Background Rice breeders stand at the forefront for application and advancement in breeding and genome based marker system for more realistic and applicable strategies in order to create opportunities for sustainable utilization of genetically diverse rice resources. In Kashmir rice cultivation is under diverse environmental conditions that is encountered by selection pressures of environmental heterogeneity, biotic and abiotic stresses, however competent enough to provide good yields, whereby drawing the attention of the breeder. Therefore, tremendous genetic differentiation and diversity has occurred at various agro-ecosystems. Methods and Results This study is a pioneering effort where agro-morphological and SSR markers has been employed to assess the genetic diversity and genetic structure of advanced rice breeding lines and local collections from northern Himalayan region of India along with screening for disease resistance. In the present investigation, a total of 15markers (12 polymorphic SSR markers and three gene specific markers) were used for agro-morphological characterization and genetic differentiation of 48 rice genotypes (40 advanced breeding lines and eight cultivated varieties). The genotypes were evaluated under two environments; Khudwani (E1) and Wadura (E2) during Kharif 2020. Results based on agro-morphological and cooking quality traits revealed that 48 genotypes got grouped into seven clusters with KS11 and KS7 at the extremes. The cluster I was the largest comprising of 13 genotypes followed by cluster III (11 genotypes), cluster II and cluster IV had 9 genotypes each. ANOVA also revealed significant mean squares for the genotypes under study with respect to all the traits in two environments (E1and E2). From principal component analysis (PCA) only six principal components (PCs) exhibited more than 1.00 Eigen value and explained 71.44 % cumulative variability among the traits studied. The result from the calculation of SSR molecular marker was further verified with clustering analysis, genetic diversity parameters, AMOVA, phenotypic characterization and validation based on gene specific marker analysis. The cluster analysis revealed wide genetic variability among the 48 genotypes with Cluster III comprises of 19 genotypes, Cluster I with 17 genotypes, whereas cluster II comprised of 12 genotypes. The genetic profiles detected 53 alleles from these 15 loci, with PIC values of 0.494 per locus. Based on AMOVA, variation was distributed within population 99% and among populations no significant genetic differentiation was observed. The average number of effective alleles (Ne) was 1.38 with higher effective alleles in Population-1 (1.52) than Population-2 (1.24). Based on phenotypic characterization, most of genotypes along with two resistant checks (DHMAS and Shalimar Rice-1) displayed resistant reaction, followed by 16 genotypes showing moderately resistant while two landraces viz., Mushk Budji and Red Rice proved highly susceptible. Out of 48, three and nine genotypes were positive for gene Pikm with respect to marker Ckm-2 and dominant marker Pikh-STS, respectively. Moreover, this low level differentiation among sub-species could provide an opportunity to identify the gene combination well-adapted by natural selection. Conclusion The pattern of clustering based on SSR markers provided information about shared genetic characters among rice genotypes in order to eliminate duplications between rice genotypes. Such a genetic differentiation within genotypes provides an insight towards selective pressure and evolution adaptation to local conditions and could be utilized for introgression of resistant gene for higher yield potential, and development of rice varieties with better plant types as per the preferences of rice consumers.

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