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Rice sheath blight is one of the most important fungal disease‐causing yield losses up to 50%. An Oryza rufipogon‐derived introgression line (Pusa 1908‐13‐12‐5) identified to be highly resistant to sheath blight was used for developing a RIL population by crossing with a highly susceptible rice cultivar, Pusa Basmati 1 (PB 1). The RIL population was genotyped with 80K Rice Pangenome (RPAN) single nucleotide polymorphism (SNP) array and phenotyped for disease response under artificial inoculation for 2 consecutive years. Inclusive composite interval mapping could identify six quantitative trait loci (QTLs) for two traits, namely, relative lesion height and relative yield loss per plant. Among these QTLs, four were major QTLs and three are novel QTLs. Three major QTLs were co‐localised within 1.4 Mb region on chromosome 1. In silico analysis revealed the presence of 12 candidate genes with potential role in disease resistance. Kompetitive Allele Specific PCR (KASP™) assay was developed for the linked SNPs and used for validation in an independent BC1F2:3 population (PB 1121/Pusa 1908‐13‐12‐5). One SNP (AX‐182186537) on chromosome 1 was validated, which offers scope for its utilisation in marker‐assisted breeding. [ABSTRACT FROM AUTHOR]

Increasing crop productivity is one of the prime goals of crop breeding research. Rice grain yield is a complex quantitative trait governed by polygenes. Although several QTLs governing grain yield traits have been reported and limited attempts have been made to map QTLs for grain yield parameters in Basmati rice. A population from the cross Sonasal and Pusa Basmati 1121 comprising 352 RILs was generated through the single seed descent method. A total of 12 QTLs governing yield and yield‐related traits were mapped on six chromosomes, namely, 1, 2, 3, 7, 8 and 9, of which five QTLs were novel. We identified a novel and robust epistatic QTL (qPH1.1 and qPL1.1) governing plant height and panicle length, flanked by the markers RM5336‐RM1 on chromosome 1. The gene encoding brassinosteroid insensitive 1‐associated receptor kinase 1 precursor is the putative candidate gene underlying this epistatic QTL. Another novel QTL, qNT3.1, governing tiller number was bracketed to a region of.77 Mb between the markers RM15247 and RM15281 on chromosome 3. Of the 57 annotated gene models, Os03g0437600 encoding alpha/beta‐fold hydrolase, a homologous to AtKai2 is a putative candidate gene underlying the novel QTL qNT3.1. The other QTLs such as qDFF1.1 governing days to 50% flowering co‐localizes with the gene Ghd7, QTL for plant height qPH1.2 co‐localizes with the gene sd1, the QTLs for panicle length co‐localizes with FUWA and DEP2, the QTL for tiller number co‐localizes with OsRLCK57 and QTLs for thousand‐grain weight co‐localize with the major gene GS3. The QTLs identified in the current study can be effectively used in marker‐assisted selection for developing Basmati rice varieties with a higher yield. [ABSTRACT FROM AUTHOR]

Functional stay‐green (FSG) is characterized by delayed senescence during the reproductive stage in rice. A recombinant inbred line (RIL) population derived from 'Pusa 677'/'PSG16' was used to study the genetics of FSG in a rice mutant, 'PSG16'. The RILs exhibited significant variation under two N regimes for agro‐morphological traits, leaf chlorophyll content, flowering time, yield components and nitrogen (N) use. Using a genome wide linkage map spanning 1910.8 cM with 104 polymorphic markers, we have mapped six major quantitative trait loci (QTLs). One robust QTL on chromosome 1 was associated consistently across different N regimes with chlorophyll content and flowering time. The QTL on chromosome 7 was associated with grain number, whereas the QTL on chromosome 6 was found related to N harvest index and spikelet fertility. Although 'PSG16' showed a clear advantage in grain yield as well as having better N assimilation, we could not establish a direct genetic relationship SG trait and N use. Based on the QTL information, FSG trait of 'PSG16' could be useful in deciphering multiple stress responses in rice. [ABSTRACT FROM AUTHOR]

Reproductive stage drought stress (RSDS) is a major challenge in rice production worldwide. Cultivar development with drought tolerance has been slow due to the lack of precise high throughput phenotyping tools to quantify drought stress-induced effects. Most of the available techniques are based on destructive sampling and do not assess the progress of the plant's response to drought. In this study, we have used state-of-the-art image-based phenotyping in a phenomics platform that offers a controlled environment, non-invasive phenotyping, high accuracy, speed, and continuity. In rice, several quantitative trait loci (QTLs) which govern grain yield under drought determine RSDS tolerance. Among these, qDTY2.1 and qDTY3.1 were used for marker-assisted breeding. A set of 35 near-isogenic lines (NILs), introgressed with these QTLs in the popular variety, Pusa 44 were used to assess the efficiency of image-based phenotyping for RSDS tolerance. NILs offered the most reliable contrast since they differed from Pusa 44 only for the QTLs. Four traits, namely, the projected shoot area (PSA), water use (WU), transpiration rate (TR), and red-green-blue (RGB) and near-infrared (NIR) values were used. Differential temporal responses could be seen under drought, but not under unstressed conditions. NILs showed significant level of RSDS tolerance as compared to Pusa 44. Among the traits, PSA showed strong association with yield (80%) as well as with two drought tolerances indices, stress susceptibility index (SSI) and tolerance index (TOL), establishing its ability in identifying the best drought tolerant NILs. The results revealed that the introgression of QTLs helped minimize the mean WU per unit of biomass per day, suggesting the potential role of these QTLs in improving WU-efficiency (WUE). We identified 11 NILs based on phenomics traits as well as performance under imposed drought in the field. The study emphasizes the use of phenomics traits as selection criteria for RSDS tolerance at an early stage, and is the first report of using phenomics parameters in RSDS selection in rice. [ABSTRACT FROM AUTHOR]

Breeding rice varieties with a low phytic acid (LPA) content is an effective strategy to overcome micronutrient deficiency in a population which consume rice as a staple food. An LPA mutant, Pusa LPA Mutant 11 (PLM11), was identified from an ethyl methane sulfonate (EMS)-induced population of Nagina 22. The present study was carried out to map the loci governing the LPA trait in PLM11 using an F2:3 population derived from a cross between a high phytic acid rice variety, Pusa Basmati 6, with PLM11. The genotyping of the F2 population with 78 polymorphic SSR markers followed by the estimation of phytic acid content in the seeds harvested from 176 F2 plants helped in mapping a major QTL, qLPA8.1, explaining a 22.2% phenotypic variation on Chromosome 8. The QTL was delimited to a 1.96 cM region flanked by the markers RM25 and RM22832. Since there are no previous reports of a QTL/gene governing the LPA content in rice in this region, the QTL qLPA8.1 is a novel QTL. In silico analysis based on the annotated physical map of rice suggested the possible involvement of a locus, Os08g0274775, encoding for a protein similar to a phosphatidylinositol 3- and 4-kinase family member. This needs further validation and fine mapping. Since this QTL is currently specific to PLM11, the linked markers can be utilized for the development of rice varieties with reduced phytic acid (PA) content using PLM11 as the donor, thus enhancing the bioavailability of mineral micronutrients in humans. [ABSTRACT FROM AUTHOR]