Key message

A novel QTL, Q.DB.ui-7DS, and the PCR-based markers identified in the current study will accelerate variety development for resistance to dwarf and common bunt of wheat. Dwarf bunt [Tilletia controversa J.G. Kühn [as 'contraversa'], in Rabenhorst, Hedwigia 13: 188 (1874)] is a destructive disease of wheat (Triticum aestivum L.) that reduces grain yield and quality. A number of distinct genes conferring resistance to dwarf bunt have been used by breeding programs for nearly 100 years. However, few markers were identified that can be used in selection of dwarf bunt resistance. A recombinant inbred line (RIL) population derived from the bunt-resistant germplasm, Idaho 444 (IDO444), and the susceptible cultivar, Rio Blanco, was evaluated for phenotypic reaction to dwarf bunt inoculation in four trials in two locations (USU and USDA) over 3 years. The population was genotyped with the Diversity Arrays Technology (DArT) and the Illumina Infinium 9K iSelect marker platforms. A total of three QTL were detected, and resistant alleles were from IDO444. QTL Q.DB.ui-7DS on 7DS was determined based on the location of a DArT marker wPt-2565 (X116197), which was consistently detected and explained 32 to 56 % of phenotypic variation among the four trials. QTL Q.DB.ui-1A on 1A was detected in three Utah State University (USU) trials and explained 11-15 % of phenotypic variation. QTL Q.DB.ui-2B on 2B was detected in two USU and one United States Department of Agriculture (USDA) trials and explained up to 6 % of phenotypic variation. Two PCR-based markers were developed based on the sequence of wPt-2565 and validated in the RIL population and used in genotyping of dwarf bunt differential lines, known resistance sources, and resistant cultivars.

To improve the efficiency of high-density genotype data storage and imputation in bread wheat (Triticum aestivum L.), we applied the Practical Haplotype Graph (PHG) tool. The Wheat PHG database was built using whole-exome capture sequencing data from a diverse set of 65 wheat accessions. Population haplotypes were inferred for the reference genome intervals defined by the boundaries of the high-quality gene models. Missing genotypes in the inference panels, composed of wheat cultivars or recombinant inbred lines genotyped by exome capture, genotyping-by-sequencing (GBS), or whole-genome skim-seq sequencing approaches, were imputed using the Wheat PHG database. Though imputation accuracy varied depending on the method of sequencing and coverage depth, we found 92% imputation accuracy with 0.01× sequence coverage, which was slightly lower than the accuracy obtained using the 0.5× sequence coverage (96.6%). Compared to Beagle, on average, PHG imputation was ∼3.5% (P-value < 2 × 10-14) more accurate, and showed 27% higher accuracy at imputing a rare haplotype introgressed from a wild relative into wheat. We found reduced accuracy of imputation with independent 2× GBS data (88.6%), which increases to 89.2% with the inclusion of parental haplotypes in the database. The accuracy reduction with GBS is likely associated with the small overlap between GBS markers and the exome capture dataset, which was used for constructing PHG. The highest imputation accuracy was obtained with exome capture for the wheat D genome, which also showed the highest levels of linkage disequilibrium and proportion of identity-by-descent regions among accessions in the PHG database. We demonstrate that genetic mapping based on genotypes imputed using PHG identifies SNPs with a broader range of effect sizes that together explain a higher proportion of genetic variance for heading date and meiotic crossover rate compared to previous studies.