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Background <p>Buckwheat is an important underutilised crop valued for its nutritional benefits, including high-quality protein, dietary fiber, and rich bioactive compounds such as rutin and antioxidants. Characterization of genetic diversity in buckwheat is very important, as it facilitates management of germplasm banks and identifies genetically-contrasting accessions, useful for genetic research and breeding programs. This study provides a comprehensive analysis of genetic diversity, population structure, and relationships within a buckwheat germplasm collection using GBS-derived SilicoDArT and SNP markers.</p> Results <p>A total of 25,985 raw SNP and 10,714 SilicoDArT markers were called through GBS analysis, with SNPs exhibiting higher missing data (29%) compared to SilicoDArTs (9%). After filtering for quality metrics such as repeatability (&gt; 90%) and call rate (100%), 1,475 SNP and 608 SilicoDArT markers with MAF &gt; 1% were selected for downstream analyses. Genetic diversity indices based on SNPs revealed moderate diversity (Nei’s genetic diversity = 0.24) within the collection, with pairwise genetic distances ranging from 0.035 to 0.227 and an average of 0.194. UPGMA clustering identified two major groups (S-upg-I and S-upg-II), supported also by STRUCTURE analysis, which detected two genetic clusters with minimal admixture. Principal coordinate analysis corroborated these findings, highlighting clear separation consistent with STRUCTURE results, although most genetic variation resided among the accessions (over 92%). Analysis of molecular variance indicated low genetic differentiation among clusters (FST = 0.005) and geographical origins, with over 99% of variation attributable to individual accessions. Similarly, analyses based on SilicoDArT markers revealed lower diversity metrics and a less distinct clustering pattern, with the majority of genetic variation residing among the accessions (90.7%) and minimal differentiation among populations (FST = 0.002).</p> Conclusions <p>The results show high intra-population diversity, low geographic differentiation, and low genetic divergence, highlighting the value of these markers for buckwheat germplasm characterization and breeding.</p>

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GBS-derived SNP and SilicoDArT markers reveals the genetic variation and population structure of Korean buckwheat (Fagopyrum esculentum) an underutilised crop

  • 期刊论文

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  • 发表日期：

    2025年10月30日

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  • Faheem Shehzad Baloch,
  • Seong Mun Lee,
  • Sheikh Mansoor,
  • Andres Morales,
  • E. M. B. M. Karunathilake,
  • Muhammad Azhar Nadeem,
  • Pablo Federico Cavagnaro,
  • Yong Suk Chung

摘要

Background

Buckwheat is an important underutilised crop valued for its nutritional benefits, including high-quality protein, dietary fiber, and rich bioactive compounds such as rutin and antioxidants. Characterization of genetic diversity in buckwheat is very important, as it facilitates management of germplasm banks and identifies genetically-contrasting accessions, useful for genetic research and breeding programs. This study provides a comprehensive analysis of genetic diversity, population structure, and relationships within a buckwheat germplasm collection using GBS-derived SilicoDArT and SNP markers.

Results

A total of 25,985 raw SNP and 10,714 SilicoDArT markers were called through GBS analysis, with SNPs exhibiting higher missing data (29%) compared to SilicoDArTs (9%). After filtering for quality metrics such as repeatability (> 90%) and call rate (100%), 1,475 SNP and 608 SilicoDArT markers with MAF > 1% were selected for downstream analyses. Genetic diversity indices based on SNPs revealed moderate diversity (Nei’s genetic diversity = 0.24) within the collection, with pairwise genetic distances ranging from 0.035 to 0.227 and an average of 0.194. UPGMA clustering identified two major groups (S-upg-I and S-upg-II), supported also by STRUCTURE analysis, which detected two genetic clusters with minimal admixture. Principal coordinate analysis corroborated these findings, highlighting clear separation consistent with STRUCTURE results, although most genetic variation resided among the accessions (over 92%). Analysis of molecular variance indicated low genetic differentiation among clusters (FST = 0.005) and geographical origins, with over 99% of variation attributable to individual accessions. Similarly, analyses based on SilicoDArT markers revealed lower diversity metrics and a less distinct clustering pattern, with the majority of genetic variation residing among the accessions (90.7%) and minimal differentiation among populations (FST = 0.002).

Conclusions

The results show high intra-population diversity, low geographic differentiation, and low genetic divergence, highlighting the value of these markers for buckwheat germplasm characterization and breeding.