Dissecting the genetic basis of yield stability in faba bean by multi-environment analysis
17 affiliations
- 1Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark. [email protected]
- 2Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark
- 3Teagasc, Crops Research, Oak Park, Carlow, Ireland
- 4School of Agriculture, Policy and Development, University of Reading, Reading, UK
- 5Nordic Seed A/S, Odder, Denmark
- 6Division of Plant Breeding Methodology, Georg-August-University Goettingen, Goettingen, Germany
- 7Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Seeland, Germany
- 8Université Bourgogne Europe, Institut Agro Dijon, INRAE, Agroécologie, Dijon, France
- 9Agri Obtentions, Guyancourt, France
- 10Norddeutsche Pflanzenzucht Hans-Georg Lembke KG, Holtsee, Germany
- 11, Agrovegetal, Seville, Spain
- 12Sejet Plant Breeding, Horsens, Denmark
- 13Área de Mejora Vegetal y Biotecnología, IFAPA, Córdoba, Spain
- 14Department of Soil and Crop Sciences, Texas A&M AgriLife Research Dallas Center, Dallas, TX, USA
- 15Department of Agricultural Sciences, University of Helsinki, Helsinki, Finland
- 16Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark. [email protected]
- 17Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark. [email protected]
Abstract
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.
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BibTeX
@article{bornhofen2026dissecting,
title = {{Dissecting the genetic basis of yield stability in faba bean by multi-environment analysis}},
author = {Elesandro Bornhofen and Troels W. Mouritzen and Sheila Alves and Thomas Ramsay Robertson-Shersby-Harvie and Cathrine Kiel Skovbjerg and Alex Windhorst and Hailin Zhang and Thilani Bhagya Jayakody and Jing Zhang and Marcin Nadzieja and Hyeonah Shim and Jean-Bernard Magnin-Robert and Grégoire Aubert and Matthieu Floriot and Camille Guiziou and Olaf Sass and Gregor Welna and Ignacio Solís and Linda Kærgaard Nielsen and Natalia Gutiérrez and Murukarthick Jayakodi and Frederick L. Stoddard and Donal Martin O’Sullivan and Ana M. Torres and Wolfgang Link and Nadim Tayeh and Luc Janss and Stig Uggerhøj Andersen},
journal = {Genome Biology},
year = {2026},
month = jul,
doi = {10.1186/s13059-026-04181-0},
url = {https://hal.inrae.fr/hal-05699432v1},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Dissecting the genetic basis of yield stability in faba bean by multi-environment analysis
AU - Elesandro Bornhofen
AU - Troels W. Mouritzen
AU - Sheila Alves
AU - Thomas Ramsay Robertson-Shersby-Harvie
AU - Cathrine Kiel Skovbjerg
AU - Alex Windhorst
AU - Hailin Zhang
AU - Thilani Bhagya Jayakody
AU - Jing Zhang
AU - Marcin Nadzieja
AU - Hyeonah Shim
AU - Jean-Bernard Magnin-Robert
AU - Grégoire Aubert
AU - Matthieu Floriot
AU - Camille Guiziou
AU - Olaf Sass
AU - Gregor Welna
AU - Ignacio Solís
AU - Linda Kærgaard Nielsen
AU - Natalia Gutiérrez
AU - Murukarthick Jayakodi
AU - Frederick L. Stoddard
AU - Donal Martin O’Sullivan
AU - Ana M. Torres
AU - Wolfgang Link
AU - Nadim Tayeh
AU - Luc Janss
AU - Stig Uggerhøj Andersen
PY - 2026
DA - 2026/07/20
JO - Genome Biology
DO - 10.1186/s13059-026-04181-0
UR - https://hal.inrae.fr/hal-05699432v1
AB - 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.
SN - 1465-6906
SN - 1474-760X
SN - 1465-6914
SN - 1474-7596
ER -