Evaluation of the Agro-morphological traits, seed characterization and the genetic diversity of local Rice (Oryza sativa L.) Varieties of Pakistan

Evaluation of the Agro-morphological traits, seed characterization and the genetic diversity of local Rice (Oryza sativa L.) 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Varieties of Pakistan Urooj Fazal, Israr Uddin, Amir Muhammad Khan, Fahim Ullah Khan, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1736575/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Background: Broad base genetic diversity is essential for a sustainable crop yield to provide tolerance against biotic and abiotic stresses. In Pakistan, rice is ranked second as a staple food and cultivated under different climatic zones. However, very little is known in terms of rice genetic diversity. Methods and results: Present research were performed to evaluate the genetic diversity among 12 local rice varieties by using 5 SSR markers. Furthermore, agro-morphological parameters and seed characterization of these varieties were studied. The highest plant height (204.3 cm) and panicle length (40.1 cm) were observed for Hans-raj, the maximum number of panicles were recorded for dilrosh-97 (47.3). The maximum number of primary and secondary panicle branches were observed in Ratua-81 (51) and super basmati (62), respectively. Super basmati showed highest number of filled grains (264.3) and total number of grains per panicle (315). The minimum days to maturity was recorded for ratua-81 (140 days), and highest 1000 grains weight was recorded for Ksk-133 (27g). The highest concentration of elements, e.g. zinc (Zn) was observed in Ks-282 (44µg/g), Iron (Fe) in hansraj (190.3µg/g), manganese in bamla sufaid (111.3µg/g), copper (Cu) in hansraj (856.3µg/g), lead (Pb) in super basmati (3883.3µg/g), and nickel (Ni) was found in basmati-385 (314.6ug/g). For the genetic diversity analysis, five SSR markers were used and a total of 60 alleles were amplified with 20% polymorphism, RM28130 showed the highest PIC value (0.60). The maximum number (3) of alleles were produced by RM28089. Based on secondary panicle branches and total number of filled grains, Super basmati showed best panicle architecture and grain yield, which can be used in breeding program to develop high yielding aromatic rice genotypes. Conclusions: The highest number of filled grains and total number of grains per panicle were observed in super basmati, which indicated the potential adaptation. Basmati Hansraj, Ks-282, Super basmati and Basmati surkh-161 were found to be highly rich in micronutrients (iron, zinc, copper, lead and manganese). The maximum genetic distance was observed in basmati-2000 and basmati surkh 161 genotypes. Genetic diversity SSR markers agro-morphological Biochemical Oryza sativa Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Rice ( Oryza sativa L.) is classified in the family Graminae and sub family Oryzoidea, which is an essential cereal crop with nutritional and agronomic values. Rice is the staple food and major source of daily calories for more than half of the world’s population [1]. Rice genome is the smallest and well characterized compared to other major cereal crops with 400 to 430 Mb size. Other cereal crops like sorghum has 750 to 770 Mb genome size, while wheat genome is about 37 times larger than the rice at around 16,000 Mb [2]. In rice there is a vast range of variation in their genotypes among and between landraces [3]. Rice is cultivated worldwide, that includes Asia, North and South America, throughout Europe, Middle East and Africa [4]. Twenty-two species of rice have been introduced so far, but among them the most cultivated are: Oryza sativa or Asian rice and Oryza glaberrima or African rice [5]. The cultivation of rice crop can be done worldwide under any climatic condition. Almost 11% of the global arable land is planted annually to the rice crop and it ranked after wheat. It is cultivated on 163 million hectares, produces approximately 491 million tons of rice. Asia comes on the first in cultivation and usage of rice in the world that contains more than quarter population of the world. Approximately 90% of the rice is produced in Asia around the world [6]. An essential metallothionein like protein formed by the Basmati rice that are rich in sulfur. At the international level basmati rice demand is also high because of the good quality, different and pleasing aroma, its fluffy texture and high-volume enlargement when cooked [7]. In Pakistan basmati or aromatic rice are considered of high quality and an essential crop to export. About two million ha of the land covers by the rice in Pakistan which is almost 10% of the cultivated land [8]. During last 25 years the rice production become doubled because of the technological advances like introducing genetically improved high yielding varieties and appropriate crop management practices. Furthermore, to study grass genetics, rice is counted as an example due to its diploid and small genome size, genetic polymorphism, large collection of genetically diverse and conserved genetic material of about 100,000 accessions and availability of compatible wild species [9, 10, 11]. The Asiatic rice, Oryza sativa is domesticated from O. rufipogon Griff. the common wild ancestor. To understand domestication and genetic diversity in crops, rice as a model species, holds a unique position due to the sequenced genomes of Indica and Japonica [5, 12, 13, 14]. The aromatic rice also called basmati rice, is long grain rice that is used for the improvement of novel varieties of rice as it has more diversity and great potential. Pakistan counts in the world’s countries for the export of aromatic rice [15]. The good quality of basmati rice is due to its aroma, nice texture and flavor. Like Pakistan, Nepal, India and Iran also grow aromatic rice, which possess high value [16]. Micronutrients are not only crucial for plants growth but also a basic need for both the human and animal health. Iron helps in the cellular processes that includes photosynthetic electron transport, respiration and chlorophyll biosynthesis. Zinc is also an important type of element that is involved in cellular processes as well, for the metabolism of lipids, proteins and carbohydrates. The deficiency of zinc in soil badly affects the nutritional quality of the grains and the yield of crop. Manganese act as a cofactor to activate the enzymes with functional groups [17, 18]. Copper is among the trace metals and is one of the redox active elements that is needed for the plant growth. But in soil its high concentration is harmful for the growth of plants like it inhibits the growth and producing the reactive oxygen radicals that cause oxidative damage [19]. Among the heavy metals nickel is also an important microelement for humans which acts as a cofactor for some of the enzyme’s proteins that contains metal ions and in humans it helps in the iron metabolism [20]. During the last 20 years, vast progress has been observed in the molecular mechanisms implicit in the significant agronomic features. To achieve high yield for a crop, flowering time is a captious trait through the use of maximum sunlight and temperature. Others agronomical traits that contribute to crop production are plant height, tiller number and panicle architecture. Panicle number is determined by the growth of tiller that has a direct effect on the yield of crop. Tiller quality make up the foundation of lodging tolerance and significance of cereal plant fiber as animal food [21]. Multiple genes control the most agronomic traits in rice which show complicated and numerical inheritance. Molecular breeding is beneficial to recognize the Quantitative Trait Loci (QTLs) associated with the desired traits, as QTL controlling agronomic traits are labeled by DNA markers and introduced into the elite cultivars by Marker Assisted Selection ((MAS). The benefits of SSR markers are accessible and potent in breeding new rice cultivars to improve important agronomic traits, like plant height, maturity, seed shattering, amylose content, yield and resistance to disease and environmental stresses [22]. Genes that are linked to drought stress in rice have been identified through the characterization and screening of rice genotypes genetically and morphologically, to determine the genetic variation in them. Through molecular marker technology quantitative traits are now possible to be identified [23]. Molecular markers help to identify polymorphism in rice genomes and are very useful to study the diversity at genetic level [24]. In order to identify the enhanced genotypes for the quantitative and qualitative traits the study of Agro-morphological diversity is very important for the germplasm of local and exotic varieties. It is very helpful to determine the best genotypes that can be promoted further for the yield purposes [22]. Presently in plant genetics and breeding studies, microsatellites or Simple Sequence Repeats (SSR) are widely used and chosen markers after Single Nucleotide Polymorphism (SNPs), due to their large genome coverage, comparative abundance, high reproducibility, co-dominant inheritance, able to differentiate both inter- and within-species variations, and simple to analyze them [25, 26]. There are several applications for which the SSRs markers are being used that includes; (i) genome mapping, (ii) to assess genetic diversity and linkage between different cultivars in aromatic and non-aromatic rice, (iii) for the estimation and testing of purity in varieties, (iv) to determine genetic relatedness among several sub species [27). For rice, SSR markers for any genome part or region are easily available and the candidate gene markers can easily be developed. The molecular markers are very important to the rice cultivars for the purpose of improvement and possess so many applications in the field of agriculture [28]. The objectives of this study is to examine the genetic diversity through SSR markers, seed characterization and agro-morphological study amongst twelve rice genotypes. Material And Methods Plant material and experimental design The current research was carried out at the Institute of Biotechnology and Genetic Engineering (IBGE), The University of Agriculture Peshawar, Pakistan. The research was performed on local rice genotypes including ten basmati and two non-basmati varieties (Table 1 ). The selected varieties were evaluated for agro-morphological traits and genetic diversity. The rice varieties included ten aromatic and two non-aromatic rice genotypes under the Randomized Complete Block Design. The rice seeds were collected from Plant Genetic Resources Institute (PGRI), National Agriculture Research Center (NARC), Islamabad, Pakistan. Seeds were grown in pots in screen house at IBGE under normal growth condition. As soon the nursery was established, uniform seedlings were transferred to the field. Table 1 List of selected local Pakistani rice varieties S.no Varieties names Variety Type Varietal group 1. Basmati Surkh-161 Basmati Aromatic 2. Basmati-385 Basmati Aromatic 3. Basmati-2000 Basmati Aromatic 4. Basmati-198 Basmati Aromatic 5. Super-basmati Basmati Aromatic 6. Dilrosh-97 Basmati Aromatic 7. Hansraj Basmati Aromatic 8. KS-282 Basmati Aromatic 9. KSK-133 Basmati Aromatic 10. Sara Sella Basmati Aromatic 11. Ratua-81 Aus/Boro Non aromatic 12. Bamla Sufaid-320 Aus/Boro Non aromatic Table 2 List of SSR Markers, primers, oligo sequences and expected size of PCR product S.No Markers Primers Oligo Sequence (5' to 3') Chr. No. Product size 1. RM28099 ForwardReverse TGT GCG GAT GCG GGT AAG TCC CCA CCT GTC AAC CAC CGA AAC C 12 210 2. RM28089 ForwardReverse GGG AGG ACA CCT GTG TAA GTA GG GGT TCA AAT GAG ACC CAA TTC C 12 290 3. RM315 Forward Reverse GAG GTA CTT CCT CCG TTT CAC AGT CAG CTC ACT GTG CAG TG 1 150 4. RM28130 Forward Reverse CAG CAG ACG TTC CGG TTC TAC TCG AGG ACG GTG GTG GTG ATC TGG 12 190 5. RM211 Forward Reverse CCG ATC TCA TCA ACC ACC TG CTT CAC AAG GAT CTC AAA GG 2 150 Agro-morphological Traits The data were recorded on plant height (cm), number of panicles plant- 1 , main panicle length (cm), primary and secondary branches panicle − 1 , filled grains panicle − 1 , total number of seeds panicle − 1 , days to maturity, seed morphology and quality, 1000 grains weight (gm) of rice genotypes. Determination Of Seed Quality The heavy metal contents in samples of rice grains were measured after acid digestion (wet digestion) by following the protocol [ 29 ]. ground plant sample (0.5 g) was taken in (250 ml) calibrated Pyrex flask with (10 ml) of HNO3 solution and kept overnight and 4 ml of per chloric acid solution was added. The mixture was heated on hot plate under fume hood until the