Population structure, genetic diversity, and GWAS analyses with GBS-derived SNPs and silicodart markers unveil genetic potential for breeding and candidate genes for agronomic and root quality traits in an international sugar beet germplasm collection - Bahjat, Noor Maiwan; Yildiz, Mehtap; Nadeem, Muhammad Azhar; Morales Sanfurgo, Hugo Andres; Wohlfeiler Altavilla, Josefina; Baloch, Faheem Shehzad; Tunçtürk, Murat; Koçak, Metin; Yong, Suk Chung; Grzebelus, Dariusz; Sadık, Gökhan; Kuzğun, Cansu; et al. (BMC, 2025)
Background: 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. Results: 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). Conclusions: 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.