High-density whole-genome resequencing unravels the genetic basis of yield components in alfalfa via multi-environment GWAS
Abstract
Alfalfa (Medicago sativa L.) yield is a complex quantitative trait shaped by multiple yield components and strong genotype-by-environment interactions. In this study, we combined multi-environment phenotyping with deep whole-genome resequencing to dissect the genetic architecture of six agronomic traits in 198 half-sib families. Field trials conducted across two contrasting locations over three years revealed extensive variation in plant height, stem diameter, stem number, fresh weight, dry weight, and leaf-to-stem ratio. Deep resequencing generated an average of 39.4 Gb clean data per accession, with an effective sequencing depth of 42.14×, and identified 10.37 million high-quality SNPs densely distributed across the alfalfa genome. Using multi-environment BLUP values for GWAS, we detected 1137 trait-associated SNPs and prioritized candidate genes by integrating variant effects, haplotype differentiation, functional annotation, and expression patterns. A non-synonymous SNP in MsBG42, encoding beta-glucosidase 42, was associated with stem diameter. For biomass-related traits, MsG0780040381.01, designated MsFBL, encodes an F-box/FBD/LRR-repeat protein and was associated with both fresh and dry weight, with root-preferential expression. Hairy root-based functional validation further showed that MsFBL positively regulates root and whole-plant biomass. These findings provide a high-resolution genomic resource and identify MsFBL as a functionally supported target for alfalfa biomass improvement.