Bahjat Noor Maiwan, Yıldız Mehtap, Nadeem Muhammad Azhar, Morales Andres, Wohlfeiler Josefina, Baloch Faheem Shahzad, Tunçtürk Murat, Koçak Metin, Chung Yong Suk, Grzebelus Dariusz, Sadik Gökhan, Kuzğun Cansu, Cavagnaro Pablo Federico
Department of Agricultural Biotechnology, Faculty of Agriculture, Van Yuzuncu Yil University, Van, 65080, Turkey.
Department of Biotechnology, Faculty of Science, Mersin University, Yenişehir, Mersin, 33343, Turkey.
BMC Plant Biol. 2025 Apr 24;25(1):523. doi: 10.1186/s12870-025-06525-7.
Knowledge about the degree of genetic diversity and population structure is crucial as it facilitates novel variations that can be used in breeding programs. Similarly, genome-wide association studies (GWAS) can reveal candidate genes controlling traits of interest. Sugar beet is a major industrial crops worldwide, generating 20% of the world's total sugar production. In this work, using genotyping by sequencing (GBS)-derived SNP and silicoDArT markers, we present new insights into the genetic structure and level of genetic diversity in an international sugar beet germplasm (94 accessions from 16 countries). We also performed GWAS to identify candidate genes for agriculturally-relevant traits.
After applying various filtering criteria, a total of 4,609 high-quality non-redundant SNPs and 6,950 silicoDArT markers were used for genetic analyses. Calculation of various diversity indices using the SNP (e.g., mean gene diversity: 0.31, MAF: 0.22) and silicoDArT (mean gene diversity: 0.21, MAF: 0.12) data sets revealed the existence of a good level of conserved genetic diversity. Cluster analysis by UPGMA revealed three and two distinct clusters for SNP and DArT data, respectively, with accessions being grouped in general agreement with their geographical origins and their tap root color. Coincidently, structure analysis indicated three (K = 3) and two (K = 2) subpopulations for SNP and DArT data, respectively, with accessions in each subpopulation sharing similar geographic origins and root color; and comparable clustering patterns were also found by principal component analysis. GWAS on 13 root and leaf phenotypic traits allowed the identification of 35 significant marker-trait associations for nine traits and, based on predicted functions of the genes in the genomic regions surrounding the significant markers, 25 candidate genes were identified for four root (fresh weight, width, length, and color) and three leaf traits (shape, blade color, and veins color).
The present work unveiled conserved genetic diversity-evidenced both genetically (by SNP and silicoDArT markers) and phenotypically- exploitable in breeding programs and germplasm curation of sugar beet. Results from GWAS and candidate gene analyses provide a frame work for future studies aiming at deciphering the genetic basis underlying relevant traits for sugar beet and related crop types within Beta vulgaris subsp. vulgaris.
了解遗传多样性程度和种群结构至关重要,因为它有助于发现可用于育种计划的新变异。同样,全基因组关联研究(GWAS)可以揭示控制目标性状的候选基因。甜菜是全球主要的经济作物,占世界食糖总产量的20%。在本研究中,我们使用基于测序的基因分型(GBS)衍生的单核苷酸多态性(SNP)和硅芯片DArT标记,对一个国际甜菜种质资源(来自16个国家的94份材料)的遗传结构和遗传多样性水平有了新的认识。我们还进行了GWAS以鉴定与农业相关性状的候选基因。
应用各种筛选标准后,共4609个高质量非冗余SNP和6950个硅芯片DArT标记用于遗传分析。使用SNP数据集(例如,平均基因多样性:0.31,最小等位基因频率:0.22)和硅芯片DArT数据集(平均基因多样性:0.21,最小等位基因频率:0.12)计算各种多样性指数,揭示了存在良好水平的保守遗传多样性。通过UPGMA聚类分析,SNP和DArT数据分别显示出三个和两个不同的聚类,材料的分组总体上与其地理起源和主根颜色一致。巧合的是,结构分析表明SNP和DArT数据分别有三个(K = 3)和两个(K = 2)亚群,每个亚群中的材料具有相似的地理起源和根颜色;主成分分析也发现了类似的聚类模式。对13个根和叶表型性状进行GWAS,鉴定出9个性状的35个显著标记-性状关联,并根据显著标记周围基因组区域中基因的预测功能,鉴定出4个根性状(鲜重、宽度、长度和颜色)和3个叶性状(形状、叶片颜色和叶脉颜色)的25个候选基因。
本研究揭示了甜菜在育种计划和种质管理中在遗传(通过SNP和硅芯片DArT标记)和表型上都可利用的保守遗传多样性。GWAS和候选基因分析的结果为未来旨在解读甜菜及甜菜亚种内相关作物类型相关性状遗传基础的研究提供了框架。