INTROGRESSION AND QTL MAPPING OF STEM RUST RESISTANCE GENES USING BI-PARENTAL WHEAT POPULATIONS

Abstract

Rust diseases are a global challenge to wheat production, causing yield losses of up to 50% and even 100% in susceptible host cultivars. A virulent stem rust race named Ug99 (TTKSK) was discovered in Uganda in 1999, and has since then continued to cause a significant threat to world wheat production and food security in turn. Kenya wheat production is particularly at risk as the Kenyan weather makes it a hotspot for stem rust to thrive. Among the best strategies to combating stem rust is host based disease resistance, made even more achievable with the advent of genomics and bioinformatics. This study’s objective was to characterize identified mapping populations for resistance to stem rust and use molecular markers to track introgressed genes as well identify genomic regions potentially harboring resistance genes through QTL mapping. Two bi-parental mapping populations were used, an F2 Robin/Kwale and an F2:5 PBW343/Akuri population. Both populations were evaluated for stem rust resistance in the field in Njoro, Kenya for several seasons. F2 population was evaluated at the F2 and F3 generation. Parental purity and uniformity of the parental genotypes used to make the cross were evaluated using ten SSR markers from the 1A and 6A chromosomes of the wheat genome. These revealed un-uniform banding patterns for both genotypes. Pearson’s Chi square test with co-efficient of infection data fit the 13:3 (at p=0.05) gene ration revealing a dominant and a recessive gene underlying observed resistance. SSR markers gwm533 and xcfd49 were used to track the introgression of genes Sr2 and SrTmp respectively. The parents and one hundred and forty eight lines of the F2:5 recombinant inbred line (RIL) population were evaluated for three seasons under field conditions and genotyped using DArT markers. A frequency distribution of the disease severity data revealed a normal distribution, indicative of underlying quantitative resistance. Linkage mapping was done using Join Map v 4.1 revealing 44 linkage groups and a map spanning 2759.39 cMs with 910 markers. Composite interval mapping was implemented on Windows QTL Cartographer to detect QTLs at an LOD threshold of 2.5 revealing three QTL on 1BL, 2BL and 3B consistent in more than one season, and were designated as QSr.cim-1BL, QSr.cim-2BL, and QSr.cim-3B-. These QTL respectively explained ~7, 9, and 8% of the phenotypic variation. Results from these studies will go a long way in the efforts to enhance utilization of marker assisted selection in combating Ug99 and boost food security.