An advanced backcross population consisting of 80 BC(3)F(3) lines derived from rice vars. Vandana/ Moroberekan was analysed for blast resistance and genotyped with 50 candidate genes and 23 simple sequence repeat (SSR) markers. Six candidate defence response genes [thaumatin, three nucleotide-binding site-leucine-rich repeat sequences from maize and two resistance gene analogue (RGA) markers] and one SSR marker (RM21) were significantly associated with partial blast resistance in rice ( P=0.01). These markers accounted for phenotypic variation ranging from 9.6% to 29.4% and contributed to 76% of the total variation of percentage diseased leaf area (DLA) observed under natural infection. Four candidate genes (oxalate oxidase, 14-3-3 protein and two RGA markers) and four SSR markers (RM21, RM168, RM215 and RM250) were significantly associated with resistance to a single pathogen isolate, PO6-6. Among these, two markers were for DLA, five for lesion number and one for lesion size. These markers accounted for 9.1-28.7% of the phenotypic variation. A moderate correlation ( r=0.48, P<0.01) was found between the level of partial resistance measured in the greenhouse and that measured under natural conditions. Analysis of BC(3)F(4) progeny using genotypes of BC(3)F(3) confirmed the phenotypic contribution of these markers. Cluster analysis of DNA profiles showed that the BC(3) population was genetically similar (>85%) to the recurrent parent Vandana. Although no obvious relationship between DNA profiles and resistant phenotypes was observed, three lines (VM19, VM46 and VM76) in a cluster with high similarity to Vandana (89-96%) expressed a high level of partial blast resistance in the field. Analysis of disease progress in the field confirmed the performance of selected lines based on greenhouse and nursery analyses. The advanced backcross progeny with resistance phenotypes tagged by markers will be useful for accumulating blast resistance in upland rice.

novel, and broad-spectrum BB resistance genes from wild species of Oryza and also presents a perspective on potential strategies to achieve the goal of sustainable disease management.

the study was undertaken to identify the quantitative trait loci (QtLs) governing yield and its related traits using a recombinant inbred line (RIL) population derived from the popular rice hybrid, KRH-2 (IR58025A/KMR3R). A genetic map spanning 294.2 cM was constructed with 126 simple sequence repeats (SSR) loci uniformly distributed across the rice genome. QTL analysis using phenotyping and genotyping information identified a total of 22 QTLs. Of these, five major effect QTLs were identified for the following traits: total grain yield/plant (qYLD3-1), panicle weight (qPW3-1), plant height (qPH12-1), flag leaf width (qFLW4-1) and panicle length (qPL3-1), explaining 20.23-22.76% of the phenotypic variance with LOD scores range of 6.5-10.59. Few genomic regions controlling several traits (QTL hotspot) were identified on chromosome 3 for total grain yield/plant (qYLD3-1) and panicle length (qPL3-1). Significant epistatic interactions were also observed for total grain yield per plant (YLD) and panicle length (PL). While most of these QTLs were observed to be co-localized with the previously reported QTL regions, a novel, major QTL associated with panicle length (qPL3-1) was also identified. SNP genotyping of selected high and low yielding RILs and their QTL mapping with 1,082 SNPs validated most of the QTLs identified through SSR genotyping. This facilitated the identification of novel major effect QTLs with much better resolution and precision. In-silico analysis of novel QTLs revealed the biological functions of the putative candidate gene (s) associated with selected traits. Most of the high-yielding RILs possessing the major yield related QTLs were identified to be complete restorers, indicating their possible utilization in development of superior rice hybrids.

No abstract

Significant increase in prevalence of hypertension during winter compared to summer indicates need for considering this factor while comparing prevalence reported in different studies as well as interpreting the surveillance data based on repeat surveys.