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Not AvailableHigh yielding rice varieties are usually low in grain iron (Fe) and zinc (Zn) content. These two micronutrients are involved in many enzymatic activities, lack of which cause many disorders in human body. Biofortification is a cheaper and easier way to improve the content of these nutrients in rice grain. A population panel was prepared representing all the phenotypic classes for grain Fe-Zn content from 485 germplasm lines. The panel was studied for genetic diversity, population structure and association mapping of grain Fe-Zn content in the milled rice. The population showed linkage disequilibrium showing deviation of Hardy-Weinberg’s expectation for Fe-Zn content in rice. Population structure at K = 3 categorized the panel population into distinct sub-populations corroborating with their grain Fe-Zn content. STRUCTURE analysis revealed a common primary ancestor for each sub-population. Novel quantitative trait loci (QTLs) namely qFe3.3 and qFe7.3 for grain Fe and qZn2.2, qZn8.3 and qZn12.3 for Zn content were detected using association mapping. Four QTLs, namely qFe3.3, qFe7.3, qFe8.1 and qFe12.2 for grain Fe content were detected to be co-localized with qZn3.1, qZn7, qZn8.3 and qZn12.3 QTLs controlling grain Zn content, respectively. Additionally, some Fe-Zn controlling QTLs were co-localized with the yield component QTLs, qTBGW, OsSPL14 and qPN. The QTLs qFe1.1, qFe3.1, qFe5.1, qFe7.1, qFe8.1, qZn6, qZn7 and gRMm9–1 for grain Fe-Zn content reported in earlier studies were validated in this study. Novel QTLs, qFe3.3 and qFe7.3 for grain Fe and qZn2.2, qZn8.3 and qZn12.3 for Zn content were detected for these two traits. Four Fe-Zn controlling QTLs and few yield component QTLs were detected to be colocalized. The QTLs, qFe1.1, qFe3.1, qFe5.1, qFe7.1, qFe8.1, qFe3.3, qFe7.3, qZn6, qZn7, qZn2.2, qZn8.3 and qZn12.3 will be useful for biofortification of the micronutrients. Simultaneous enhancement of Fe-Zn content may be possible with yield component traits in rice.Not Availabl

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Not AvailableWith the objective of identifying SSR markers that can distinguish parental lines of rice hybrids, we characterized 10 each of cytoplasmic male sterile (CMS) and restorer (R) lines along with 10 popular Indian rice varieties using a set of 48 hyperpolymorphic SSRs distributed uniformly across the rice genome. All the SSR markers were polymorphic, amplifying a total of 163 alleles, with an average of 3.36 ± 1.3 allelic variants per locus. Twenty-seven SSR markers showed amplification of an allele, which was very specific and unique to a particular parental line and not amplified in any other rice genotype tested. Through multiplex PCR, SSR marker combinations that were unique to a particular parental line or hybrid were also identified. With a set of 10 SSR markers, all the public bred Indian rice hybrids along with their parental lines could be clearly distinguished. To utilize these SSR markers effectively for detection of impurities in parental lines, a two dimensional bulked DNA sampling strategy involving a 20 × 20 grow-out matrix has been designed and used for detection of contaminants in a seed-lot of the popular CMS line IR58025A. We have also designed a multiplex PCR strategy involving single tube analysis using 2–3 markers for hybrid seed purity assessments and demonstrate its superiority over single marker analysis in accurate detection of impurities in hybrids. Implications of parental and hybrid specific SSR markers and strategies to utilize the informative SSR markers for detection of contaminants in a cost effective manner are discussed.Not Availabl