digest became cleared. After heating the flask’s, distilled water was added to make the volume up to 100ml. Heavy metal concentration in plant extract was measured by atomic absorption spectrophotometer. Dna Extraction And Pcr Amplification Genomic DNA extraction from Rice cultivar was carried out by CTAB method described by [ 30 ]. A fresh young leaves of 1g was taken and ground to powder form in liquid nitrogen. Sample was transferred to 1.5ml Eppendorf tube and 500µl of CTAB buffer was added. Sample was incubated in water bath at 60°C for 30 min and was vortexed after every 5min. Tubes were centrifuged at 12000 rpm for 15 min and supernatant was transferred to a new 1.5ml Eppendorf tube and 500µl of Phenol:Chloroform: Isoamylalcohol (25:24:1) solution was added. After centrifugation at 12000 rpm for 10 min, the upper aqueous phase was transferred to a new tube. After that 600µl ice cold isopropanol was added for the purpose of precipitation. Sample was centrifuged again at 12000 rpm for 2 min and supernatant was discarded without disturbing the pellet. The pellet was washed twice with 500µl of 70% ethanol. Tubes were dried at room temperature. Sample was resuspended in 50µl TE buffer. The extracted DNA quality and optical density were measured at a wavelength of 260/280 nm by using Nanodrop and was stored at -20 o C for future use. Molecular Diversity Screening To study the genetic diversity a total set of five SSR markers were selected from Gramene Markers Database ( https://archive . gramene.org/ markers/) and were used to determine the genetic diversity among the selected rice genotypes as shown in (Table 1 ). Polymerase Chain Reaction (Pcr) The PCR reaction was performed as described (31]. A toral of 10 µl reaction mixture containing 1 µl DNA template, 1 µl of each forward and reverse primer, 1.8 µl of nuclease free water, 0.2 µl of Taq DNA polymerase and 5 µl of PCR master mix. PCR conditions was set with an initial denaturation step of 5 min at 94°C followed by 35 cycles, each comprises of a denaturation at 94°C for 30 Sec., annealing at an appropriate temperature for 30 sec and elongation step of 1min/kb for 72°C, and final extension at 72°C for 10 min. The BIO-RAD T100 thermal cycler was used. Gel Electrophoresis The amplified PCR product were confirmed using 2% Agarose gel. The agarose gel was prepared in 1X TBE buffer that was mixed and heated for 2 min in microwave oven. The gel was solidified by cooling for 20 min and then transferred to gel tank. The product size of different genotypes was determined by comparing with 100bp DNA ladder. The gel was run for 45 to 50 min at 110V. The PCR products were visualized by using 2ul of Ethidium Bromide and checked under UV light Trans illuminator and documented. Data analysis Molecular and morphological data was compiled in MS Excel and Popgene 3.5 version was used to find out Allelic Frequency, PIC, Heterozygosity and Homozygosity and Genetic diversity. The UPGMA procedure was used to construct dendrogram using the Popgene. Results Evaluation of rice cultivars for agro-morphological traits Morphological traits of twelve Pakistani rice cultivars were evaluated and data was taken after harvesting time. The highest plant height (204.3 cm) and panicle length (40.1 cm) were observed for Hans-raj, whereas the maximum number of panicles was noted for dilrosh-97(47.3). The maximum number of primary and secondary panicle branches were observed in Ratua-81 (15) and super basmati (62), respectively. Super basmati showed highest number of filled grains (264.3) and total number of grains per panicle (315). The minimum days to maturity was exhibit by ratua-81 (140 days), and highest 1000 grains weight was recorded for Ksk-133 (27g) which exhibited highly significant variation for agro-morphological parameters among the rice varieties (Table 3 ). Table 3 Mean performances of twelve rice varieties for different morpho-yield parameters using LSD test No. Genotypes Plant height (cm) Number of panicles per plant Main Panicle length (cm) Primary branches per panicle Secondary branches per panicle Filled seeds per panicle Total seeds per panicle Days to maturity 1000 grains weight (gm) 1. Dilrosh-97 95.67 H 47.333 A 23.067 E 9.333 D 19.333 DE 85.00 F 115.33 F 141.33 H 24.300 D 2. Basmati-198 162.67 CDE 30.667 C 31.667 BC 13.000 ABC 48.667 B 228.67 BC 246.33 BC 161.00 B 19.233 H 3. Basmati-2000 150.33 EFG 21.667 30.333 BCD 11.333 CD 24.667 D 82.67 F 164.67 E 150.00 D 21.767 F 4. Ksk-133 92.00 H 25.333 DE 25.300 DE 11.000 CD 16.333 E 89.00 F 94.67 F 143.67 G 27.000 A 5. Hansraj 204.33 A 42.667 AB 40.100 A 12.333 BC 48.333 B 235.33 BC 255.00 B 149.33 D 24.467 D 6. Ratua-81 190.00 B 28.333 B 38.167 A 15.000 A 51.667 B 245.33 AB 268.67 B 140.00 I 20.067 G 7. Basmati-385 146.00 FG 43.667 A 32.100 B 12.667 ABC 49.000 B 240.67 AB 260.33 B 145.00 F 20.100 G 8. Super basmati 141.00 G 38.333 B 37.000 A 14.667 AB 62.000 A 264.33 A 315.00 A 158.67 C 21.700 F 9. Ks-282 92.67 H 15.000 G 27.700 CD 11.000 CD 23.333 DE 124.00 E 147.67 E 144.00 G 24.733 C 10. Bamla-Sufaid 157.00 DEF 29.667 CD 28.333 BCD 10.667 CD 36.000 C 210.67 C 220.00 CD 144.33 FG 25.167 B 11. Basmati-Surkh-161 169.00 CD 27.333 DE 37.500 A 12.667 ABC 33.000 C 163.33 D 198.67 D 147.67 E 22.600 E 12. Sara sella 175.67 C 19.667 FG 31.667 BC 11.000 CD 47.667 B 222.33 BC 256.00 B 169.67 A 22.533 E LSD 0.05 13.459 4.8428 4.0553 2.4022 7.6170 26.612 26.435 0.8294 0.1897 Grains Characterization Of Rice Genotypes The seed quality analysis including, minerals content of the rice seeds, including iron, zinc and manganese and heavy metals, including lead, copper and nickel were evaluated. Estimation Of Iron (Fe) Iron content in rice grains from 12 different genotypes analyzed, has shown average values ranged from 46-190.3 µg/g (Fig. 2 ). The highest content of iron was found in seeds of Hansraj (190.3µg/g), followed by basmati-198 (173 µg/g) and super basmati (147.6 µg/g). While the lowest content of iron was found in bamla sufaid (46 µg/g) followed by basmati-385 (74 µg/g) and ksk-133(76.3 µg/g). ANOVA showed highly significant difference (LSD: 6.8956) for iron content in seeds of 12 rice genotypes (Table 4 ). Table 4 Mean of twelve basmati rice genotypes for different elemental analysis using LSD test No Varieties Iron (Fe) µg g-1 Zinc (Zn) µg g-1 Manganese (Mn) µg g-1 Copper (Cu) µg g − 1 Lead (Pb) µg g − 1 Nickel (Ni) µg g − 1 1. Dilrosh-97 77.67 H 7.233 H 57.33 D 303.33 F 972.3 I 160.67 D 2. Basmati-198 85.33 G 13.333 G 57.67 D 159.33 J 1175.0 H 114.67 G 3. Basmati-2000 173.00 B 11.000 G 69.00 C 358.00 E 3055.7 D 141.33 F 4. Ksk-133 76.33 H 20.333 E 60.33 D 193.00 H 2221.0 E 149.00 E 5. Hansraj 190.33 A 17.333 F 71.67 C 856.33 A 3148.7 C 25.30 K 6. Ratua-81 152.33 C 24.000 D 56.67 D 227.33 G 547.3 K 100.33 H 7. Basmati-385 104.67 F 35.000 B 108.00 A 374.67 D 1233.0 G 71.67 I 8. Super basmati 147.67CD 29.000 C 41.67 E 384.67 D 3883.3 A 241.33 B 9. Ks-282 141.33 D 44.000 A 87.67 B 545.00 B 1350.7 F 203.00 C 10. Bamla-sufaid 46.00 I 11.667 G 111.33 A 183.33 I 3560.0 B 146.67 EF 11. Basmati-surkh-161 74.00 H 10.667 G 76.00 C 165.33 J 1311.3 F 314.67 A 12. Sara sella 128.33 E 24.200 D 46.07 E 158.33 J 706.0 J 45.67 J LSD 0.05 6.8956 2.7328 7.4418 9.3186 54.378 6.7480 Table 5 Shows band pattern, polymorphic bands and polymorphism of 5 markers among 12 rice varieties . Primers Bands pattern Polymorphic bands Polymorphism (%) RM28099 2 2 20% RM28089 3 3 20% RM315 2 2 20% RM28130 2 2 20% RM211 2 2 20% TOTAL 11 11 20% Estimation Of Zinc (Zn) Data regarding Zinc content in seeds of 12 rice varieties, the average concentration ranged from 7.2–44 µg/g (Fig. 2 ). The highest content of Zn was found in Ks-282(44 µg/g), followed by basmati 2000 (35 µg/g) and super basmati (29 µg/g), while the lowest was recorded for dilrosh-97(7.2 µg/g) followed by basmati-385(10.6 µg/g) and basmati-198 (11 µg/g). ANOVA showed highly significant difference (LSD: 2.7328) for zinc content among the studied genotypes (Table 4 ). Estimation Of Manganese (Mn) The average concentration of manganese in seeds of 12 rice varieties ranged from (111.3–41.6 µg/g) (Fig. 2 ). The highest content of manganese was found in Bamla sufaid (111.3 µg/g), followed by basmati-2000(108 µg/g) and ks-282(87.6 µg/g). While the lowest concentration was found in super basmati (41.6 µg/g) followed by sara sella (46.0 µg/g) and dilrosh-97(57.3 µg/g). ANOVA showed highly significant difference (LSD: 7.4418) (Table 4 ). Estimation Of Copper (Cu) The average concentration of copper in rice grains from 12 genotypes varied from 158.3-856.3 µg/g (Fig. 2 ). The highest concentration of copper was recorded in Hansraj (856.3 µg/g), followed by ks-282 (545 µg/g) and super basmati (384.6 µg/g). While the lowest content of copper was noted in sara sella (158.3 µg/g), followed by basmati surkh-161 (159.3 µg/g) and basmati-385(165.3 µg/g). ANOVA showed highly significant difference (LSD: 9.3186) (Table 4 ). Estimation Of Lead (Pb) The average concentration of Pb in seed samples ranged from 547.3-3883.3 µg/g (Fig. 2 ). The highest content of lead was recorded in super basmati (3883.3 µg/g), followed by bamla sufaid (3560 µg/g) and hansraj (3148.6 µg/g), while the lowest content of lead was noted in ratua-81 (547.3 µg/g), followed by the variety of sara sella (706 µg/g) and dilrosh-97 (972.3 µg/g). ANOVA resulted highly significant difference (LSD: 54.378) (Table 4 ). Estimation Of Nickel (Ni) The average concentration of nickel, varied from 25.3-314.6 µg/g among 12 genotypes (Fig. 2 ). The highest content of nickle was recorded in basmati-385 (314.6 µg/g), followed by super basmati (241.3 µg/g) and ks-282 (203 µg/g), while the lowest content of nickel was noted in hansraj (25.3 µg/g), followed by sara sella (45.6 µg/g) and basmati-2000 (71.6 µg/g). ANOVA showed highly significant difference (LSD: 6.7480) (Table 4 ). Genetic diversity based on SSR markers Genetic diversity among twelve rice cultivars were detected utilizing 5 SSR markers with a toral of 60 allelic band with highly polymorphism were recorded. RM28099 primer exhibited 12 allelic bands with two patterns of polymorphism. The allelic frequency observed for allele A was 0.7500 and for allele B, it was 0.2500. PIC value for RM28099 was recorded as 0.29 (Fig. 3 ). RM28089 produced a total 12 allelic bands were amplified, which comprising 3 band patterns (Fig. 3 ). The allelic frequencies recorded for allele A, B and C were 0.2917, 0.5417 and 0.1667, respectively. PIC value for RM28099 was recorded to be 0.34. RM315, produced 12 allelic bands and 2 band patterns. The allelic frequency for allele A was recorded as 0.7273, while for allele B, it was 0.2727. PIC value for RM315 was recorded as 0.27 (Fig. 3 ). RM28130 primers showed overall 12 allelic bands and 2 band patterns were observed. The allelic frequency recorded for allele A, among 12 rice varieties were 0.6667 and for allele B, showed 0.3333. PIC value for RM28130 was recorded as 0.60 (Fig. 3 ). Marker RM211 amplified 12 allelic bands with 2 band patterns. The allelic frequency recorded for allele A and B was 0.7500 and 0.2500, respectively. PIC value for RM211 was recorded as 0.29 (Table 6 ). Table 6 Overall allelic frequency Primers Locus/alleles Frequency RM28099 A 0.7500 RM28099 B 0.2500 RM28089 A 0.2917 RM28089 B 0.5417 RM28089 C 0.1667 RM315 A 0.7273 RM315 B 0.2727 RM28130 A 0.6667 RM28130 B 0.3333 RM211 A 0.7500 RM211 B 0.2500 Table 7 Descriptive Statistics of multi-populations Locus I Na Ne Obs. Hetro Exp. Hetro PIC Primer RM28099 0.5623 2.0000 1.6000 0.0000 0.3913 0.29 Primer RM28089 0.9901 3.0000 2.4615 0.5833 0.6196 0.34 Primer RM315 0.5860 2.0000 1.6575 0.0000 0.4156 0.27 Primer RM28130 0.6365 2.0000 1.8000 0.0000 0.4638 0.60 Primer RM211 0.5623 2.0000 1.6000 0.0000 0.3913 0.29 na = Observed number of alleles ne = Effective number of alleles [Kimura and Crow (1964)] I = Shannon's Information index [Lewontin (1972)] Nei's (1973) expected heterozygosity Table 8 Overall allele frequency Allele/locus Primer RM28099 Primer RM28089 Primer RM315 Primer RM28130 Primer RM211 A 0.7500 0.2917 0.7273 0.6667 0.7500 B 0.2500 0.5417 0.2727 0.3333 0.2500 C 0.1667 Overall Allelic Frequency In current study, RM28099 exhibited two alleles A, B was observed with highest frequency of 0.7500 and lowest frequency of 0.2500, respectively. For RM28089, three alleles were observed with highest frequency for allele B (0.5417), followed by allele A (0.2917) and the lowest frequency was observed for allele C (0.1667). For RM315 the highest frequncey was noted for allele A (0.7273) and lowest frequency was recorded for allele B (0.2727). For RM28130, the highest frequency was observed for allele A (0.6667) and lowest frequency was noted for allele B (0.3333). For RM211 the highest