Profiling of Fusarium head blight resistance QTL haplotypes through molecular markers, genotyping-by-sequencing, and machine learning

Utilization of a publicly available diversity panel in genomic prediction of Fusarium head blight resistance traits in wheat

Fusarium head blight (FHB) is an economically and environmentally concerning disease of wheat (Triticum aestivum L). A two-pronged approach of marker-assisted selection coupled with genomic selection has been suggested when breeding for FHB resistance. A historical dataset comprised of entries in the Southern Uniform Winter Wheat Scab Nursery (SUWWSN) from 2011 to 2021 was partitioned and used in genomic prediction. Two traits were curated from 2011 to 2021 in the SUWWSN: percent Fusarium damaged kernels (FDK) and deoxynivalenol (DON) content. Heritability was estimated for each trait-by-environment combination. A consistent set of check lines was drawn from each year in the SUWWSN, and k-means clustering was performed across environments to assign environments into clusters. Two clusters were identified as FDK and three for DON. Cross-validation on SUWWSN data from 2011 to 2019 indicated no outperforming training population in comparison to the combined dataset. Forward validation for FDK on the SUWWSN 2020 and 2021 data indicated a predictive accuracyr ≈ 0.58 $r \approx 0.58$ andr ≈ 0.53 $r \approx 0.53$ , respectively. Forward validation for DON indicated a predictive accuracy ofr ≈ 0.57 $r \approx 0.57$ andr ≈ 0.45 $r \approx 0.45$ , respectively. Forward validation using environments in cluster one for FDK indicated a predictive accuracy ofr ≈ 0.65 $r \approx 0.65$ andr ≈ 0.60 $r \approx 0.60$ , respectively. Forward validation using environments in cluster one for DON indicated a predictive accuracy ofr ≈ 0.67 $r \approx 0.67$ andr ≈ 0.60 $r \approx 0.60$ , respectively. These results indicated that selecting environments based on check performance may produce higher forward prediction accuracies. This work may be used as a model for utilizing public resources for genomic prediction of FHB resistance traits across public wheat breeding programs.

Genetic analysis of stripe rust resistance in the common wheat line Kfa/2*Kachu under a Chinese rust environment

KEY MESSAGE

We mapped a new race-specific seedling stripe rust resistance gene on wheat chromosome 5BL and a new APR locus QYr.hazu-2BS from CIMMYT wheat line Kfa/2*Kachu. Breeding resistant wheat (Triticum aestivum) varieties is the most economical and efficient way to manage wheat stripe rust, but requires the prior identification of new resistance genes and development of associated molecular markers for marker-assisted selection. To map stripe rust resistance loci in wheat, we used a recombinant inbred line population generated by crossing the stripe rust-resistant parent 'Kfa/2*Kachu' and the susceptible parent 'Apav#1'. We employed genotyping-by-sequencing and bulked segregant RNA sequencing to map a new race-specific seedling stripe rust resistance gene, which we designated YrK, to wheat chromosome arm 5BL. TraesCS5B02G330700 encodes a receptor-like kinase and is a high-confidence candidate gene for YrK based on virus-induced gene silencing results and the significant induction of its expression 24 h after inoculation with wheat stripe rust. To assist breeding, we developed functional molecular markers based on the polymorphic single nucleotide polymorphisms in the coding sequence region of YrK. We also mapped four adult plant resistance (APR) loci to wheat chromosome arms 1BL, 2AS, 2BS and 4AL. Among these APR loci, we determined that QYr.hazu-1BL and QYr.hazu-2AS are allelic to the known pleiotropic resistance gene Lr46/Yr29/Pm39 and the race-specific gene Yr17, respectively. However, QYr.hazu-2BS is likely a new APR locus, for which we converted closely linked SNP polymorphisms into breeder-friendly Kompetitive allele-specific PCR (KASP) markers. In the present study, we provided new stripe rust resistance locus/gene and molecular markers for wheat breeder to develop rust-resistant wheat variety.

Utilizing genomics and historical data to optimize gene pools for new breeding programs: A case study in winter wheat

With the rapid generation and preservation of both genomic and phenotypic information for many genotypes within crops and across locations, emerging breeding programs have a valuable opportunity to leverage these resources to 1) establish the most appropriate genetic foundation at program inception and 2) implement robust genomic prediction platforms that can effectively select future breeding lines. Integrating genomics-enabled¹ breeding into cultivar development can save costs and allow resources to be reallocated towards advanced (i.e., later) stages of field evaluation, which can facilitate an increased number of testing locations and replicates within locations. In this context, a reestablished winter wheat breeding program was used as a case study to understand best practices to leverage and tailor existing genomic and phenotypic resources to determine optimal genetics for a specific target population of environments. First, historical multi-environment phenotype data, representing 1,285 advanced breeding lines, were compiled from multi-institutional testing as part of the SunGrains cooperative and used to produce GGE biplots and PCA for yield. Locations were clustered based on highly correlated line performance among the target population of environments into 22 subsets. For each of the subsets generated, EMMs and BLUPs were calculated using linear models with the ‘lme4’ R package. Second, for each subset, TPs representative of the new SC breeding lines were determined based on genetic relatedness using the ‘STPGA’ R package. Third, for each TP, phenotypic values and SNP data were incorporated into the ‘rrBLUP’ mixed models for generation of GEBVs of YLD, TW, HD and PH. Using a five-fold cross-validation strategy, an average accuracy of r = 0.42 was obtained for yield between all TPs. The validation performed with 58 SC elite breeding lines resulted in an accuracy of r = 0.62 when the TP included complete historical data. Lastly, QTL-by-environment interaction for 18 major effect genes across three geographic regions was examined. Lines harboring major QTL in the absence of disease could potentially underperform (e.g., Fhb1 R-gene), whereas it is advantageous to express a major QTL under biotic pressure (e.g., stripe rust R-gene). This study highlights the importance of genomics-enabled breeding and multi-institutional partnerships to accelerate cultivar development.