SSR Marker-Based Molecular Diversity and Genetic Characterization of Sesame (Sesamum indicum L.) Germplasm

Sesame (Sesamum indicum L.) is an important oilseed crop, and assessment of its genetic diversity can inform the selection of parental material for crop improvement. This study evaluated molecular variation among 45 sesame genotypes, including two check varieties, using 30 sesame-specific simple sequence repeat (SSR) markers. Genomic DNA was extracted from seven-day-old seedlings, and amplified SSR profiles were assessed for allele number and marker informativeness. Genetic relationships were evaluated using Jaccard’s dissimilarity coefficients and unweighted pair group method with arithmetic mean (UPGMA) clustering. Of the 30 SSR markers examined, 18 (60%) were polymorphic, whereas 12 were monomorphic. The polymorphic loci generated 58 alleles, with two to five alleles per locus and a mean of 3.22. Polymorphism information content values ranged from 0.043 to 0.745, with an overall mean of 0.474. Hs425, Hs635, SiSSM61314 and Si24 showed comparatively high marker informativeness. UPGMA analysis assigned the genotypes to three molecular clusters. Cluster I comprised 36 genotypes, Cluster II contained eight genotypes divided into two subclusters, and Cluster III contained only TPTS-69. The isolated position of TPTS-69 indicated a distinct molecular profile within the marker panel evaluated. The observed allelic variation and clustering patterns provide information for identifying relatively differentiated sesame germplasm and selecting materials for further validation and potential use in breeding programmes.

Authors

Institutions

Publication Details

Journal
Asian Journal of Current Research
Published
2026-11-04
DOI
https://doi.org/10.56557/ajocr/2026/v11i411323
Primary Topic
Sesame and Sesamin Research
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

SSR Marker-Based Molecular Diversity and Genetic Characterization of Sesame (Sesamum indicum L.) Germplasm

B. Ramana Murthy, N. Sabitha, D. Bharathi, C. Anjana
Asian Journal of Current Research
Sesame and Sesamin Research
article

SSR Marker-Based Molecular Diversity and Genetic Characterization of Sesame (Sesamum indicum L.) Germplasm

B. Ramana Murthy, N. Sabitha, D. Bharathi, C. Anjana
article en

Abstract

Sesame (Sesamum indicum L.) is an important oilseed crop, and assessment of its genetic diversity can inform the selection of parental material for crop improvement. This study evaluated molecular variation among 45 sesame genotypes, including two check varieties, using 30 sesame-specific simple sequence repeat (SSR) markers. Genomic DNA was extracted from seven-day-old seedlings, and amplified SSR profiles were assessed for allele number and marker informativeness. Genetic relationships were evaluated using Jaccard’s dissimilarity coefficients and unweighted pair group method with arithmetic mean (UPGMA) clustering. Of the 30 SSR markers examined, 18 (60%) were polymorphic, whereas 12 were monomorphic. The polymorphic loci generated 58 alleles, with two to five alleles per locus and a mean of 3.22. Polymorphism information content values ranged from 0.043 to 0.745, with an overall mean of 0.474. Hs425, Hs635, SiSSM61314 and Si24 showed comparatively high marker informativeness. UPGMA analysis assigned the genotypes to three molecular clusters. Cluster I comprised 36 genotypes, Cluster II contained eight genotypes divided into two subclusters, and Cluster III contained only TPTS-69. The isolated position of TPTS-69 indicated a distinct molecular profile within the marker panel evaluated. The observed allelic variation and clustering patterns provide information for identifying relatively differentiated sesame germplasm and selecting materials for further validation and potential use in breeding programmes.

Asian Journal of Current ResearchVol. 11(4)
Acharya N. G. Ranga Agricultural University (IN)
Openalex Percentile: Top 17%
Sesame and Sesamin Research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon