Indian Journal of Biotechnology Vol. 18, January 2019, pp 69-80 Linkage mapping of QTLs for grain minerals (iron and zinc) and physio- morphological traits for development of mineral rich rice ( Oryza sativa L.) Naveen Kumar*, Rajesh, Rajinder Kumar Jain and Vijay Kumar Chowdhury Department of Molecular Biology and Biotechnology, CCS Haryana Agricultural University, Hisar 125004, India Received 15 May 2018; revised 20 December 2018; accepted 23 December 2018 In the present investigation, experiments were conducted to evaluate F 3 and F 4 populations derived from cross between PAU201 (high-yield) and Palman 579 (iron-rich) indica rice varieties for various physio-morphological traits and minerals (iron and zinc) content. Phenotypic correlation analysis showed no correlation between grain iron and zinc content in F 3 and F 4 population (s). A DNA fingerprint database of 33 PAU201 × Palman 579 derived F 4 plants was prepared using 61 polymorphic SSR markers distributed on the entire genome of rice. The results of NTSYS-pc UPGMA tree cluster analysis and two and three dimensional principal component analysis (PCA) scaling showed scattering of the F 4 population between the two distinct parent genotypes; but more inclined towards Palman 579. The SSR data was used to identify quantitative trait loci (QTL) for grain mineral content and physio-morphological traits. A total of 128 alleles and three new recombinant alleles were identified in F 4 plants population. Composite interval mapping (CIM) analysis by WinQTL cartographer 2.5 revealed a total of six QTLs for mineral content (five for iron and one for zinc) in rice grains on chromosome 5, 6, 7, 9 and sixteen QTLs for various physio-morphological traits on chromosomes 2, 5, 7, 8, 9, 10 and 12. Linkage mapping of QTLs of minerals (iron and zinc) can greatly enhance the efficacy of breeding programs to improve mineral density in rice. The QTLs for minerals identified can successfully employed to improve the target traits through marker assisted selection. Key words: Rice, minerals (Fe and Zn) content, SSR markers, CIM, QTLs Introduction Rice (Oryza sativa L.), one of the most important food crops in the world, forms the staple diet of over 50% of the world population. Micronutrient (Fe and Zn) malnutrition is recognized as a massive and rapidly growing public health issue especially among poor people living on an unbalanced diet dominated by a single cereal such as rice. Iron (Fe) and Zinc (Zn) deficiencies cause immune dysfunction and may impair growth and development of individuals. Iron deficiency is one of the most prevalent nutrient deficiencies in the world. As per World Health Organization (WHO) report, iron deficiency anemia affects around 2 billion people in both developed and developing countries 1 . Worldwide, zinc deficiency is responsible for approximately 16% of lower respiratory tract infections, 18% of malaria and 10% of diarrheal disease. In total, 1.4% (0.8 million) of deaths worldwide are attributable to zinc deficiency: 1.4% in males and 1.5% in females 2 . Bio-fortification has the potential to provide coverage for remote rural population, and it inherently targets the poor who consume high levels of staple foods and little else. Molecular markers have proven useful in both basic and applied research, such as DNA fingerprinting, varietal identification and diversity analysis, phylogenetic analysis, linkage mapping of genes/quantitative trait loci (QTL), marker assisted breeding and map based cloning of genes in rice 3-7 . Among the various types of available molecular markers, microsatellites (also known as simple sequence repeats or SSRs) have been used to know about the genetic architecture of complex traits in rice, based on traditional QTL linkage 8 . Using molecular linkage maps, it is possible to estimate the number of QTLs controlling genetic variation for a target trait in a segregating population and to characterize these loci with regard to their map positions on the genome, gene action, phenotypic effects, pleiotropic effect and epistatic interaction between the QTLs 9 . In QTL mapping, genes controlling genetic variation of quantitative traits in segregating populations are resolved into individual Mendelian factors by detecting marker-trait associations 10 . —————— *Author for correspondence: nimbhal@gmail.com