allelic frequency was recorded for allele A (0.7500) and lowest allelic frequency was observed for allele B (0.2500) (Table 6 ). Genetic Relationship Of The Rice Germplasm Dendrogram constructed based on similarity coefficient for the estimation of genetic relationship using Unweight Pair Group Method with Arithmetic Means (UPGMA) cluster analysis, which are based on Nei's Genetic distance Modified from NEIGHBOR procedure of PHYLIP Version 3.5 Pop-Gene software. All the genotypes were divided into two main groups A and B. Group A was further divided into sub groups A1 and A2. Group A1 included the varieties dilrosh-97 and sara sella. Group A2 divided into further subgroups A2a, A2b and A2c. The A2a group included ksk-133, A2b included ratua-81 and super basmati varieties. While the A2c contained basmati-385, bamla sufaid-121and basmati surkh-161. Group B is divided into sub groups B1 and B2. B1 group included basmati 198, basmati 2000 and hansraj, whereas the B2 group included only KS 282 variety. The maximum genetic distance (41.406%) was noted for varieties basmati surkh-161 and basmati-2000. While the least genetic distance (0.564%) was observed for varieties that includes KS-282, and BASMATI-2000, and no genetic distance (0.000%) was observed between the varieties Basmati-385, Bamla sufaid, and basmati surkh-161 as they belonged to the same origin of genotype (Fig. 04 ). Discussion Pakistan is the 11th largest producer and 4th largest exporter of rice, which is cultivated under different climatic zones; however, very little is known in terms of rice genetic diversity in Pakistan with respect to global germplasm [ 32 ]. A broad base genetic diversity having a wide range of genetic variations in genome is crucial to provide tolerance against biotic and abiotic stresses. Germplasm serves as the main source of genetic variation that could be utilized by the breeders in the future for the enhancement of crops through breeding programs or hybridization. For the improvement and development of any variety the main source for crop breeding programs is the germplasm due to the conserved characters needed for any good crop yield production, tolerance against environmental stresses and for enhanced crop quality. To monitor and evaluate the diversity and some essential genes among the crops, the assessment of germplasms collections is very indispensable [ 33 ] Germplasms plays a vital role for the development of novel varieties and to secure our food. The developed varieties contain a distinct genic trait responsible for combating against all the diseases, insects and have improved nutritional value. Rice germplasm including landraces, traditional varieties, breeding lines and elite genotypes. Worldwide, around 4,500,000 accessions of plants have been collected and specifically in rice about 400,000 accessions are reported [ 34 , 35 ]. For the estimation of rice germplasm, IRRI has presented more than 1000 genotypes yearly [ 42 ]. In the present the qualitative and quantitate agro-phenotypic characteristics has been showed highly phenotypic variation included plant height, number of panicles per plant, main panicle length, primary and secondary branches per panicle, filled grains per panicle, total number of seeds per panicle, days to maturity, seed quality and 1000 grains weight. Super basmati showed maximum number of secondary branches per panicle (62), maximum number of filled grains (264.3] and total grains per panicle (315). The crucial and main agronomic character to achieve the improved rice production is the grain yield under favorable and unfavorable environments [ 36 ]. A previous study displayed similar results with high significant variation in all the 14 traits of rice germplasms [ 37 , 36 ]. Researchers investigated the copper concentration in rice genotypes and observed high content in rice root [ 19 ]. In the current study the quality analysis of rice grains of twelve rice varieties ware analyzed, total six micronutrients included iron, zinc, manganese, copper, lead and nickel. The highest content of iron was found in seeds of Hansraj, the highest Zn concentration was found in Ks-282, highest manganese and lead were found in super basmati, highest concentration of copper was recorded in Hansraj, and highest content of nickle was recorded in basmati − 385. Highly significant variation was recorded for elements in all the genotypes of the rice. The results of this study are consisted with previous work [ 20 ] where they worked on nickel metal concentration in milled rice grain and found the significant variation. Moreover, our results are also in line with the study of [ 38 ], who analyzed eleven aromatic and one non aromatic rice genotypes for micronutrients (Zn, Mn, Fe, Cu). [ 20 ] investigated lead content in rice grains. It is prominent to explore the genetic variation within a rice population, for the management and utilization of rice germplasm. That could be further used for the improvement and development of new rice varieties [ 39 ]. Traditionally the use of morphological or physiological traits were used to evaluate the genetic diversity in plants but this technique was not trustworthy as this procedure was dependent upon the environment due to the gene expression influenced by it. The scientists approached an alternative way to figure out this problem and they introduced the use of molecular markers. Molecular markers can be used to assess the genetic diversity among the varieties of rice, as well as the quantitative and inherited traits can also be analyzed through it [ 40 ]. In the present study total of five SSR primers were used in order to identify the genetic variation among the 12 local rice genotypes. The highest number of polymorphic alleles (three alleles) were observed for RM28089. While the remaining markers showed less number of alleles i.e. 2 polymorphic alleles each. These markers resulted in 20% polymorphism among all the rice genotypes. [ 41 ] identified the genetic variation and characterized 15 varieties using 30 different SSR primers that revealed distinct polymorphism between the studied varieties. The number of band pattern in the current study corresponded well with the study [ 37 ]. The PIC values varied from 0.27 to 0.60 with average value of 0.358. The highest PIC value (0.60) was observed for RM28130. In a previous study [ 28 ] has shown similar results with PIC values ranged from 0.0476 to 0.5993 with average of 0.3785 using 24 SSR markers. PIC value shows the variation, diversity and frequency among the genotypes of the varieties or cultivars [ 28 ]. The 12 rice genotypes were divided in to mainly two groups, i.e. A and B based on UPGMA cluster analysis. The highest genetic distance 41.4% was recorded among the varieties of basmati-2000 and basmti surkh-161 having same geographical origin. Conclusion The current study observed highly significant difference for the agro-morphological traits including plant height, number of panicles per plant, main panicle length, primary and secondary panicle branches, filled grains per panicle, total number of seeds per panicle, days to maturity, and 1000 grains weight. The highest number of filled grains and total number of grains per panicle was noted in super basmati, which indicated the potential adaptation of this variety to the prevailing high temperature during the growth period. Basmati Hansraj, Ks-282, Super basmati and Basmati surkh-161 were found to be highly rich in micronutrients (iron, zinc, copper, lead and manganese). Based on the molecular markers assessment, the 12 local varieties cluster into two groups. The maximum genetic distance was observed in the varieties of basmati-2000 and basmati surkh 161. RM28310 was highly informative having maximum PIC value, whereas highest polymorphic alleles (three) were observed for RM28089. Based on current research study it is recommended that broad genetic background especially highly yield contributing components, super basmati is recommended to be included in breeding program. Rice varieties may further be characterized for nutrients uptake ability and its underlying mechanism. Abbreviations QTLs Quantitative trait loci SSR simple sequence repeats CTAB cetyltrimethylammmonium bromide UPGMA Unweight Pair Group Method with Arithmetic Means PIC polymorphic information content Declarations Funding Not applicable. Data availability Not applicable. Declaration Conflict of interest The authors have no conflict of interest. Ethical approval This article does not contains any study with human participants or animals performed by any of the authors. 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Rice Res 2(1):1–4 Nadia I, Mohiuddin AMK, Sultana S, Ferdous J (2014) Diversity analysis of indica rice accessions (Oryza sativa L.) Using morphological and SSR markers. Annals Biol Res 5(11):20–31 Shamim M, Manzar H, Sharma V, Kumar P (2016) Microsatellite marker-based characterization and divergence analysis among rice varieties. Indian J Biotechnol 15:182–189 Ming HUANG, Ya-hui UV, Xing-xing TAO, Yong-zhu LIU, Gui-li YANG, Zhi-qiang VHEN (2015) Genetic diversity of main inbred Indica rice varieties applied in Guangdong Province as revealed by molecular marker. Rice Sci 22(1):1–8 Kshirsagar S, Samal K, Rout G (2012) Genetic diversity associated with agronomic traits using SSR markers in Indica rice landraces. J Plant Sci Res 28(1):27–36 Gao LZ, Innan H (2008) Non independent domestication of the two-rice subspecies, Oryza sativa ssp. Indica and ssp. Japonica , demonstrated by multi-locus microsatellites. Genetics 179(2):965–976 Joshi BK (2017) Conservation and utilization of agro-biodiversity advanced from 1937 to 2017 in Nepal. Krishi Sanchar Smarika (F Devkota, ed). Agricultural Information and Communication Center (AICC), moad.181–208 Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 03 Jul, 2022 Reviewers invited by journal 03 Jul, 2022 Editor assigned by journal 13 Jun, 2022 First submitted to journal 07 Jun, 2022 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1736575","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":118148638,"identity":"7e4cf11a-42d0-4bff-aa3e-1b99ade5ca8b","order_by":0,"name":"Urooj Fazal","email":"","orcid":"","institution":"The University of Agriculture Peshawar","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Urooj","middleName":"","lastName":"Fazal","suffix":""},{"id":118148639,"identity":"38a4e601-23bc-4820-9834-5816a28f34ae","order_by":1,"name":"Israr Uddin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYBACNijN2HCYgfEBhJ0AxAeI08JsQJQWGGBsOMDAJkGUFj7+sw8/VzAclu07znus4mfbHQZ+9hwD5oIzeBwmkW4seYbhsPHMw3xpN3vbnjFI9rwxYJ5xA58WNgbJBobDiRsO85jd4G07zGBwA2gLzwc8WviPMf+EaSn8C9RiT1ALQxob3BZmsC0SIC14HZbGZtlgkA70C4+xtMy5ZzwSZ54VHJ6Bx/vy/ceYbzZUWMv2nT9j+PFN2R05/vbkjY8LjuHWAgEGcNYBHhB5mJAGZHAATDKTomUUjIJRMAqGPQAAkvFRZ0mTNwwAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-0643-2408","institution":"The University of Agriculture Peshawar","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Israr","middleName":"","lastName":"Uddin","suffix":""},{"id":118148640,"identity":"e2815ec8-cfd8-42a7-9cc5-d317d47f27be","order_by":2,"name":"Amir Muhammad Khan","email":"","orcid":"","institution":"The University of Agriculture Peshawar","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Amir","middleName":"Muhammad","lastName":"Khan","suffix":""},{"id":118148641,"identity":"12b4e0ab-aca4-43ec-a912-de1927ed72e7","order_by":3,"name":"Fahim Ullah Khan","email":"","orcid":"","institution":"Hazara University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Fahim","middleName":"Ullah","lastName":"Khan","suffix":""},{"id":118148642,"identity":"16f8cf8e-987f-4509-bdb4-67d135371a48","order_by":4,"name":"Mudassar Nawaz Khan","email":"","orcid":"","institution":"Hazara University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mudassar","middleName":"Nawaz","lastName":"Khan","suffix":""},{"id":118148643,"identity":"42d45ec0-e33e-418c-8b62-b13d398c4ee1","order_by":5,"name":"Navid Iqbal","email":"","orcid":"","institution":"The University of Agriculture Peshawar","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Navid","middleName":"","lastName":"Iqbal","suffix":""},{"id":118148644,"identity":"4d049c58-35fe-4d66-8c09-78bb28a8d51b","order_by":6,"name":"Muhammad Ibrahim","email":"","orcid":"","institution":"The University of Agriculture Peshawar","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"","lastName":"Ibrahim","suffix":""},{"id":118148645,"identity":"fa80b55a-f9c7-49b2-a4de-2d9315825174","order_by":7,"name":"Sajid Ali Khan Bangash","email":"","orcid":"","institution":"The University of Agriculture Peshawar","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Sajid","middleName":"Ali Khan","lastName":"Bangash","suffix":""}],"badges":[],"createdAt":"2022-06-08 05:40:46","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1736575/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1736575/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":23723828,"identity":"2f763e30-dddb-4e5e-b2f6-061e8ee5eb32","added_by":"auto","created_at":"2022-07-11 18:19:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1070850,"visible":true,"origin":"","legend":"\u003cp\u003ePanicle number of twelve rice varieties. 