Full Text: 1 Introduction Rice is the world's most important crop, and more than half of the global population is dependent on rice (Rao et al., 2020). Rice is the chief source of carbohydrate and is used as an integral part of a balanced diet for billions of people in the world (Das et al., 2020). So it is a crop of global significance. In the Indian context, rice has deeply embedded itself in Indian culture. Although the country has attained self-sufficiency in rice production (Ghose et al., 2013) but nutritional enrichment of rice with enhanced grain quality, without compromise on yield and consumer acceptance is a new challenge ahead for the plant breeders. Even though rice is rich in starch (80%) but it is deficient in major micronutrients such as zinc and iron. Zinc (Zn) is one of the essential micronutrients for human health which plays an important role in the normal functioning of the immune system, reproductive growth, nervous system development and behavior (Hotz & Brown, 2004). Iron (Fe) and Zinc (Zn) deficiencies are among the most prevalent micronutrient deficiencies in humans and one-third of the world’s population are at the risk of zinc deficiency (HarvestPlus, 2014). Dietary deficiency of iron and zinc is identified as a major cause of malnutrition (Bouis & Saltzman, 2017). Micronutrient deficiencies have raised health risk in developing countries and also its existing spectrum has expanded to those strata of society who is dependent on the unbalanced diet of single staple food rice hence impacting global public health (Virk & Barry, 2009). Biofortification is the process of enriching food grains for nutritive elements in crop plants using genetic approaches (Pradhan et al., 2020a). The biofortification strategy implemented with the aids of breeding will help to address the problem of micronutrient malnutrition in a more sustainable way (Bouis & Welch, 2010). The precision breeding approach is recommended for nutritional enhancement of high yielding varieties with major micronutrients which includes the use of potential donors, identification of QTLs for high grain mineral content, and identification of robust markers (Pradhan et al., 2020b). Dissecting genomic complexity by mapping quantitative trait loci (QTLs) for grain quality traits, for their deployment in marker-assisted selection and gene discovery is an effective forward genetic approach (Swamy et al., 2012). To design an effective breeding strategy of rice biofortification, identification of genomic regions associated with grain iron and zinc content is essential (Bollinedi et al., 2020). Improvement of any trait in rice has always benefited to a sizable proportion of the population. In this context, plant breeders can play a major role in screening genetic variability in the trace mineral concentration, identification of genetic basis and genomic region of complex polygenic traits, perform target trait mapping and marker-assisted selection for the improvement of grain quality and nutritional traits. SSRs marker always remains the choice of plant breeders, due to their abundance, genome coverage, low analysis cost, co-dominant nature, high reproducibility, high level of polymorphism and their high utility in marker assisted selection. Although there are various markers systems available in rice, microsatellites or simple sequence repeats (SSRs) have high polymorphism and are technically simple for detection hence are widely used (McCouch et al., 2002). SSRs have been used widely in the majority of crop species including rice for which sequence information is available. These are mainly useful for gene tagging, genetic diversity studies, genotyping of mapping population, linkage map preparation, tracing marker-trait association, single-marker analysis and all types of QTL mapping analysis. Generally, there are two types of SSRs viz Class I and Class II. Class I SSRs also called hypervariable markers have longer repeats of ≥ 20 nucleotides in length hence are more polymorphic than Class II SSRs (Cho et al., 2000; Temnykh et al., 2000). Parental polymorphism survey is a prerequisite for every QTL mapping experiment. Hence the present study was conducted to screen genome wide distributed large number of SSR markers between two parental genotypes of rice viz Rajendrakasturi and URG-30. The study was aimed to identify the polymorphic markers set for allelic variation between both parents, to study their distribution across the chromosomes and identify repeat motif.  Based on these, a subset of SSR will be selected and utilized for performing QTL mapping in mapping population developed from the above cross between these two parents for various traits of interest like nutritional trait involving high grain zinc content, high grain iron content; grain quality traits and yield trait. As per phenotypic evaluation of parents a higher amount of variation was observed for enough traits like yield, yield-related traits, quality and nutritional traits. So, there is huge scope for QTL mapping analysis for the various number of traits in mapping population developed from the cross of both the parents. 2 Materials and Methods 2.1 Selection of Parents for Study The two parents viz Rajendrakasturi and URG-30 were selected for this study, among these  Rajendrakasturi is short grain aromatic variety which developed from Kasturi × Sungadha parentage by Dr. Rajendra Prasad Central Agricultural University, Pusa, Bihar. This was selected for its quality traits while URG-30 is a genotype from Eastern Uttar Pradesh collected by the Institute of Agricultural Sciences, Banaras Hindu University, Varanasi as local collection material and identified and considered by the Harvest Plus project as stable high zinc donor with high grain iron content. Both selected parents have contrasting features for desirable grain quality and nutritional traits. Details of all the traits of both the parents have been mentioned in table 1. The mapping population was developed by crossing both parents and advancing they confirm F₁ plant to raise F₂ population. 2.2 Field trial and leaf sample collection Present experiment was conducted during Rabi-2018 at IRRI South Asia Hub, ICRISAT, Hyderabad. Both parents viz Rajendrakasturi and URG-30 were raised by following all recommended package of practices of the region. Leaf samples of both the parents were collected from 21 days old seedlings which were transplanted in the field later for evaluation. Collected leaf samples were preserved at -20ºC up to further use. Both parents were evaluated for morphological and yield-related traits. Collected leaf samples were used for DNA isolation for the present study. 2.3 DNA isolation Parental genomic DNA isolation was done by a quick DNA extraction method (TPS Buffer) adopted from Dr. Yohie Koide for IRRI-JAPAN Collaborative Research Project and modified from Lenie A. Quiatchon (Kim et al., 2016).  Micro Tubes of 1.1 ml were added with fresh leaf samples (around 100 mg), two steel beads and 600µl of TPS buffer (100mM Tris HCl pH 8.0, 10mM EDTA, 1M KCl, nano pure water for volume makeup). Micro Racks sealed properly and subjected to Geno Grinder with speed 900 rpm set for 3 min. (repeat this 3-4 times) and incubated in a water bath at 55ºC for 30 min. Then samples were centrifuged for 30 min, at 3000 rpm and the supernatant was collected in a fresh tube (96 Eppendorf deep well storage microplate). Chilled isopropanol @ 300 µl was added to each tube and kept in the refrigerator at -20ºC for overnight. DNA pellet was collected by centrifuge at 3000 rpm for 30 min at 4ºC and washed with 160µl 70% ethanol and centrifuge at 2000 rpm for 20 min. Pouring off ethanol, pellets were allowed to dry up completely and rehydrated with 100 µl of 1X TE buffer (1M Tris pH-8.0, 0.5M EDTA pH- 8.0). 2.4 Quantification of Genomic DNA and Agarose gel electrophoresis. The quality of DNA was evaluated by running it on 0.8% agarose gel along with a standard ladder and compared with band intensity and thickness. 0.8% Agarose (800 mg) was added to 100 ml of 1X TAE buffer melted till it became transparent. The solution was cooled to about 50°C, added with 6 µl of ethidium bromide (0.5 µg  mL^-1), and poured into the gel casting unit. 2µl of DNA samples added with gel loading dye were electrophoresed (GeNei horizontal mini gel unit) in agarose gel at 60 Volts in TAE buffer for 1 hr. After electrophoresis, the band intensity of genomic DNA was visualized on the Gel documentation unit (SYNGENE GBox, UK) (Yerva et al., 2018).  These gels provided a visual measure of purity and integrity of DNA. 2.5 Use of Rice Microsatellite Markers for the parental polymorphism survey A total of 1013 microsatellite markers (IDT^®) covering the entire length of each of 12 chromosomes of rice were used for this study. In this study, it is ensured that marker distribution will be uniform with whole-genome coverage across all chromosomes. The details about McCouch Locus_ID, physical position (SSR start and SSR end), number of repeat motif, forward and reverse primer sequences, chromosome number, etc., were obtained from the Gramene markers database (https://www.gramene.org/). 2.6 Use of RAP-DB For those markers whose information was not available in the Gramene database, it was obtained from RAP-DB (The Rice Annotation Project DataBase) by using its BLAST tool and submitting query sequence in FASTA format to run a search against DNA database (blastn) (https://rapdb.dna.affrc.go.jp/tools/blast). 2.7 PCR amplification of genomic DNA using SSR markers DNA amplification using SSR markers was carried out with Polymerase chain reaction in Eppendorf nexus gradient master cycler. PCR reaction was done in 0.2ml 96 well PCR plates (Watson 137-174C) by adding 1µl Taq buffer [MgCl₂: 100mM Tris ( pH 9.0) 500mM KCl, 15mM MgCl_2, and 0.1% gelatin], forward and reverse primer each 0.5µl (10 picomoles each), 3µl of genomic DNA, 0.5µl of dNTPs (2.5mM), 0.1µl Taq DNA polymerase 3U/µl (Bangalore GeNei Laboratories Private Limited) and 4.4µl of sterilized distilled water to make a final volume of 10µl for each reaction (Yerva et al., 2018). PCR plate kept in a thermal cycler and the reaction was set as 1^st cycle initial denaturation at 94°C for 5 min, followed by 35 cycles of denaturation at 94°C for 30 seconds, annealing at 58ºC (touch down -0.1ºC/cycle) for 30 seconds and primer extension at 72°C for 1 minute. A final extension was given at 72°C for 5 min at the end of the cycle and samples were held at 4°C till retrieval. PCR product was analyzed on 3% agarose gel added with Ethidium bromide and standard DNA ladders of 50 bp and 100 bp was then visualized under the Gel Doc system. Band pattern was observed and polymorphic markers were recorded. 