A wheat genotype PBW343+Gpc-B1+LR24 containing the high grain protein content (GPC) gene Gpc-B1 linked to marker Xucw108 was used as the donor parent to transfer Gpc-B1 and Lr24 into Eastern Gangetic Plains (EGP) cv. HUW234 and HUW468 that were released in 1986 and 1999, respectively. The backcrossing program involved the following steps: (i) foreground selection, (ii) marker selection, and (iii) recovery of recipient parent genome. Grain protein contents were recorded for all selected plants from the BC2F2:3 generation. The dominant marker Xucw108 was used for foreground selection, and heterozygous plants were identified through progeny testing. For RPG recovery, both genotypic and phenotypic selection was used. Introgression of the high GPC gene into the recipient background without yield loss was completed in 5 years, starting from 2009-10 (F1) and completed in 2013-14 (BC2F5). A conventional selection program would take the same time to reach BC2F5 but ensuring the transfer of GPC would not not be possible. Ten selected single plants from the BC2F3:4 generation had comparable yields of the parents with 26% higher GPC than the recurrent parent HUW 234. Eight selected plants had comparable yields and 34% higher GPC than HUW 468. Multi-row progenies (BC2F4 and BC2F5) of each selected plant were evaluated in yield traits with the donor and recipient parents as controls during 2012-13 and 2013-14. Two lines based on each recurrent parent were identified with significantly higher GPC with no yield penalty. The study reinforced the belief that MAS in combination with phenotypic selection could be a useful strategy to develop high GPC genotypes without sacrificing grain yield. These lines will be submitted to national trial where MAS derived lines require only two years of testing compared to four years for conventionally bred lines.
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Stripe or yellow rust is a constraint to wheat production on about 12.8 m ha in the Northern Hills and North Western regions of India. Varieties resistant at the time of release become susceptible usually within a few years due to new pathogen races. The present study conducted in 2013-14 was undertaken to identify slow stripe rusting genotypes among a panel of 192 advanced breeding lines and popular cultivars. All genotypes were planted in two replications and a susceptible control was planted after every 20 plots. The nursery, grown at Karnal, was inoculated with a mixture of prevalent Pst races 78S84 and 46S119. Genotypes were categorized into distinct groups based on area under disease progress curve (AUDPC) values, viz. 22 lines with AUDPC values 1-100, 18 lines with values 101-200, 43 lines with values 201-500, and remaining lines with higher values. Apart from rust-free lines assumed to carry all-stage resistance genes, lines with AUDPC values of less than 500 and having AUDPC values <20% of those of the susceptible check (maximum AUDPC value, 2500) were considered to be slow rusting. Some of the popular cultivars (HS 507, HS 542, WH 1105, HD 3086, DPW 621-50, HD 3059) currently grown in northern India showed slow rusting. The information generated can be utilized in improving the levels of stripe rust resistance in current cultivars.
Leaf rust of wheat causes considerable losses worldwide. New pathotypes may cause previously resistant varieties to become susceptible. Identification of pathotypes and their relationships provide information for breeding efforts and designing management strategies. Traditional identification of pathotypes is based on responses of differential hosts. At present 50 pathotypes of P. triticina are maintained in the National collection. To determine variability and relationships at the molecular level we conducted analyses with 26 SSR primers, eight of which were polymorphic. Binary (0 or 1) molecular data generated by NTSYS-pc was used to construct a phylogenetic tree. Cluster analysis was done using the unweighted pair group arithmetic means (UPGMA) method in the SAHN program of NTSYS-pc. Pathotype groups and subgroups were determined based on the Jaccard similarity coefficients (JC). Manual observations indicated seven major groups. Among them, two groups each have one pathotype (pathotypes 16 and 17). Jaccard similarity coefficients supported groupings based on pathogenicity data. For example, pathotypes in the race 12 group (12, 12-1, 12-3, 12-4, 12-6, 12-7, 12-8, but excluding 12-2 and 12-5) had similarity coefficients greater than 0.7. Similar observations were recorded for the race 77 group. Maximum similarity was observed between 12-3 and 12-7 (JC value: 0.89) followed by 12-3, 12-7 and 12-6 (JC value: 0.82). Based on the phylogenetic tree and similarity coefficients data, there was substantial diversity among pathotypes. Thus SSR marker data can be used for effective characterization of pathotypes and for making evolutionary inferences.