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Marker assisted backcross breeding was employed to incorporate the blast resistance genes, Pi2 and Pi54 and bacterial blight (BB) resistance genes xa13 and Xa21 into the genetic background of Pusa Basmati 1121 (PB1121) and Pusa Basmati 6. Foreground selection for target gene(s) was followed by arduous phenotypic and background selection which fast-tracked the recovery of recurrent parent genome (RPG) to an extent of 95.8% in one of the near-isogenic lines (NILs) namely, Pusa 1728-23-33-31-56, which also showed high degree of resemblance to recurrent parent, PB6 in phenotype. The phenotypic selection prior to background selection provided an additional opportunity for identifying the novel recombinants viz., Pusa 1884-9-12-14 and Pusa 1884-3-9-175, superior to parental lines in terms of early maturity, higher yield and improved quality parameters. There was no significant difference between the RPG recovery estimated based on SSR or SNP markers, however, the panel of SNPs markers was considered as the better choice for background selection as it provided better genome coverage and included SNPs in the genic regions. Multi-location evaluation of NILs depicted their stable and high mean performance in comparison to the respective recurrent parents. The Pi2+Pi54 carrying NILs were effective in combating a pan-India panel of Magnaporthe oryzae isolates with high level of field resistance in northern, eastern and southern parts of India. Alongside, the PB1121-NILs and PB6-NILs carrying BB resistance genes xa13+Xa21 were resistant against Xanthomonas oryzae pv. oryzae races of north-western, southern and eastern parts of the country. Three of NILs developed in this study, have been promoted to final stage of testing during the ​Kharif 2015 in the Indian National Basmati Trial.

BackgroundRice is staple food for more than half of the world’s population including two billion Asians, who obtain 60-70% of their energy intake from rice and its derivatives. To meet the growing demand from human population, rice varieties with higher yield potential and greater yield stability need to be developed. The favourable alleles for yield and yield contributing traits are distributed among two subspecies i.e., indica and japonica of cultivated rice (Oryza sativa L.). Identification of novel favourable alleles in indica/japonica will pave way to marker-assisted mobilization of these alleles in to a genetic background to break genetic barriers to yield.ResultsA new plant type (NPT) based mapping population of 310 recombinant inbred lines (RILs) was used to map novel genomic regions and QTL hotspots influencing yield and eleven yield component traits. We identified major quantitative trait loci (QTLs) for days to 50% flowering (R2 = 25%, LOD = 14.3), panicles per plant (R2 = 19%, LOD = 9.74), flag leaf length (R2 = 22%, LOD = 3.05), flag leaf width (R2 = 53%, LOD = 46.5), spikelets per panicle (R2 = 16%, LOD = 13.8), filled grains per panicle (R2 = 22%, LOD = 15.3), percent spikelet sterility (R2 = 18%, LOD = 14.24), thousand grain weight (R2 = 25%, LOD = 12.9) and spikelet setting density (R2 = 23%, LOD = 15) expressing over two or more locations by using composite interval mapping. The phenotypic variation (R2) ranged from 8 to 53% for eleven QTLs expressing across all three locations. 19 novel QTLs were contributed by the NPT parent, Pusa1266. 15 QTL hotpots on eight chromosomes were identified for the correlated traits. Six epistatic QTLs effecting five traits at two locations were identified. A marker interval (RM3276-RM5709) on chromosome 4 harboring major QTLs for four traits was identified.ConclusionsThe present study reveals that favourable alleles for yield and yield contributing traits were distributed among two subspecies of rice and QTLs were co-localized in different genomic regions. QTL hotspots will be useful for understanding the common genetic control mechanism of the co-localized traits and selection for beneficial allele at these loci will result in a cumulative increase in yield due to the integrative positive effect of various QTLs. The information generated in the present study will be useful to fine map and to identify the genes underlying major robust QTLs and to transfer all favourable QTLs to one genetic background to break genetic barriers to yield for sustained food security.

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