2.8 Use of GGT 2.0 (software for display of Graphical Genotypes) Graphical representation of marker data was obtained with GGT 2.0 software (Van Berloo, 2008). GGT 2.0 deals with visualization and analyses that involve molecular marker scores (Van Berloo, 2008). Based on inputs of physical positions of markers given in row and column data matrix, visualization of the distribution of polymorphic markers across the length of chromosome according to their physical positions (Mb) was obtained by GGT represented in figure 1 (Van Berloo, 2007; Van Berloo, 2008). 3 Results and Discussion In the present investigation, only those SSRs which showed clear polymorphism in terms of scorable band difference were considered (Figure 4). Out of 1013 SSR markers screened, 294 SSRs were polymorphic indicating overall polymorphism of 29.02% between the two parents used for the study. At the individual chromosome level, the highest polymorphism was observed with chromosome 4 (40.96%) followed by chromosome 6 (37.8%) whereas the lowest polymorphism was observed in chromosome 9 (16%). Details of polymorphism percentage of individual chromosomes are mentioned in table 2. Further 38 polymorphic markers were present on chromosome 1, followed by 34 polymorphic markers on chromosome 2 and 4 individually, the least number of polymorphic markers (12) were obtained for chromosome 12. A detailed list of all the polymorphic markers obtained on each chromosome is given in Table 3. Kumar et al. (2013) reported 31% polymorphism with 128 HRM primers which were screened between JGL 1798 and donor parents GPP 2 and NLR 145. Parental polymorphism survey between popular rice varieties of Andaman & Nicobar Islands viz C14-8 and CARI Dhan 5 with donor IRBB 60 showed 36 (18% polymorphism) and 48 (24% polymorphism) polymorphic markers out of 200 SSR markers screened and the highest polymorphism was of 53% reported for chromosome 4 (Gautam et al.,  2015). Parental polymorphism survey between the two parents (N22 and Uma) revealed 20.82 percent polymorphism with 41 polymorphic markers out of 197 markers screened (Waghmare et al., 2018). Another study using parental lines PR122 and IR10M196  surveyed 647 SSR markers and found 108 polymorphic markers with 16.69% of polymorphism level between parents (Yerva et al., 2018). A polymorphism survey conducted by Challa & Kole (2019) using drought contrasting parents reported overall polymorphism of 13.17% in which the highest polymorphism was noticed in chromosome 5 (17.02%) and the lowest was noticed in chromosome 10 (5.36%). Parental polymorphism in iron and zinc rich rice varieties 52 polymorphic markers were obtained out of 171 total screened rice microsatellite markers (Shivani et al., 2020). In another study by Vishalakshi et al. (2020) also assess the polymorphism and reported that, out of 500 markers screened between Varalu and Vandana NIL, 150 polymorphic markers was reported which expressed 30% polymorphism. Out of this highest polymorphism was recorded for chromosome 12 (42.5%) and least for chromosome 8 (20%).  The necessity of combining different genetic backgrounds for QTL mapping experiment for grain zinc and iron content has been also highlighted by Lee et al. (2020). A higher level                      of polymorphism in the present study is a result of the diverse   background of the two selected parents. The selection of genotypically diverse parents is imperative for any QTL mapping program. The geographical diverse origin of parents reflects in genotypic variations in terms of polymorphism. Mutual relation between the geographic distribution of germplasm and polymorphism of molecular markers has been also noticed by He et al. (2004). Also, a significant correlation between genetic relationship and geographical place of indica local rice varieties has been indicated (Zhang, 2005). As SSR based polymorphism is characterized by repeat motifs (di, tri, tetra); in the current study also it was observed that SSR repeat motifs with dinucleotide repeats were more polymorphic than others. Out of 294 polymorphic markers obtained from screening 1013 SSRs, 1 marker possesses a single nucleotide repeat, 182 contained dinucleotide repeats, 87 showed trinucleotide repeats and 23 were having tetranucleotide repeats. Out of the 182, dinucleotide repeats AG motif appeared more polymorphic than others, which constitutes 49.45% of dinucleotide repeats, followed by AT repeats (21.97%). The abundance of (AG)_n and (AT)_n dinucleotide repeat has been noticed by earlier studies (Grover et al., 2007; Kumar et al., 2013). Out of 87 trinucleotides, AAG repeated 23 times representing 26.43% of its total, followed by AAT 12 times (13.79%). It was also observed that GC rich trinucleotide repeats viz., ACG, AGC, CGG, CCG are at a variable frequency (Figure 2). The occurrence of 80% trinucleotide GC rich repeats in predicted exons and their frequent association with genes (or ESTs) and (AC)n/(AT)n dinucleotides and tetranucleotide repeat motif in the non-coding intergenomic region has been reported (Temnykh et al., 2001). Out of 23 tetranucleotide repeats, AGAT appeared 6 times (26.08%).  