1. basmati surkh-161; 2. basmati-385; 3. basmati-2000; 4. basmati-198; 5. super basmati; 6. Dilrosh-97; 7. hansraj; 8. ks-282; 09. ksk-133; 10. sara sella; 11. ratua-81; 12. bamla-sufaid.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-1736575/v1/3c65436cb7701ee790a9c5df.png"},{"id":23723827,"identity":"e6284f20-2bf1-40d4-9eac-e51d72c29423","added_by":"auto","created_at":"2022-07-11 18:19:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":363416,"visible":true,"origin":"","legend":"\u003cp\u003eQuality attributes of Pakistan local genotypes.\u0026nbsp;\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-1736575/v1/48c543d855bda54ed3518e2e.png"},{"id":23723829,"identity":"78fd6dbe-0457-4a31-ab8e-d6733e1f735f","added_by":"auto","created_at":"2022-07-11 18:19:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":518378,"visible":true,"origin":"","legend":"\u003cp\u003ePCR amplification profile of SSR marker RM28099, RM28089, RM315, RM28130 \u0026amp; RMRM211. L represents 1kb ladder; 1. Dilrosh-97; 2. basmati-198; 3. basmati-2000; 4. ksk-133; 5. hansraj; 6. ratua-81; 7. basmati-385; 8. super basmati; 9. ks-282; 10. bamla-sufaid; 11. basmati surkh-161; 12. sara sella. The PCR product was separated on 2% agarose gel.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-1736575/v1/035cdd5366711946220e32e2.png"},{"id":23723826,"identity":"b1b8349b-31e5-4952-807b-23c641a61d4f","added_by":"auto","created_at":"2022-07-11 18:19:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":38368,"visible":true,"origin":"","legend":"\u003cp\u003eDendrogram constructed for 12 rice genotypes by Popgene version 3.5 based on genetic similarities using set of five SSR markers.\u0026nbsp;\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-1736575/v1/78e77c160ecb47d0d066dda2.png"},{"id":23723830,"identity":"a9f12998-a9e7-423f-9ead-7aa7f5049bb8","added_by":"auto","created_at":"2022-07-11 18:19:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":656288,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1736575/v1/97d4d5e4-c4b0-4b70-8fe4-6bb0a2abf181.pdf"}],"financialInterests":"","formattedTitle":"Evaluation of the Agro-morphological traits, seed characterization and the genetic diversity of local Rice (Oryza sativa L.) Varieties of Pakistan","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRice (\u003cem\u003eOryza sativa\u003c/em\u003e L.) is classified in the family \u003cem\u003eGraminae\u0026nbsp;\u003c/em\u003eand sub family \u003cem\u003eOryzoidea,\u003c/em\u003e which is an essential cereal crop with nutritional and agronomic values. Rice is the staple food and major source of daily calories for more than half of the world\u0026rsquo;s population [1]. Rice genome is the smallest and well characterized compared to other major cereal crops with 400 to 430 Mb size. Other cereal crops like sorghum has 750 to 770 Mb genome size, while wheat genome is about 37 times larger than the rice at around 16,000 Mb [2]. In rice there is a vast range of variation in their genotypes among and between landraces [3]. Rice is cultivated worldwide, that includes Asia, North and South America, throughout Europe, Middle East and Africa [4]. Twenty-two species of rice have been introduced so far, but among them the most cultivated are: \u003cem\u003eOryza sativa\u003c/em\u003e or Asian rice and \u003cem\u003eOryza glaberrima\u0026nbsp;\u003c/em\u003eor African rice [5].\u003c/p\u003e\n\u003cp\u003eThe cultivation of rice crop can be done worldwide under any climatic condition. Almost 11% of the global arable land is planted annually to the rice crop and it ranked after wheat. It is cultivated on 163 million hectares, produces approximately 491 million tons of rice. Asia comes on the first in cultivation and usage of rice in the world that contains more than quarter population of the world. Approximately 90% of the rice is produced in Asia around the world [6]. An essential metallothionein like protein formed by the Basmati rice that are rich in sulfur. At the international level basmati rice demand is also high because of the good quality, different and pleasing aroma, its fluffy texture and high-volume enlargement when cooked [7]. In Pakistan basmati or aromatic rice are considered of high quality and an essential crop to export. About two million ha of the land covers by the rice in Pakistan which is almost 10% of the cultivated land [8]. During last 25 years the rice production become doubled because of the technological advances like introducing genetically improved high yielding varieties and appropriate crop management practices. Furthermore, to study grass genetics, rice is counted as an example due to its diploid and small genome size, genetic polymorphism, large collection of genetically diverse and conserved genetic material of about 100,000 accessions and availability of compatible wild species [9, 10, 11].\u003c/p\u003e\n\u003cp\u003eThe Asiatic rice, \u003cem\u003eOryza sativa\u0026nbsp;\u003c/em\u003eis domesticated from \u003cem\u003eO. rufipogon Griff.\u0026nbsp;\u003c/em\u003ethe common wild ancestor. To understand domestication and genetic diversity in crops, rice as a model species, holds a unique position due to the sequenced genomes of \u003cem\u003eIndica\u0026nbsp;\u003c/em\u003eand \u003cem\u003eJaponica\u0026nbsp;\u003c/em\u003e[5, 12, 13, 14]. The aromatic rice also called basmati rice, is long grain rice that is used for the improvement of novel varieties of rice as it has more diversity and great potential. Pakistan counts in the world\u0026rsquo;s countries for the export of aromatic rice [15]. The good quality of basmati rice is due to its aroma, nice texture and flavor. Like Pakistan, Nepal, India and Iran also grow aromatic rice, which possess high value [16].\u003c/p\u003e\n\u003cp\u003eMicronutrients are not only crucial for plants growth but also a basic need for both the human and animal health. Iron helps in the cellular processes that includes photosynthetic electron transport, respiration and chlorophyll biosynthesis. Zinc is also an important type of element that is involved in cellular processes as well, for the metabolism of lipids, proteins and carbohydrates. The deficiency of zinc in soil badly affects the nutritional quality of the grains and the yield of crop. Manganese act as a cofactor to activate the enzymes with functional groups [17, 18]. Copper is among the trace metals and is one of the redox active elements that is needed for the plant growth. But in soil its high concentration is harmful for the growth of plants like it inhibits the growth and producing the reactive oxygen radicals that cause oxidative damage [19]. Among the heavy metals nickel is also an important microelement for humans which acts as a cofactor for some of the enzyme\u0026rsquo;s proteins that contains metal ions and in humans it helps in the iron metabolism [20].\u003c/p\u003e\n\u003cp\u003eDuring the last 20 years, vast progress has been observed in the molecular mechanisms implicit in the significant agronomic features. To achieve high yield for a crop, flowering time is a captious trait through the use of maximum sunlight and temperature. Others agronomical traits that contribute to crop production are plant height, tiller number and panicle architecture. Panicle number is determined by the growth of tiller that has a direct effect on the yield of crop. Tiller quality make up the foundation of lodging tolerance and significance of cereal plant fiber as animal food [21]. Multiple genes control the most agronomic traits in rice which show complicated and numerical inheritance. Molecular breeding is beneficial to recognize the Quantitative Trait Loci (QTLs) associated with the desired traits, as QTL controlling agronomic traits are labeled by DNA markers and introduced into the elite cultivars by Marker Assisted Selection ((MAS). The benefits of SSR markers are accessible and potent in breeding new rice cultivars to improve important agronomic traits, like plant height, maturity, seed shattering, amylose content, yield and resistance to disease and environmental stresses [22].\u003c/p\u003e\n\u003cp\u003eGenes that are linked to drought stress in rice have been identified through the characterization and screening of rice genotypes genetically and morphologically, to determine the genetic variation in them. Through molecular marker technology quantitative traits are now possible to be identified [23]. Molecular markers help to identify polymorphism in rice genomes and are very useful to study the diversity at genetic level [24]. In order to identify the enhanced genotypes for the quantitative and qualitative traits the study of Agro-morphological diversity is very important for the germplasm of local and exotic varieties. It is very helpful to determine the best genotypes that can be promoted further for the yield purposes [22].\u003c/p\u003e\n\u003cp\u003ePresently in plant genetics and breeding studies, microsatellites or Simple Sequence Repeats (SSR) are widely used and chosen markers after Single Nucleotide Polymorphism (SNPs), due to their large genome coverage, comparative abundance, high reproducibility, co-dominant inheritance, able to differentiate both inter- and within-species variations, and simple to analyze them [25, 26]. There are several applications for which the SSRs markers are being used that includes; (i) genome mapping, (ii) to assess genetic diversity and linkage between different cultivars in aromatic and non-aromatic rice, (iii) for the estimation and testing of purity in varieties, (iv) to determine genetic relatedness among several sub species [27). For rice, SSR markers for any genome part or region are easily available and the candidate gene markers can easily be developed. The molecular markers are very important to the rice cultivars for the purpose of improvement and possess so many applications in the field of agriculture [28]. The objectives of this study is to examine the genetic diversity through SSR markers, seed characterization and agro-morphological study amongst twelve rice genotypes.\u003c/p\u003e"},{"header":"Material And Methods","content":"\u003cdiv class=\"Section2\" id=\"Sec3\"\u003e\n \u003ch2\u003ePlant material and experimental design\u003c/h2\u003e\n \u003cp\u003eThe current research was carried out at the Institute of Biotechnology and Genetic Engineering (IBGE), The University of Agriculture Peshawar, Pakistan. The research was performed on local rice genotypes including ten basmati and two non-basmati varieties (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The selected varieties were evaluated for agro-morphological traits and genetic diversity. The rice varieties included ten aromatic and two non-aromatic rice genotypes under the Randomized Complete Block Design. The rice seeds were collected from Plant Genetic Resources Institute (PGRI), National Agriculture Research Center (NARC), Islamabad, Pakistan. Seeds were grown in pots in screen house at IBGE under normal growth condition. As soon the nursery was established, uniform seedlings were transferred to the field.