Unlocking genetic diversity for low-input systems in a changing climate through participatory characterization and GWAS of lentil landraces

Abstract Lentils are a vital staple crop in a world seeking sustainable and secure food, but their cultivation face a threat due to yield instability, mainly arising from a lack of genetic diversity in breeding programmes. In this study, we assembled and characterized the genetic and phenotypic dive...

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Main Authors: Elisa Lorenzetti, Mercy Wairimu Macharia, Svenja Mager, Matteo Dell’Acqua, Stefano Carlesi, Paolo Bàrberi
Format: Article
Language:English
Published: Nature Portfolio 2024-12-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-024-83516-y
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author Elisa Lorenzetti
Mercy Wairimu Macharia
Svenja Mager
Matteo Dell’Acqua
Stefano Carlesi
Paolo Bàrberi
author_facet Elisa Lorenzetti
Mercy Wairimu Macharia
Svenja Mager
Matteo Dell’Acqua
Stefano Carlesi
Paolo Bàrberi
author_sort Elisa Lorenzetti
collection DOAJ
description Abstract Lentils are a vital staple crop in a world seeking sustainable and secure food, but their cultivation face a threat due to yield instability, mainly arising from a lack of genetic diversity in breeding programmes. In this study, we assembled and characterized the genetic and phenotypic diversities of a collection of 106 lentil genotypes, to evaluate their breeding and cropping potential. Lentil landraces from Italy and beyond, either abandoned or still cultivated, were collected from genebanks, seed savers, universities and farmers. We characterized their phenotypic diversity with an augmented block design, using a control plot enabling a spatial analysis. We phenotyped the collection during two cropping seasons for its agronomic performance, involving local practitioners in a participatory variety evaluation. Meanwhile, we genotyped the landrace collection with a DNA sequencing approach, obtaining 91,136 high quality single nucleotide polymorphisms (SNPs). We used SNPs to describe the phylogenetic relation among landraces, unveiling their uniqueness, and combined SNP data with measured traits to conduct a genome-wide association study (GWAS) that led to the identification of 32 unique marker-trait associations highlighting lentil genomic loci related with adaptation and performance. The results of this study offer new tools to unlock agrobiodiversity for lentil breeding in the Mediterranean, towards the identification of genetic factors responsible for traits of agronomic interest and providing possible sources of parental material.
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spelling doaj-art-e49b9f61c1ca46eb96cf635fe2a180ce2025-01-05T12:30:18ZengNature PortfolioScientific Reports2045-23222024-12-0114111510.1038/s41598-024-83516-yUnlocking genetic diversity for low-input systems in a changing climate through participatory characterization and GWAS of lentil landracesElisa Lorenzetti0Mercy Wairimu Macharia1Svenja Mager2Matteo Dell’Acqua3Stefano Carlesi4Paolo Bàrberi5Instittue of Plant Sciences, Scuola Superiore Sant’AnnaInstittue of Plant Sciences, Scuola Superiore Sant’AnnaInstittue of Plant Sciences, Scuola Superiore Sant’AnnaInstittue of Plant Sciences, Scuola Superiore Sant’AnnaInstittue of Plant Sciences, Scuola Superiore Sant’AnnaInstittue of Plant Sciences, Scuola Superiore Sant’AnnaAbstract Lentils are a vital staple crop in a world seeking sustainable and secure food, but their cultivation face a threat due to yield instability, mainly arising from a lack of genetic diversity in breeding programmes. In this study, we assembled and characterized the genetic and phenotypic diversities of a collection of 106 lentil genotypes, to evaluate their breeding and cropping potential. Lentil landraces from Italy and beyond, either abandoned or still cultivated, were collected from genebanks, seed savers, universities and farmers. We characterized their phenotypic diversity with an augmented block design, using a control plot enabling a spatial analysis. We phenotyped the collection during two cropping seasons for its agronomic performance, involving local practitioners in a participatory variety evaluation. Meanwhile, we genotyped the landrace collection with a DNA sequencing approach, obtaining 91,136 high quality single nucleotide polymorphisms (SNPs). We used SNPs to describe the phylogenetic relation among landraces, unveiling their uniqueness, and combined SNP data with measured traits to conduct a genome-wide association study (GWAS) that led to the identification of 32 unique marker-trait associations highlighting lentil genomic loci related with adaptation and performance. The results of this study offer new tools to unlock agrobiodiversity for lentil breeding in the Mediterranean, towards the identification of genetic factors responsible for traits of agronomic interest and providing possible sources of parental material.https://doi.org/10.1038/s41598-024-83516-y
spellingShingle Elisa Lorenzetti
Mercy Wairimu Macharia
Svenja Mager
Matteo Dell’Acqua
Stefano Carlesi
Paolo Bàrberi
Unlocking genetic diversity for low-input systems in a changing climate through participatory characterization and GWAS of lentil landraces
Scientific Reports
title Unlocking genetic diversity for low-input systems in a changing climate through participatory characterization and GWAS of lentil landraces
title_full Unlocking genetic diversity for low-input systems in a changing climate through participatory characterization and GWAS of lentil landraces
title_fullStr Unlocking genetic diversity for low-input systems in a changing climate through participatory characterization and GWAS of lentil landraces
title_full_unstemmed Unlocking genetic diversity for low-input systems in a changing climate through participatory characterization and GWAS of lentil landraces
title_short Unlocking genetic diversity for low-input systems in a changing climate through participatory characterization and GWAS of lentil landraces
title_sort unlocking genetic diversity for low input systems in a changing climate through participatory characterization and gwas of lentil landraces
url https://doi.org/10.1038/s41598-024-83516-y
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