In a comparative study across the genome of two subspecies of Oryza sativa viz ssp. indica and ssp. japonica abundance of (AGAT)_n repeats has been observed (Grover et al., 2007). In the present study tetra nucleotides were found to be frequent for chromosomes 5 and 9 and least frequent in chromosome 8, 11, 12, and 4. Tri nucleotides were found to be highest for chromosome 4 and less frequent in chromosomes 9, 12, and 7 (Figure.3). Similarly long trinucleotides repeats have been reported on chromosome 1, 4, and 12 (Grover et al., 2007). Dinucleotides were most frequent for chromosome 1 and less frequent in chromosome 12 and 9. The richness of (AT)_n dinucleotide repeat motif has been explained by the presence of micron transposable sequences that particularly target (AT)_n repeats (Temnykh et al., 2001; Akagi et al., 2001; Grover et al., 2007). Out of 840 hypervariable rice microsatellite (hvRM) markers identified, markers with dinucleotides repeat motifs were more in number followed by markers with tri and tetranucleotides repeat motifs (Narshimulu et al., 2011). In present study number of repeats ranged from 5 to 63. Details of repeat motif, length of repeats, and physical position for all the polymorphic microsatellites in the present study will assist in selection for set of polymorphic microsatellites to be used in genotyping of mapping population for QTL mapping and marker-assisted selection in breeding. Distribution of polymorphic markers based on their physical position (Gramene database)  is almost uniform for chromosome 2, 3, and 9 (gap between polymorphic markers is less than 5Mb) but in chromosome 12, 10, 6, 1, and 7 longer stretches of DNA found to be monomorphic indicating conserved sequences distributed in the two parents selected for the study. The importance of evenly distributed markers for analysis and to generate few but highly informative data points either for mapping or threshold recovery of a recurrent parent in marker assisted backcross breeding has been highlighted (Prigge et al, 2009; Servin & Hospital, 2002). Non-random and non-uniform distribution of microsatellite markers in plant genome have also been reported (Morgante et al., 2002). Also, preferential positioning of the microsatellite in the rice genome was revealed (Grover et al., 2007). The reason behind this kind of positioning of markers has been explained as DNA replication and repair mechanisms during the synthesis of new repeats and elimination of existing repeats (Schlotterer, 2000). Low polymorphic regions can be recovered by screening more SSR markers for a given region based on available resources and published literature on the genome map of rice. The identified polymorphic markers from this investigation will be utilized for the mapping of major QTLs/ Candidate genes associated with different micro-nutrients and quality-related traits   in rice. Once identified QTL region defined by marker interval can be narrowed down further by fine-mapping approaches. In detail scanning of genome-wide polymorphic SSR resources for a given bi-parental mapping population, provides way forward for studying ‘Causal polymorphism in terms of SNP variant’ in a genomic region defined by QTL interval. Recent advancement in sequencing technologies has strengthened this possibility by high-throughput genotyping and NGS-led QTL-seq assay. QTL-seq analysis and differential expression profiling studies were performed by  Daware et al. (2016) to identify a potential candidate and allelic variants for grain weight trait in rice is an example of successful integrated use of SSR and SNP markers data. Conclusion Breeding mineral dense rice variety is one of the sustainable approaches for ensuring the nutritional security of the nation. Local genotypes and landraces are a proven source of higher mineral content and the present study attempts to make use of local genotype to identify new genomic regions for high grain zinc and iron content. Identified markers showing allelic variant for whole genome wide SSRs covering the whole genome of rice in the present parental polymorphism study will be utilized for genotyping of whole mapping population developed from the above cross and better understanding of mapping QTLs/genes for high grain zinc, iron, grain quality, and yield traits. It can significantly contribute towards the development of mineral dense rice variety using marker assisted selection and marker assisted backcross approaches. Acknowledgement The authors gratefully acknowledge the financial support provided by Department of Science and Technology (DST), Ministry of Science and Technology, Government of India, through DST INSPIRE fellowship (IF170506) to the first author for pursuing full-time doctoral  (Ph. D.) degree programme at Banaras Hindu University, Varanasi, as well as thankful to Harvest Plus project on Developing high zinc rice for Eastern India (IFPRI, Washington D.C. and CIAT, Columbia) for providing funds for conducting the trial and thankful to Dr. Arvind Kumar, Director ISARC, Varanasi and Dr. Vikas Kumar Singh, Coordinator, IRRI-SAH, Hyderabad for providing resources and facilities to carry out above research work. Conflicts of interest The authors declare that there is no conflict of interest.