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u0026nbsp;\u003ctable border=\"1\" id=\"Tab1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eList of selected local Pakistani rice varieties\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS.no\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVarieties names\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariety Type\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVarietal group\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBasmati Surkh-161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBasmati\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAromatic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBasmati-385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBasmati\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAromatic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBasmati-2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBasmati\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAromatic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBasmati-198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBasmati\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAromatic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSuper-basmati\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBasmati\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAromatic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDilrosh-97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBasmati\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAromatic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHansraj\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBasmati\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAromatic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKS-282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBasmati\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAromatic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKSK-133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBasmati\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAromatic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSara Sella\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBasmati\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAromatic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRatua-81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAus/Boro\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon aromatic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBamla Sufaid-320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAus/Boro\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon aromatic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u0026nbsp;\u003ctable border=\"1\" id=\"Tab2\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eList of SSR Markers, primers, oligo sequences and expected size of PCR product\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS.No\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMarkers\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePrimers\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOligo Sequence (5\u0026apos; to 3\u0026apos;)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eChr. No.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProduct size\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRM28099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eForwardReverse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTGT GCG GAT GCG GGT AAG TCC\u003c/p\u003e\n \u003cp\u003eCCA CCT GTC AAC CAC CGA AAC C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e210\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRM28089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eForwardReverse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGGG AGG ACA CCT GTG TAA GTA GG\u003c/p\u003e\n \u003cp\u003eGGT TCA AAT GAG ACC CAA TTC C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e290\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRM315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eForward\u003c/p\u003e\n \u003cp\u003eReverse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGAG GTA CTT CCT CCG TTT CAC\u003c/p\u003e\n \u003cp\u003eAGT CAG CTC ACT GTG CAG TG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRM28130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eForward\u003c/p\u003e\n \u003cp\u003eReverse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCAG CAG ACG TTC CGG TTC TAC TCG\u003c/p\u003e\n \u003cp\u003eAGG ACG GTG GTG GTG ATC TGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e190\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRM211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eForward\u003c/p\u003e\n \u003cp\u003eReverse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCCG ATC TCA TCA ACC ACC TG\u003c/p\u003e\n \u003cp\u003eCTT CAC AAG GAT CTC AAA GG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003ch2\u003eAgro-morphological Traits\u003c/h2\u003e\n\u003cp\u003eThe data were recorded on plant height (cm), number of panicles plant-\u003csup\u003e1\u003c/sup\u003e, main panicle length (cm), primary and secondary branches panicle\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, filled grains panicle\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, total number of seeds panicle\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, days to maturity, seed morphology and quality, 1000 grains weight (gm) of rice genotypes.\u003c/p\u003e\n\u003ch2\u003eDetermination Of Seed Quality\u003c/h2\u003e\n\u003cp\u003eThe heavy metal contents in samples of rice grains were measured after acid digestion (wet digestion) by following the protocol [\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e]. ground plant sample (0.5 g) was taken in (250 ml) calibrated Pyrex flask with (10 ml) of HNO3 solution and kept overnight and 4 ml of per chloric acid solution was added. The mixture was heated on hot plate under fume hood until the digest became cleared. After heating the flask\u0026rsquo;s, distilled water was added to make the volume up to 100ml. Heavy metal concentration in plant extract was measured by atomic absorption spectrophotometer.\u003c/p\u003e\n\u003ch2\u003eDna Extraction And Pcr Amplification\u003c/h2\u003e\n\u003cp\u003eGenomic DNA extraction from Rice cultivar was carried out by CTAB method described by [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e]. A fresh young leaves of 1g was taken and ground to powder form in liquid nitrogen. Sample was transferred to 1.5ml Eppendorf tube and 500\u0026micro;l of CTAB buffer was added. Sample was incubated in water bath at 60\u0026deg;C for 30 min and was vortexed after every 5min. Tubes were centrifuged at 12000 rpm for 15 min and supernatant was transferred to a new 1.5ml Eppendorf tube and 500\u0026micro;l of Phenol:Chloroform: Isoamylalcohol (25:24:1) solution was added. After centrifugation at 12000 rpm for 10 min, the upper aqueous phase was transferred to a new tube. After that 600\u0026micro;l ice cold isopropanol was added for the purpose of precipitation. Sample was centrifuged again at 12000 rpm for 2 min and supernatant was discarded without disturbing the pellet. The pellet was washed twice with 500\u0026micro;l of 70% ethanol. Tubes were dried at room temperature. Sample was resuspended in 50\u0026micro;l TE buffer. The extracted DNA quality and optical density were measured at a wavelength of 260/280 nm by using Nanodrop and was stored at -20\u003csup\u003eo\u003c/sup\u003eC for future use.\u003c/p\u003e\n\u003ch2\u003eMolecular Diversity Screening\u003c/h2\u003e\n\u003cp\u003eTo study the genetic diversity a total set of five SSR markers were selected from Gramene Markers Database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://archive\u003c/span\u003e\u003c/span\u003e. gramene.org/ markers/) and were used to determine the genetic diversity among the selected rice genotypes as shown in (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003ch2\u003ePolymerase Chain Reaction (Pcr)\u003c/h2\u003e\n\u003cp\u003eThe PCR reaction was performed as described (31]. A toral of 10 \u0026micro;l reaction mixture containing 1 \u0026micro;l DNA template, 1 \u0026micro;l of each forward and reverse primer, 1.8 \u0026micro;l of nuclease free water, 0.2 \u0026micro;l of \u003cem\u003eTaq\u003c/em\u003e DNA polymerase and 5 \u0026micro;l of PCR master mix. PCR conditions was set with an initial denaturation step of 5 min at 94\u0026deg;C followed by 35 cycles, each comprises of a denaturation at 94\u0026deg;C for 30 Sec., annealing at an appropriate temperature for 30 sec and elongation step of 1min/kb for 72\u0026deg;C, and final extension at 72\u0026deg;C for 10 min. The BIO-RAD T100 thermal cycler was used.\u003c/p\u003e\n\u003ch2\u003eGel Electrophoresis\u003c/h2\u003e\n\u003cp\u003eThe amplified PCR product were confirmed using 2% Agarose gel. The agarose gel was prepared in 1X TBE buffer that was mixed and heated for 2 min in microwave oven. The gel was solidified by cooling for 20 min and then transferred to gel tank. The product size of different genotypes was determined by comparing with 100bp DNA ladder. The gel was run for 45 to 50 min at 110V. The PCR products were visualized by using 2ul of Ethidium Bromide and checked under UV light Trans illuminator and documented.\u003c/p\u003e\n\u003cdiv class=\"Section2\" id=\"Sec10\"\u003e\n \u003ch2\u003eData analysis\u003c/h2\u003e\n \u003cp\u003eMolecular and morphological data was compiled in MS Excel and Popgene 3.5 version was used to find out Allelic Frequency, PIC, Heterozygosity and Homozygosity and Genetic diversity. The UPGMA procedure was used to construct dendrogram using the Popgene.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eEvaluation of rice cultivars for agro-morphological traits\u003c/h2\u003e \u003cp\u003eMorphological traits of twelve Pakistani rice cultivars were evaluated and data was taken after harvesting time. The highest plant height (204.3 cm) and panicle length (40.1 cm) were observed for Hans-raj, whereas the maximum number of panicles was noted for dilrosh-97(47.3). The maximum number of primary and secondary panicle branches were observed in Ratua-81 (15) and super basmati (62), respectively. Super basmati showed highest number of filled grains (264.3) and total number of grains per panicle (315). The minimum days to maturity was exhibit by ratua-81 (140 days), and highest 1000 grains weight was recorded for Ksk-133 (27g) which exhibited highly significant variation for agro-morphological parameters among the rice varieties (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMean performances of twelve rice varieties for different morpho-yield parameters using LSD test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGenotypes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePlant height (cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNumber of panicles per plant\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMain Panicle length (cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePrimary branches per panicle\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSecondary branches per panicle\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFilled seeds per panicle\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTotal seeds per panicle\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDays to maturity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1000 grains weight (gm)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDilrosh-97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95.67 H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47.333 A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.067 E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.333 D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19.333 DE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e85.00 F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e115.33 F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e141.33 H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e24.300 D\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBasmati-198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e162.67 CDE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.667 C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31.667 BC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.000 ABC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e48.667 B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e228.67 BC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e246.33 BC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e161.00 B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e19.233 H\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBasmati-2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e150.33 EFG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.333 BCD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.333 CD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e24.667 D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e82.67 F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e164.67 E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e150.00 D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e21.767 F\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKsk-133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92.00 H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.333 DE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.300 DE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.000 CD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.333 E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e89.00 F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e94.67 F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e143.67 G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e27.000 