ArticleMolecular genetics and genomics : MGG2026
Exploring the genetic landscape of sesame: whole genome sequencing reveals distinct Egyptian lineages and SNP variability.
Article in Molecular genetics and genomics : MGG, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Sesame, one of the earliest cultivated oil crops in human history, holds a distinguished place due to its high oil content, rich flavor, and distinctive nutty aroma. In this study, we performed whole-genome resequencing on five Egyptian sesame genotypes (Shandaweel 3, Toshky1, and three gamma-irradiated mutant lines). Additionally, we incorporated 19 publicly available sesame genome datasets from the NCBI Sequence Read Archive (SRA) to support comparative genomic analyses. We identified 6,106,085 nucleotide variants across the 24 sesame genotypes studied. After excluding low-coverage SNPs, we obtained 26,424 SNPs with MAF > 0.05 used for subsequent analysis. The highest number of SNPs was observed on Chromosome Si01 (4144 SNPs), followed by Si12 (3721 SNPs) and Si13 (3151 SNPs), whereas Si09 (241 SNPs) and Si02 (359 SNPs) displayed the lowest SNP counts. Genome-wide diversity analysis revealed variation across chromosomes, with chromosomes Si04 and Si07 showing the highest heterozygosity (Ho = 0.816-0.849) and polymorphism information content (PIC = 0.347-0.353). Population structure analysis identified three distinct genetic clusters, with Egyptian cultivars forming a genetically pure and separate cluster.Chinese cultivars showed admixture with those from the USA and South Korea. Phylogenetic analysis supported the country-based clustering, with Egyptian genotypes forming a unique group. SNP effect analysis showed that missense mutations were common, potentially affecting protein function. Notable impacted genes included AXR1, CYP73A5, and CERK1, which play critical roles in growth, stress resilience, and pathogen defense. By integrating public RNA-seq datasets with our genomic variant data, we explored the correlation between gene expression and genetic variation. This multi-omics analysis identified genes with variants and expression patterns potentially involved in stress adaptation, including membrane stabilizers (UGT80B1, CalS10), signaling regulators (PUB9, CNGC15b), hormonal integrators (ARF2, DLO1), and redox managers. Our whole-genome sequencing study highlights the influence of geographic origin on sesame genetic diversity and provides SNP data for breeding programs aimed at enhancing adaptability and resilience.
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