Genomic prediction of Fusarium head blight resistance in early stages using advanced breeding lines in hard winter wheat

Fusarium head blight(FHB),also known as scab,is a devastating fungal disease of wheat that causes significant losses in grain yield and quality.Quantitative inheritance and cumbersome phenotyping make FHB resistance a challenging trait for direct selection in wheat breeding.Genomic selection to predict FHB resistance traits has shown promise in several studies.Here,we used univariate and multivariate genomic prediction models to evaluate the prediction accuracy(PA)for different FHB traits using 476elite and advanced breeding lines developed by South Dakota State University hard winter wheat breeding program.These breeding lines were assessed for FHB disease index(DIS),and percentage of Fusarium damaged kernels(FDK)in three FHB nurseries in 2018,2019,and 2020(TP18,TP19,and TP20)and were evaluated as training populations(TP)for genomic prediction(GP)of FHB traits.We observed a moderate PA using univariate models for DIS(0.39 and 0.35)and FDK(0.35 and 0.37)using TP19 and TP20,respectively,while slightly higher PA was observed(0.41 for DIS and 0.38 for FDK)when TP19 and TP20(TP19+20)were combined to leverage the advantage of a large training population.Although GP with multivariate approach including plant height and days to heading as covariates did not significantly improve PA for DIS and FDK over univariate models,PA for DON increased by 20%using DIS,FDK,DTH as covariates using multi-trait model in 2020.Finally,we used TP19,TP20,and TP19+20 in forward prediction to calculate genomic-estimated breeding values(GEBVs)for DIS and FDK in preliminary breeding lines at an early stage of the breeding program.We observed moderate PA of up to 0.59 for DIS and 0.54for FDK,demonstrating the promise in genomic prediction for FHB resistance in earlier stages using advanced lines.Our results suggest GP for expensive FHB traits like DON and FDK can facilitate the rejection of highly susceptible materials at an early stage in a breeding program.

High-Resolution Genome-wide Association Study Identifies Genomic Regions and Candidate Genes for Important Agronomic Traits in Wheat

Wheat(Triticum aestivum)is a major staple food crop worldwide.Genetic dissection of important agronomic traits is essential for continuous improvement of wheat yield to meet the demand of the world's growing population.We conducted a large-scale genome-wide association study(GWAS)using a panel of 768 wheat cultivars that were genotyped with 327609 single-nucleotide polymorphisms generated by genotyping-by-sequencing and detected 395 quantitative trait loci(QTLs)for 12 traits under 7 environments.Among them,273 QTLs were delimited to≤1.0-Mb intervals and 7 of them are either known genes(Rht-D,Vrn-B1,and Vrn-D1)that have been cloned or known QTLs(TaGA2ox8,APO1,TaSus1-7B,and Rht12)that were previously mapped.Eight putative candidate genes were identified for three QTLs that enhance spike seed setting and grain size using gene expression data and were validated in three bi-parental populations.Protein sequence analysis identified 33 putative wheat orthologs that have high identity with rice genes in QTLs affecting similar traits.Large r^2 values for additive effects observed among the QTLs for most traits indicated that the phenotypes of these identified QTLs were highly predictable.Results from this study demonstrated that significantly increasing GWAS population size and marker density greatly improves detection and identification of candidate genes underlying a QTL,solidifying the foundation for large-scale QTL fine mapping,candidate gene validation,and developing functional markers for genomics-based breeding in wheat.