A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHansraj\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e204.33 A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42.667 AB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.100 A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.333 BC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e48.333 B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e235.33 BC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e255.00 B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e149.33 D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e24.467 D\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRatua-81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e190.00 B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.333 B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38.167 A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.000 A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e51.667 B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e245.33 AB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e268.67 B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e140.00 I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e20.067 G\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBasmati-385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e146.00 FG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43.667 A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32.100 B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.667 ABC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e49.000 B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e240.67 AB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e260.33 B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e145.00 F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e20.100 G\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSuper basmati\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e141.00 G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.333 B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37.000 A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.667 AB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e62.000 A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e264.33 A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e315.00 A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e158.67 C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e21.700 F\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKs-282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92.67 H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.000 G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.700 CD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.000 CD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23.333 DE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e124.00 E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e147.67 E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e144.00 G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e24.733 C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBamla-Sufaid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e157.00 DEF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.667 CD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.333 BCD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.667 CD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e36.000 C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e210.67 C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e220.00 CD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e144.33 FG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e25.167 B\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBasmati-Surkh-161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e169.00 CD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.333 DE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37.500 A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.667 ABC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e33.000 C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e163.33 D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e198.67 D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e147.67 E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e22.600 E\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSara sella\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e175.67 C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.667 FG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31.667 BC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.000 CD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e47.667 B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e222.33 BC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e256.00 B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e169.67 A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e22.533 E\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLSD 0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.8428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.0553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.4022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.6170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e26.612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e26.435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.8294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.1897\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch2\u003eGrains Characterization Of Rice Genotypes\u003c/h2\u003e\n\u003cp\u003eThe seed quality analysis including, minerals content of the rice seeds, including iron, zinc and manganese and heavy metals, including lead, copper and nickel were evaluated.\u003c/p\u003e\n\u003ch2\u003eEstimation Of Iron (Fe)\u003c/h2\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIron content in rice grains from 12 different genotypes analyzed, has shown average values ranged from 46-190.3 \u0026micro;g/g (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The highest content of iron was found in seeds of Hansraj (190.3\u0026micro;g/g), followed by basmati-198 (173 \u0026micro;g/g) and super basmati (147.6 \u0026micro;g/g). While the lowest content of iron was found in bamla sufaid (46 \u0026micro;g/g) followed by basmati-385 (74 \u0026micro;g/g) and ksk-133(76.3 \u0026micro;g/g). ANOVA showed highly significant difference (LSD: 6.8956) for iron content in seeds of 12 rice genotypes (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMean of twelve basmati rice genotypes for different elemental analysis using LSD test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVarieties\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIron (Fe) \u0026micro;g g-1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZinc (Zn) \u0026micro;g g-1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eManganese (Mn) \u0026micro;g g-1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCopper (Cu) \u0026micro;g g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLead (Pb) \u0026micro;g g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNickel (Ni) \u0026micro;g g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDilrosh-97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77.67 H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.233 H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57.33 D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e303.33 F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e972.3 I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e160.67 D\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBasmati-198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85.33 G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.333 G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57.67 D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e159.33 J\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1175.0 H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e114.67 G\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBasmati-2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e173.00 B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.000 G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e69.00 C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e358.00 E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3055.7 D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e141.33 F\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKsk-133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76.33 H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.333 E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60.33 D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e193.00 H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2221.0 E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e149.00 E\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHansraj\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e190.33 A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.333 F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e71.67 C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e856.33 A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3148.7 C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e25.30 K\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRatua-81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e152.33 C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.000 D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56.67 D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e227.33 G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e547.3 K\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e100.33 H\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBasmati-385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104.67 F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.000 B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e108.00 A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e374.67 D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1233.0 G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e71.67 I\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSuper basmati\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e147.67CD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.000 C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41.67 E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e384.67 D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3883.3 A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e241.33 B\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKs-282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e141.33 D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.000 A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e87.67 B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e545.00 B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1350.7 F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e203.00 C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBamla-sufaid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.00 I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.667 G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e111.33 A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e183.33 I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3560.0 B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e146.67 EF\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBasmati-surkh-161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74.00 H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.667 G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76.00 C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e165.33 J\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1311.3 F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e314.67 A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSara sella\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e128.33 E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.200 D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46.07 E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e158.33 J\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e706.0 J\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e45.67 J\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLSD 0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.8956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.7328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.4418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.3186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e54.378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.7480\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eShows band pattern, polymorphic bands and