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<p>Sesame (<i>Sesamum indicum</i> L.) is a major oilseed crop with numerous oil and nutritional benefits. However, the genetic basis of complex agronomic traits remains fragmented across individual quantitative trait loci (QTL) studies. Meta-QTL (MQTL) analysis provides a robust framework for identifying stable genomic regions that control quantitatively inherited traits in diverse environments and genetic backgrounds. Here, we present the first comprehensive MQTL analysis of sesame, targeting morphological and yield components, oil content and quality, and seed and capsule traits. All published QTLs were compiled using LOD scores, phenotypic variance explained (PVE), and confidence intervals (CI), and projected onto a high-density consensus map comprising 38,972 markers using BioMercator v4.2. In total, 321 QTLs (54.9%) were effectively projected and summarized into 92 MQTLs. The average CI of MQTLs was 4.01&#xa0;cM, indicating a 2.41-fold (58.57%) reduction compared with the initial QTLs (9.68&#xa0;cM). Nineteen high-confidence MQTLs (CI ≤ 5&#xa0;cM and ≥ 4 initial QTLs) were selected for candidate gene mining, which collectively encompassed 1,678 unique gene models. Using orthology-based prioritization, we identified 160 orthologous candidates, and functional annotation revealed 43 genes that were strongly associated with key traits within the MQTL regions. Several MQTLs co-localize with marker–trait associations reported in previous genome-wide association studies, thereby reinforcing their significance in the regulation of traits. These findings indicate that MQTL analysis substantially improves the mapping precision and provides reliable genomic targets for sesame breeding. Integrating tightly linked markers from these MQTLs into marker-assisted and genomic selection schemes offers a powerful strategy to accelerate the genetic improvement of sesame in terms of yield, plant architecture, seed, and oil quality.</p>

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Unveiling core genomic regions shaping plant architecture, productivity, and seed quality traits in sesame (Sesamum indicum L.): insights from Meta-QTL study into breeding targets

  • 期刊论文

  • 发表日期：

    2026年6月27日

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  • Mahfuj Ahmed,
  • Md. Yeamin Hossain,
  • Md. Ridwanul Islam,
  • Md. Shohag Rana,
  • Nishat Tasnim Ananty,
  • Mst. Sufara Akhter Banu,
  • Abu Yousuf Hossin,
  • Md. Golam Robbani,
  • Md. Arifuzzaman,
  • Md. Mahmudul Hassan,
  • Kazi Md. Kamrul Huda,
  • Shahanaz Parveen,
  • Md. Harun-Ur-Rashid

摘要

Sesame (Sesamum indicum L.) is a major oilseed crop with numerous oil and nutritional benefits. However, the genetic basis of complex agronomic traits remains fragmented across individual quantitative trait loci (QTL) studies. Meta-QTL (MQTL) analysis provides a robust framework for identifying stable genomic regions that control quantitatively inherited traits in diverse environments and genetic backgrounds. Here, we present the first comprehensive MQTL analysis of sesame, targeting morphological and yield components, oil content and quality, and seed and capsule traits. All published QTLs were compiled using LOD scores, phenotypic variance explained (PVE), and confidence intervals (CI), and projected onto a high-density consensus map comprising 38,972 markers using BioMercator v4.2. In total, 321 QTLs (54.9%) were effectively projected and summarized into 92 MQTLs. The average CI of MQTLs was 4.01 cM, indicating a 2.41-fold (58.57%) reduction compared with the initial QTLs (9.68 cM). Nineteen high-confidence MQTLs (CI ≤ 5 cM and ≥ 4 initial QTLs) were selected for candidate gene mining, which collectively encompassed 1,678 unique gene models. Using orthology-based prioritization, we identified 160 orthologous candidates, and functional annotation revealed 43 genes that were strongly associated with key traits within the MQTL regions. Several MQTLs co-localize with marker–trait associations reported in previous genome-wide association studies, thereby reinforcing their significance in the regulation of traits. These findings indicate that MQTL analysis substantially improves the mapping precision and provides reliable genomic targets for sesame breeding. Integrating tightly linked markers from these MQTLs into marker-assisted and genomic selection schemes offers a powerful strategy to accelerate the genetic improvement of sesame in terms of yield, plant architecture, seed, and oil quality.