Reliable DNA Markers for a Previously Unidentified, Yet Broadly Deployed Hessian Fly Resistance Gene on Chromosome 6B in Pacific Northwest Spring Wheat Varieties.

Hessian fly [Mayetiola destructor (Say)] is a major pest of wheat (Triticum aestivum L.) throughout the United States and in several other countries. A highly effective and economically feasible way to control Hessian fly is with resistant cultivars. To date, over 37 Hessian fly resistance genes have been discovered and their approximate locations mapped. Resistance breeding is still limited, though, by the genes' effectiveness against predominant Hessian fly biotypes in a given production area, genetic markers that are developed for low-throughput marker systems, poorly adapted donor germplasm, and/or the inadequacy of closely linked DNA markers to track effective resistance genes in diverse genetic backgrounds. The purposes of this study were to determine the location of the Hessian fly resistance gene in the cultivar "Kelse" (PI 653842) and to develop and validate Kompetitive Allele Specific PCR (KASP) markers for the resistance locus. A mapping population was genotyped and screened for Hessian fly resistance. The resulting linkage map created from 2,089 Single Nucleotide Polymorphism SNP markers placed the resistance locus on the chromosome 6B short arm, near where H34 has been reported. Three flanking SNPs near the resistance locus were converted to KASP assays which were then validated by fine-mapping and testing a large panel of breeding lines from hard and soft wheat germplasm adapted to the Pacific Northwest. The KASP markers presented here are tightly linked to the resistance locus and can be used for marker-assisted selection by breeders working on Hessian fly resistance and allow confirmation of this Hessian fly resistance gene in diverse germplasm.

Development of the Wheat Practical Haplotype Graph database as a resource for genotyping data storage and genotype imputation.

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.