polymorphism of 5 markers among 12 rice varieties\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBands pattern\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePolymorphic bands\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePolymorphism (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRM28099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRM28089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRM315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRM28130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRM211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTOTAL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e11\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e11\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e20%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch2\u003eEstimation Of Zinc (Zn)\u003c/h2\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eData regarding Zinc content in seeds of 12 rice varieties, the average concentration ranged from 7.2\u0026ndash;44 \u0026micro;g/g (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The highest content of Zn was found in Ks-282(44 \u0026micro;g/g), followed by basmati 2000 (35 \u0026micro;g/g) and super basmati (29 \u0026micro;g/g), while the lowest was recorded for dilrosh-97(7.2 \u0026micro;g/g) followed by basmati-385(10.6 \u0026micro;g/g) and basmati-198 (11 \u0026micro;g/g). ANOVA showed highly significant difference (LSD: 2.7328) for zinc content among the studied genotypes (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e\n\u003ch2\u003eEstimation Of Manganese (Mn)\u003c/h2\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe average concentration of manganese in seeds of 12 rice varieties ranged from (111.3\u0026ndash;41.6 \u0026micro;g/g) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The highest content of manganese was found in Bamla sufaid (111.3 \u0026micro;g/g), followed by basmati-2000(108 \u0026micro;g/g) and ks-282(87.6 \u0026micro;g/g). While the lowest concentration was found in super basmati (41.6 \u0026micro;g/g) followed by sara sella (46.0 \u0026micro;g/g) and dilrosh-97(57.3 \u0026micro;g/g). ANOVA showed highly significant difference (LSD: 7.4418) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e\n\u003ch2\u003eEstimation Of Copper (Cu)\u003c/h2\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe average concentration of copper in rice grains from 12 genotypes varied from 158.3-856.3 \u0026micro;g/g (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The highest concentration of copper was recorded in Hansraj (856.3 \u0026micro;g/g), followed by ks-282 (545 \u0026micro;g/g) and super basmati (384.6 \u0026micro;g/g). While the lowest content of copper was noted in sara sella (158.3 \u0026micro;g/g), followed by basmati surkh-161 (159.3 \u0026micro;g/g) and basmati-385(165.3 \u0026micro;g/g). ANOVA showed highly significant difference (LSD: 9.3186) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e\n\u003ch2\u003eEstimation Of Lead (Pb)\u003c/h2\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe average concentration of Pb in seed samples ranged from 547.3-3883.3 \u0026micro;g/g (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The highest content of lead was recorded in super basmati (3883.3 \u0026micro;g/g), followed by bamla sufaid (3560 \u0026micro;g/g) and hansraj (3148.6 \u0026micro;g/g), while the lowest content of lead was noted in ratua-81 (547.3 \u0026micro;g/g), followed by the variety of sara sella (706 \u0026micro;g/g) and dilrosh-97 (972.3 \u0026micro;g/g). ANOVA resulted highly significant difference (LSD: 54.378) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e\n\u003ch2\u003eEstimation Of Nickel (Ni)\u003c/h2\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe average concentration of nickel, varied from 25.3-314.6 \u0026micro;g/g among 12 genotypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The highest content of nickle was recorded in basmati-385 (314.6 \u0026micro;g/g), followed by super basmati (241.3 \u0026micro;g/g) and ks-282 (203 \u0026micro;g/g), while the lowest content of nickel was noted in hansraj (25.3 \u0026micro;g/g), followed by sara sella (45.6 \u0026micro;g/g) and basmati-2000 (71.6 \u0026micro;g/g). ANOVA showed highly significant difference (LSD: 6.7480) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eGenetic diversity based on SSR markers\u003c/h2\u003e \u003cp\u003eGenetic diversity among twelve rice cultivars were detected utilizing 5 SSR markers with a toral of 60 allelic band with highly polymorphism were recorded. RM28099 primer exhibited 12 allelic bands with two patterns of polymorphism. The allelic frequency observed for allele A was 0.7500 and for allele B, it was 0.2500. PIC value for RM28099 was recorded as 0.29 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). RM28089 produced a total 12 allelic bands were amplified, which comprising 3 band patterns (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The allelic frequencies recorded for allele A, B and C were 0.2917, 0.5417 and 0.1667, respectively. PIC value for RM28099 was recorded to be 0.34. RM315, produced 12 allelic bands and 2 band patterns. The allelic frequency for allele A was recorded as 0.7273, while for allele B, it was 0.2727. PIC value for RM315 was recorded as 0.27 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). RM28130 primers showed overall 12 allelic bands and 2 band patterns were observed. The allelic frequency recorded for allele A, among 12 rice varieties were 0.6667 and for allele B, showed 0.3333. PIC value for RM28130 was recorded as 0.60 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Marker RM211 amplified 12 allelic bands with 2 band patterns. The allelic frequency recorded for allele A and B was 0.7500 and 0.2500, respectively. PIC value for RM211 was recorded as 0.29 (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOverall allelic frequency\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLocus/alleles\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRM28099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.7500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRM28099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRM28089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2917\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRM28089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5417\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRM28089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1667\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRM315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.7273\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRM315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2727\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRM28130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6667\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRM28130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3333\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRM211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.7500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRM211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive Statistics of multi-populations\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocus\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNa\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eObs. Hetro\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eExp. Hetro\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePIC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimer RM28099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.5623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.6000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.3913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimer RM28089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.9901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.4615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.6196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimer RM315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.5860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.6575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.4156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimer RM28130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.6365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.8000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.4638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimer RM211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.5623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.6000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.3913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003ena\u0026thinsp;=\u0026thinsp;Observed number of alleles\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003ene\u0026thinsp;=\u0026thinsp;Effective number of alleles [Kimura and Crow (1964)]\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eI\u0026thinsp;=\u0026thinsp;Shannon's Information index [Lewontin (1972)]\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNei's (1973) expected heterozygosity\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOverall allele frequency\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAllele/locus\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimer RM28099\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrimer RM28089\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePrimer RM315\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePrimer RM28130\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePrimer RM211\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.7500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.6667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.7500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.2500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.2500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch2\u003eOverall Allelic Frequency\u003c/h2\u003e\n\u003cp\u003eIn current study, RM28099 exhibited two alleles A, B was observed with highest frequency of 0.7500 and lowest frequency of 0.2500, respectively. For RM28089, three alleles were observed with highest frequency for allele B (0.5417), followed by allele A (0.2917) and the lowest frequency was observed for allele C (0.1667). For RM315 the highest frequncey was noted for allele A (0.7273) and lowest frequency was recorded for allele B (0.2727). For RM28130, the highest frequency was observed for allele A (0.6667) and lowest frequency was noted for allele B (0.3333). For RM211 the highest allelic frequency was recorded for allele A (0.7500) and lowest allelic frequency was observed for allele B (0.2500) (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\n\u003ch2\u003eGenetic Relationship Of The Rice Germplasm\u003c/h2\u003e\n\u003cp\u003eDendrogram constructed based on similarity coefficient for the estimation of genetic relationship using Unweight Pair Group Method with Arithmetic Means (UPGMA) cluster analysis, which are based on Nei's Genetic distance Modified from NEIGHBOR procedure of PHYLIP Version 3.5 Pop-Gene software. All the genotypes were divided into two main groups A and B. Group A was further divided into sub groups A1 and A2. Group A1 included the varieties dilrosh-97 and sara sella. Group A2 divided into further subgroups A2a, A2b and A2c. The A2a group included ksk-133, A2b included ratua-81 and super basmati varieties. While the A2c contained basmati-385, bamla sufaid-121and basmati surkh-161. Group B is divided into sub groups B1 and B2. B1 group included basmati 198, basmati 2000 and hansraj, whereas the B2 group included only KS 282 variety. The maximum genetic distance (41.406%) was noted for varieties basmati surkh-161 and basmati-2000. While the least genetic distance (0.564%) was observed for varieties that includes KS-282, and BASMATI-2000, and no genetic distance (0.000%) was observed between the varieties Basmati-385, Bamla sufaid, and basmati surkh-161 as they belonged to the same origin of genotype (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e04\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003ePakistan is the 11th largest producer and 4th largest exporter of rice, which is cultivated under different climatic zones; however, very little is known in terms of rice genetic diversity in Pakistan with respect to global germplasm [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. A broad base genetic diversity having a wide range of genetic variations in genome is crucial to provide tolerance against biotic and abiotic stresses. Germplasm serves as the main