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      Genome-wide association analysis for drought tolerance and associated traits in faba bean ( Vicia faba L.)

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          Abstract

          Faba bean ( Vicia faba L.) is an important high protein legume adapted to diverse climatic conditions with multiple benefits for the overall sustainability of the cropping systems. Plant-based protein demand is being expanded and faba bean is a good candidate to cover this need. However, the crop is very sensitive to abiotic stresses, especially drought, which severely affects faba bean yield and development worldwide. Therefore, identifying genes associated with drought stress tolerance is a major challenge in faba bean breeding. Although the faba bean response to drought stress has been widely studied, the molecular approaches to improve drought tolerance in this crop are still limited. Here we built on recent genomic advances such as the development of the first high-density SNP genotyping array, to conduct a genome-wide association study (GWAS) using thousands of genetic polymorphisms throughout the entire faba bean genome. A worldwide collection of 100 faba bean accessions was grown under control and drought conditions and 10 morphological, phenological and physiological traits were evaluated to identify single nucleotide polymorphism (SNP) markers associated with drought tolerance. We identified 29 SNP markers significantly correlated with these traits under drought stress conditions. The flanking sequences were blasted to the Medicago truncatula reference genomes in order to annotate potential candidate genes underlying the causal variants. Three of the SNPs for chlorophyll content after the stress, correspond to uncharacterized proteins indicating the presence of novel genes associated with drought tolerance in faba bean. The significance of stress-inducible signal transducers provides valuable information on the possible mechanisms underlying the faba bean response to drought stress, thus providing a foundation for future marker-assisted breeding in the crop.

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          Most cited references106

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          Rapid determination of free proline for water-stress studies

          Plant and Soil, 39(1), 205-207
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            Efficient methods to compute genomic predictions.

            P VanRaden (2008)
            Efficient methods for processing genomic data were developed to increase reliability of estimated breeding values and to estimate thousands of marker effects simultaneously. Algorithms were derived and computer programs tested with simulated data for 2,967 bulls and 50,000 markers distributed randomly across 30 chromosomes. Estimation of genomic inbreeding coefficients required accurate estimates of allele frequencies in the base population. Linear model predictions of breeding values were computed by 3 equivalent methods: 1) iteration for individual allele effects followed by summation across loci to obtain estimated breeding values, 2) selection index including a genomic relationship matrix, and 3) mixed model equations including the inverse of genomic relationships. A blend of first- and second-order Jacobi iteration using 2 separate relaxation factors converged well for allele frequencies and effects. Reliability of predicted net merit for young bulls was 63% compared with 32% using the traditional relationship matrix. Nonlinear predictions were also computed using iteration on data and nonlinear regression on marker deviations; an additional (about 3%) gain in reliability for young bulls increased average reliability to 66%. Computing times increased linearly with number of genotypes. Estimation of allele frequencies required 2 processor days, and genomic predictions required <1 d per trait, and traits were processed in parallel. Information from genotyping was equivalent to about 20 daughters with phenotypic records. Actual gains may differ because the simulation did not account for linkage disequilibrium in the base population or selection in subsequent generations.
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              Prediction of Total Genetic Value Using Genome-Wide Dense Marker Maps

              Recent advances in molecular genetic techniques will make dense marker maps available and genotyping many individuals for these markers feasible. Here we attempted to estimate the effects of ∼50,000 marker haplotypes simultaneously from a limited number of phenotypic records. A genome of 1000 cM was simulated with a marker spacing of 1 cM. The markers surrounding every 1-cM region were combined into marker haplotypes. Due to finite population size (Ne = 100), the marker haplotypes were in linkage disequilibrium with the QTL located between the markers. Using least squares, all haplotype effects could not be estimated simultaneously. When only the biggest effects were included, they were overestimated and the accuracy of predicting genetic values of the offspring of the recorded animals was only 0.32. Best linear unbiased prediction of haplotype effects assumed equal variances associated to each 1-cM chromosomal segment, which yielded an accuracy of 0.73, although this assumption was far from true. Bayesian methods that assumed a prior distribution of the variance associated with each chromosome segment increased this accuracy to 0.85, even when the prior was not correct. It was concluded that selection on genetic values predicted from markers could substantially increase the rate of genetic gain in animals and plants, especially if combined with reproductive techniques to shorten the generation interval.
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                Author and article information

                Contributors
                Journal
                Front Plant Sci
                Front Plant Sci
                Front. Plant Sci.
                Frontiers in Plant Science
                Frontiers Media S.A.
                1664-462X
                01 February 2023
                2023
                : 14
                : 1091875
                Affiliations
                [1] 1 Área de Mejora y Biotecnología, Andalusian Institute of Agricultural and Fisheries Research and Training (IFAPA), Centro Alameda del Obispo , Córdoba, Spain
                [2] 2 INRAE P3F, 86600 Lusignan, France, INRA, Centre Nouvelle-Aquitaine-Poitiers , Lusignan, France
                [3] 3 Julius Kühn-Institut (JKI), Federal Research Centre for Cultivated Plants, Institute for Resistance Research and Stress Tolerance , Sanitz, Germany
                Author notes

                Edited by: Leif Skot, Aberystwyth University, United Kingdom

                Reviewed by: Mohammed Ali Abd Elhammed Abd Allah, Desert Research Center, Egypt; Fouad Maalouf, International Center for Agricultural Research in the Dry Areas, Lebanon

                *Correspondence: Natalia Gutiérrez, natalia.gutierrez.leiva@ 123456juntadeandalucia.es

                This article was submitted to Plant Breeding, a section of the journal Frontiers in Plant Science

                Article
                10.3389/fpls.2023.1091875
                9928957
                36818887
                20972723-35c1-417d-b2b2-908bd2392e87
                Copyright © 2023 Gutiérrez, Pégard, Balko and Torres

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 07 November 2022
                : 16 January 2023
                Page count
                Figures: 6, Tables: 3, Equations: 3, References: 106, Pages: 17, Words: 9786
                Funding
                Funded by: Ministerio de Ciencia e Innovación , doi 10.13039/501100004837;
                Funded by: Horizon 2020 Framework Programme , doi 10.13039/100010661;
                Categories
                Plant Science
                Original Research

                Plant science & Botany
                drought stress,gwas,genetic diversity,faba bean,pca,heritability,snps markers,candidate genes

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