Abstract
BACKGROUND: Establishing faba bean (Vicia faba L.) as a major protein crop in Europe requires developing high-yielding varieties. However, the genetic regulation of yield-related traits is currently under-explored. These traits can be improved by exploiting the extensive but largely uncharacterized faba bean germplasm. Our study aimed to identify associations between 38,014 single nucleotide polymorphisms (SNPs) and flowering time (FT), plant height (PH), pod length (PL), seeds per pod (SP), and single seed weight (SSW) in 245 faba bean accessions (CGN panel) via a Genome-Wide Association Study (GWAS). The accessions were grown in 2021 and 2022 in the Netherlands. Additionally, we developed genomic selection (GS) models to predict the genetic merit within large germplasm collections for the mentioned traits, as well as yield (YLD). RESULTS: The CGN panel was an optimal panel for performing high-resolution GWAS, showing large phenotypic variation, high narrow-sense heritability for all traits, and minimal genetic relatedness among accessions. Population structure analysis revealed the presence of four genetic groups. GWAS uncovered 33 SNP-trait associations in 2021 and 17 in 2022. We identified one stable QTL for FT and four for SSW over the two years, representing key molecular markers for testing in breeding applications. Short linkage disequilibrium decay (~ 268 Kbp) facilitated the identification of several important candidate genes with interesting homologs in other crops. Ten SNPs in 2021 and five in 2022 were predicted to be intra-genic missense variants, potentially altering protein function. Moreover, modeling the SNP effect simultaneously via Bayesian GS showed promising predictive ability (PA) and prediction accuracy (ACC), supporting their potential application in germplasm-improvement programs. Predictive ability ranged from 0.58 to 0.81 in 2021, and 0.47 to 0.85 in 2022 for different traits. Additionally, across-year predictions showed stable PA. CONCLUSION: GWAS revealed promising QTLs for use in molecular breeding and highlighted new candidate genes. Interestingly, the prediction of intra-genic SNPs categorized 15 SNPs as putatively affecting protein function. Moreover, we demonstrated for the first time in faba bean that GS has the potential to unlock untapped diversity in genebank collections and accelerate trait integration into faba bean breeding programs.