source of genetic variation that could be utilized by the breeders in the future for the enhancement of crops through breeding programs or hybridization. For the improvement and development of any variety the main source for crop breeding programs is the germplasm due to the conserved characters needed for any good crop yield production, tolerance against environmental stresses and for enhanced crop quality. To monitor and evaluate the diversity and some essential genes among the crops, the assessment of germplasms collections is very indispensable [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] Germplasms plays a vital role for the development of novel varieties and to secure our food. The developed varieties contain a distinct genic trait responsible for combating against all the diseases, insects and have improved nutritional value. Rice germplasm including landraces, traditional varieties, breeding lines and elite genotypes. Worldwide, around 4,500,000 accessions of plants have been collected and specifically in rice about 400,000 accessions are reported [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. For the estimation of rice germplasm, IRRI has presented more than 1000 genotypes yearly [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. In the present the qualitative and quantitate agro-phenotypic characteristics has been showed highly phenotypic variation included plant height, number of panicles per plant, main panicle length, primary and secondary branches per panicle, filled grains per panicle, total number of seeds per panicle, days to maturity, seed quality and 1000 grains weight. Super basmati showed maximum number of secondary branches per panicle (62), maximum number of filled grains (264.3] and total grains per panicle (315). The crucial and main agronomic character to achieve the improved rice production is the grain yield under favorable and unfavorable environments [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. A previous study displayed similar results with high significant variation in all the 14 traits of rice germplasms [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eResearchers investigated the copper concentration in rice genotypes and observed high content in rice root [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In the current study the quality analysis of rice grains of twelve rice varieties ware analyzed, total six micronutrients included iron, zinc, manganese, copper, lead and nickel. The highest content of iron was found in seeds of Hansraj, the highest Zn concentration was found in Ks-282, highest manganese and lead were found in super basmati, highest concentration of copper was recorded in Hansraj, and highest content of nickle was recorded in basmati \u0026minus;\u0026thinsp;385. Highly significant variation was recorded for elements in all the genotypes of the rice. The results of this study are consisted with previous work [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] where they worked on nickel metal concentration in milled rice grain and found the significant variation. Moreover, our results are also in line with the study of [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], who analyzed eleven aromatic and one non aromatic rice genotypes for micronutrients (Zn, Mn, Fe, Cu). [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] investigated lead content in rice grains.\u003c/p\u003e \u003cp\u003eIt is prominent to explore the genetic variation within a rice population, for the management and utilization of rice germplasm. That could be further used for the improvement and development of new rice varieties [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Traditionally the use of morphological or physiological traits were used to evaluate the genetic diversity in plants but this technique was not trustworthy as this procedure was dependent upon the environment due to the gene expression influenced by it. The scientists approached an alternative way to figure out this problem and they introduced the use of molecular markers. Molecular markers can be used to assess the genetic diversity among the varieties of rice, as well as the quantitative and inherited traits can also be analyzed through it [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. In the present study total of five SSR primers were used in order to identify the genetic variation among the 12 local rice genotypes. The highest number of polymorphic alleles (three alleles) were observed for RM28089. While the remaining markers showed less number of alleles i.e. 2 polymorphic alleles each. These markers resulted in 20% polymorphism among all the rice genotypes. [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] identified the genetic variation and characterized 15 varieties using 30 different SSR primers that revealed distinct polymorphism between the studied varieties. The number of band pattern in the current study corresponded well with the study [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The PIC values varied from 0.27 to 0.60 with average value of 0.358. The highest PIC value (0.60) was observed for RM28130. In a previous study [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] has shown similar results with PIC values ranged from 0.0476 to 0.5993 with average of 0.3785 using 24 SSR markers. PIC value shows the variation, diversity and frequency among the genotypes of the varieties or cultivars [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The 12 rice genotypes were divided in to mainly two groups, i.e. A and B based on UPGMA cluster analysis. The highest genetic distance 41.4% was recorded among the varieties of basmati-2000 and basmti surkh-161 having same geographical origin.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe current study observed highly significant difference for the agro-morphological traits including plant height, number of panicles per plant, main panicle length, primary and secondary panicle branches, filled grains per panicle, total number of seeds per panicle, days to maturity, and 1000 grains weight. The highest number of filled grains and total number of grains per panicle was noted in super basmati, which indicated the potential adaptation of this variety to the prevailing high temperature during the growth period. Basmati Hansraj, Ks-282, Super basmati and Basmati surkh-161 were found to be highly rich in micronutrients (iron, zinc, copper, lead and manganese). Based on the molecular markers assessment, the 12 local varieties cluster into two groups. The maximum genetic distance was observed in the varieties of basmati-2000 and basmati surkh 161. RM28310 was highly informative having maximum PIC value, whereas highest polymorphic alleles (three) were observed for RM28089. Based on current research study it is recommended that broad genetic background especially highly yield contributing components, super basmati is recommended to be included in breeding program. Rice varieties may further be characterized for nutrients uptake ability and its underlying mechanism.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eQTLs \u0026nbsp; Quantitative trait loci\u003c/p\u003e\n\u003cp\u003eSSR \u0026nbsp; \u0026nbsp; \u0026nbsp; simple sequence repeats\u003c/p\u003e\n\u003cp\u003eCTAB \u0026nbsp; \u0026nbsp;cetyltrimethylammmonium bromide\u003c/p\u003e\n\u003cp\u003eUPGMA Unweight Pair Group Method with Arithmetic Means\u003c/p\u003e\n\u003cp\u003ePIC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;polymorphic information content\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u0026nbsp;\u003c/strong\u003eThe authors have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e This article does not contains any study with human participants or animals performed by any of the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publication\u003c/strong\u003e Not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cspan\u003eSaski T, Burr B (2000) International Rice Genome Sequencing Project: the effort to completely sequence the rice genome. 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Agricultural Information and Communication Center (AICC), moad.181\u0026ndash;208\u003c/span\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"genetic-resources-and-crop-evolution","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gres","sideBox":"Learn more about [Genetic Resources and Crop Evolution](https://www.springer.com/journal/10722)","snPcode":"10722","submissionUrl":"https://submission.nature.com/new-submission/10722/3","title":"Genetic Resources and Crop Evolution","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Genetic diversity, SSR markers, agro-morphological, Biochemical, Oryza sativa","lastPublishedDoi":"10.21203/rs.3.rs-1736575/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1736575/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eBroad base genetic diversity is essential for a sustainable crop yield to provide tolerance against biotic and abiotic stresses. In Pakistan, rice is ranked second as a staple food and cultivated under different climatic zones. However, very little is known in terms of rice genetic diversity.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods and results:\u003c/strong\u003e Present research were performed to evaluate the genetic diversity among 12 local rice varieties by using 5 SSR markers. Furthermore, agro-morphological parameters and seed characterization of these varieties were studied. The highest plant height (204.3 cm) and panicle length (40.1 cm) were observed for Hans-raj, the maximum number of panicles were recorded for dilrosh-97 (47.3).\u0026nbsp;The maximum number of primary and secondary panicle branches were observed in Ratua-81 (51) and super basmati (62), respectively. Super basmati showed highest number of filled grains (264.3) and total number of grains per panicle (315). The minimum days to maturity was recorded for ratua-81 (140 days), and highest 1000 grains weight was recorded for Ksk-133 (27g). The highest concentration of elements, e.g. zinc (Zn) was observed in Ks-282 (44µg/g), Iron (Fe) in hansraj (190.3µg/g), manganese in bamla sufaid (111.3µg/g), copper (Cu) in hansraj (856.3µg/g), lead (Pb) in super basmati (3883.3µg/g), and nickel (Ni) was found in basmati-385 (314.6ug/g). For the genetic diversity analysis, five SSR markers were used and a total of 60 alleles were amplified with 20% polymorphism, RM28130 showed the highest PIC value (0.60). The maximum number (3) of alleles were produced by RM28089. Based on secondary panicle branches and total number of filled grains, Super basmati showed best panicle architecture and grain yield, which can be used in breeding program to develop high yielding aromatic rice genotypes.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eThe highest number of filled grains and total number of grains per panicle were observed in super basmati, which indicated the potential adaptation. Basmati Hansraj, Ks-282, Super basmati and Basmati surkh-161 were found to be highly rich in micronutrients (iron, zinc, copper, lead and manganese). The maximum genetic distance was observed in basmati-2000 and basmati surkh 161 genotypes.\u003c/p\u003e","manuscriptTitle":"Evaluation of the Agro-morphological traits, seed characterization and the genetic diversity of local Rice (Oryza sativa L.) Varieties of Pakistan","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-07-11 18:19:06","doi":"10.21203/rs.3.rs-1736575/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2022-07-03T13:22:27+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2022-07-03T08:01:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-06-13T06:22:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"Genetic Resources and Crop Evolution","date":"2022-06-08T01:39:25+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"genetic-resources-and-crop-evolution","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gres","sideBox":"Learn more about [Genetic Resources and Crop Evolution](https://www.springer.com/journal/10722)","snPcode":"10722","submissionUrl":"https://submission.nature.com/new-submission/10722/3","title":"Genetic Resources and Crop Evolution","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"44246829-2074-49b0-8da5-4799a192eb56","owner":[],"postedDate":"July 11th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2022-09-25T13:39:21+00:00","versionOfRecord":[],"versionCreatedAt":"2022-07-11 18:19:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1736575","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1736575","identity":"rs-1736575","version":["v1"]},"buildId":"rHA-KDH7Qsr4HCuvH75dn","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}