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Background <p>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.</p> Results <p>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 <i>Vfaba.Hedin2.R2.1g002122</i> 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.</p> Conclusions <p>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.</p>

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Dissecting the genetic basis of yield stability in faba bean by multi-environment analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月20日

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  • Elesandro Bornhofen,
  • Troels W. Mouritzen,
  • Sheila Alves,
  • Thomas Ramsay Robertson-Shersby-Harvie,
  • Cathrine Kiel Skovbjerg,
  • Alex Windhorst,
  • Hailin Zhang,
  • Thilani Bhagya Jayakody,
  • Jing Zhang,
  • Marcin Nadzieja,
  • Hyeonah Shim,
  • Jean-Bernard Magnin-Robert,
  • Grégoire Aubert,
  • Matthieu Floriot,
  • Camille Guiziou,
  • Olaf Sass,
  • Gregor Welna,
  • Ignacio Solís,
  • Linda Kærgaard Nielsen,
  • Natalia Gutiérrez,
  • Murukarthick Jayakodi,
  • Frederick L. Stoddard,
  • Donal Martin O’Sullivan,
  • Ana M. Torres,
  • Wolfgang Link,
  • Nadim Tayeh,
  • Luc Janss,
  • Stig Uggerhøj Andersen

摘要

Background

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.

Results

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.

Conclusions

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.