Genomic approaches for mapping and predicting disease resistance in wheat (Triticum aestivum L.) - Lemes Da Silva, Cristiano (Kansas State University, 2018)
Wheat diseases cause significant economic losses every year. To ensure global food security, newly released cultivars must possess increased levels of broadly-effective resistance against wheat pathogens, acceptable end-use quality, and high yield potential. Genetic host resistance stands out from other management strategies as the most viable option for controlling diseases. New genotyping platforms allow whole genome marker discovery at a relatively low cost, favoring the identification of novel loci underlying traits of interest. The work presented here describes genomic approaches for mapping and predicting the resistance to Fusarium head blight (FHB) and wheat rusts. The first study used biparental mapping to identify quantitative trait loci (QTL) associated with Fusarium head blight (FHB) resistance. A doubled haploid population (DH) was originated from a cross of Everest and WB-Cedar, which are widely grown wheat cultivars in Kansas with moderately resistant and moderately susceptible reactions to FHB, respectively. We confirmed that neither of the parents carry known large-effect QTLs, suggesting that FHB resistance is native. Eight small-effect QTLs were identified as associated with multiple mechanisms of FHB resistance. All QTLs had additive effects, providing significant improvements in levels of resistance when they were found in combinations within DH lines. In the second study, a genome-wide association mapping (GWAS) and genomic selection (GS) models were applied for FHB resistance in a panel of 962 elite lines from the K-State Wheat Breeding Program. Significant single nucleotide polymorphisms (SNPs) associated with the percentage of symptomatic spikelets were identified but not reproducible across breeding panels tested in each year. Accuracy of predictions ranged from 0.25 to 0.51 depending on GS model, indicating that it can be a useful tool to increase levels of FHB resistance. GWAS and GS approaches were also applied to a historical dataset to identify loci underlying resistance to leaf and stem rust at seedling stage in a panel of elite winter wheat lines. Infection types of multiple races of wheat rusts from the last sixteen years of the Southern Regional Performance Nursery (SRPN) were used in this study. A total of 533 elite lines originating from several breeding programs were tested in the SRPN during this period of time. GWAS identified significant SNP-trait associations for wheat rusts, confirming the effectiveness of already known genes and revealing potentially novel loci associated with resistance.
Keywords: Wheat breeding; Disease resistance; QTL mapping; Association mapping; Genomic prediction; Genetics