Faba bean is a globally adapted legume protein crop with a high yield potential. Currently, yield variation across environments limits more widespread cultivation, and the underlying genetics remain poorly understood. Here, we identify major QTL for faba bean yield and yield stability. We genotype the ProFaba diversity panel with high resolution and carry out coordinated multi-year/location trials across Europe. Based on these data, we identify more than one hundred loci associated with mean performance and stability for 14 complex traits, including yield. Experimental validation supports the involvement of the candidate gene Vfaba.Hedin2.R2.1g002122 in plant architecture, with gene expression significantly associated with first pod position and plant height. Furthermore, we introduce a method for integrating environmental data in the analysis of trait stability based on a random regression mixed model, which enables prediction of performance in untested environments. Our study provides insights into the genetic architecture of yield, yield stability, and genotype-by-environment interaction in faba bean. The genomic resources, candidate loci, and weather-informed analytical framework provide practical tools for predicting performance across environments and accelerating breeding of resilient, high-yielding protein crops.
Elesandro Bornhofen, Troels W. Mouritzen, Sheila Alves et al.· Genome Biology· 0 citations
Faba bean breeding and genomics have seen steady progress in recent years, supported by genome sequences and high-density genotyping platforms. These tools have been valuable for trait mapping, diversity assessment, and genomic research, but they have limited routine use in breeding programs due to their relatively high cost. Recent progress in establishing an optimized, cost-efficient genotyping-by-sequencing protocol tailored to the large and complex faba bean genome has created the foundation for a more accessible genotyping solution.
Using this approach, we explored the genetic diversity of faba bean germplasm from various panels, providing a comprehensive representation of the crop’s genetic landscape. From this dataset, we identified and selected a high-quality set of informative SNP markers that are evenly distributed across the genome. Building on these resources, we designed a breeder-friendly 10K SNP chip.
The 10K SNP chip delivers high accuracy, broad genomic coverage, and affordability. The chip was validated across diverse germplasm panels, demonstrating strong clustering performance, high reproducibility, and applicability to breeding-relevant germplasm.
This platform offers a cost-effective alternative to higher-density arrays, enabling its integration into genomic selection, marker-assisted breeding, and diversity monitoring, ultimately supporting accelerated genetic gain and the delivery of improved varieties to farmers.
Hyeonah Shim, Hailin Zhang, Thomas Groß et al.· Frontiers in Plant Science· 0 citations
