Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 1/11/2023
Publication Date: N/A
Technical Abstract: Rice blast, which is caused by the fungus Magnaporthe oryzae, is one of the most destructive diseases of rice worldwide. Major genes, such as Pi-ta, Pi-kh/m/s, Pi-z, and Pi-b have been deployed in US rice varieties for highly effective blast resistance. However, the minor genes and epistatic interactions contributing to broad-spectrum resistance are largely uncharacterized. The objectives of this study were to validate major quantitative trait loci (QTLs) for blast resistance, discover new minor QTLs, and evaluate QTL interactions across a spectrum of blast races, as well as to assess the accuracy of genomic prediction for blast resistance across fungal races. To accomplish this, we used a mapping population consisting of 272 individuals that were derived from the blast resistant variety 'Katy' and the blast susceptible line 'PI312777'. The parent ‘Katy’ contains blast resistance alleles from the landrace variety “Tetep” at the Pi-ta gene complex which includes R-genes Pi-ta, and Ptr, and it contains the blast resistance allele from the tropical japonica variety ‘Newbonnet’ at the Pi-ks gene. This population was genotyped using the Rice C7AIR Illumina Infinium array and 1,578 SNPs were polymorphic in this population. Pathogenicity assays were performed on this population for 11 M. oryzae races: IA-45(75L14), IB-1(YJB1), IB-49(ZN61), IC-17(ZN60), ID-1(ZN42), IE-1(ZN13), IG-1(ZN39), IB-45 (YJ45), IB-54(Y54), IH-1(YJH1), and IE-1k(TM2) under greenhouse conditions. QTL and QTL interactions were identified by inclusive composite interval mapping for additive QTLs (ICIM-ADD) and inclusive composite interval mapping of epistatic QTLs (ICIM-EPI). QTL analysis identified and verified resistance spectra of major genes Pi-ta/Ptr and Pi-ks, and further identified 13 QTLs for 10 of the 11 pathogen races tested and it detected additive-by-additive epistasis for 32 pairs of QTLs. Genomic prediction results revealed that models trained using only blast races IC17, IG1, IA45, or IB45 have high prediction accuracy when used to predict resistance to other blast races, whereas training with blast races other than these four only provided moderate to low prediction accuracy. A genomic selection rank sum index (GSRI) of genomic estimated breeding values (GEBVs) for overall resistance to 11 pathogen races distinctly separated high and low resistant lines for all races except IE1k. Thus, a GSRI may be a useful breeding tool to select for broad-spectrum blast resistance.