mapped with the ITMI population, 126 were also physically mapped. Considering all mapped loci, 126, 125, and 96 mapped to the A, B, and D genomes, respectively. Twenty-three of the new loci were positioned in gaps larger than 10 cM in the map based on pre-existing markers, and 14 mapped to the ends of chromosomes. The length of the linkage map was extended by 80.7 cM. Map positions were consistent for 111 of the 126 loci positioned by both genetic and physical mapping. The majority of the 15 discrepancies between genetic and physical mapping involved chromosome group 5.

Because of polyploidy and large genome size, deletion stocks of bread wheat are an ideal material for physically allocating ESTs and genes to small chromosomal regions for targeted mapping. To enhance the utility of deletion stocks for chromosome bin mapping, we characterized a set of 84 deletion lines covering the 21 chromosomes of wheat using 725 microsatellites. We localized these microsatellite loci to 94 breakpoints in a homozygous state (88 distal deletions, 6 interstitial), and 5 in a heterozygous state representing 159 deletion bins. Chromosomes from homoeologous groups 2 and 5 were the best covered (126 and 125 microsatellites, respectively) while the coverage for group 4 was lower (80 microsatellites). We assigned at least one microsatellite in up to 92% of the bins (mean 4.97 SSR/bin). Only a few discrepancies concerning marker order were observed. The cytogenetic maps revealed small genetic distances over large physical regions around the centromeres and large genetic to physical map ratios close to the telomeres. As SSRs are the markers of choice for many genetic and breeding studies, the mapped microsatellite loci will be useful not only for deletion stock verifications but also for allocating associated QTLs to deletion bins where numerous ESTs that could be potential candidate genes are currently assigned.

Climate change has generated unpredictability in the timing and amount of rain, as well as extreme heat and cold spells that have affected grain yields worldwide and threaten food security. Sources of specific adaptation related to drought and heat, as well as associated breeding of genetic traits, will contribute to maintaining grain yields in dry and warm years. Increased crop photosynthesis and biomass have been achieved particularly through disease resistance and healthy leaves. Similarly, sources of drought and heat adaptation through extended photosynthesis and increased biomass would also greatly benefit crop improvement. Wheat landraces have been cultivated for thousands of years under the most extreme environmental conditions. They have also been cultivated in lower input farming systems for which adaptation traits, particularly those that increase the duration of photosynthesis, have been conserved. Landraces are a valuable source of genetic diversity and specific adaptation to local environmental conditions according to their place of origin. Evidence supports the hypothesis that landraces can provide sources of increased biomass and thousand kernel weight, both important traits for adaptation to tolerate drought and heat. Evaluation of wheat landraces stored in gene banks with highly beneficial untapped diversity and sources of stress adaptation, once characterized, should also be used for wheat improvement. Unified development of databases and promotion of data sharing among physiologists, pathologists, wheat quality scientists, national programmes, and breeders will greatly benefit wheat improvement for adaptation to climate change worldwide.

This study examines genomic prediction within 8416 Mexican landrace accessions and 2403 Iranian landrace accessions stored in gene banks. The Mexican and Iranian collections were evaluated in separate field trials, including an optimum environment for several traits, and in two separate environments (drought, D and heat, H) for the highly heritable traits, days to heading (DTH), and days to maturity (DTM). Analyses accounting and not accounting for population structure were performed. Genomic prediction models include genotype × environment interaction (G × E). Two alternative prediction strategies were studied: (1) random cross-validation of the data in 20% training (TRN) and 80% testing (TST) (TRN20-TST80) sets, and (2) two types of core sets, “diversity” and “prediction”, including 10% and 20%, respectively, of the total collections. Accounting for population structure decreased prediction accuracy by 15–20% as compared to prediction accuracy obtained when not accounting for population structure. Accounting for population structure gave prediction accuracies for traits evaluated in one environment for TRN20-TST80 that ranged from 0.407 to 0.677 for Mexican landraces, and from 0.166 to 0.662 for Iranian landraces. Prediction accuracy of the 20% diversity core set was similar to accuracies obtained for TRN20-TST80, ranging from 0.412 to 0.654 for Mexican landraces, and from 0.182 to 0.647 for Iranian landraces. The predictive core set gave similar prediction accuracy as the diversity core set for Mexican collections, but slightly lower for Iranian collections. Prediction accuracy when incorporating G × E for DTH and DTM for Mexican landraces for TRN20-TST80 was around 0.60, which is greater than without the G × E term. For Iranian landraces, accuracies were 0.55 for the G × E model with TRN20-TST80. Results show promising prediction accuracies for potential use in germplasm enhancement and rapid introgression of exotic germplasm into elite materials.