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      Genome-wide association study for yield-related traits in faba bean ( Vicia faba L.)

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          Abstract

          Yield is the most complex trait to improve crop production, and identifying the genetic determinants for high yield is a major issue in breeding new varieties. In faba bean ( Vicia faba L.), quantitative trait loci (QTLs) have previously been detected in studies of biparental mapping populations, but the genes controlling the main trait components remain largely unknown. In this study, we investigated for the first time the genetic control of six faba bean yield-related traits: shattering (SH), pods per plant (PP), seeds per pod (SP), seeds per plant (SPL), 100-seed weight (HSW), and plot yield (PY), using a genome-wide association study (GWAS) on a worldwide collection of 352 homozygous faba bean accessions with the aim of identifying markers associated with them. Phenotyping was carried out in field trials at three locations (Spain, United Kingdom, and Serbia) over 2 years. The faba bean panel was genotyped with the Affymetrix faba bean SNP-chip yielding 22,867 SNP markers. The GWAS analysis identified 112 marker–trait associations (MTAs) in 97 candidate genes, distributed over the six faba bean chromosomes. Eight MTAs were detected in at least two environments, and five were associated with multiple traits. The next step will be to validate these candidates in different genetic backgrounds to provide resources for marker-assisted breeding of faba bean yield.

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          PLINK: a tool set for whole-genome association and population-based linkage analyses.

          Whole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis.
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              MEGA11: Molecular Evolutionary Genetics Analysis Version 11

              The Molecular Evolutionary Genetics Analysis (MEGA) software has matured to contain a large collection of methods and tools of computational molecular evolution. Here, we describe new additions that make MEGA a more comprehensive tool for building timetrees of species, pathogens, and gene families using rapid relaxed-clock methods. Methods for estimating divergence times and confidence intervals are implemented to use probability densities for calibration constraints for node-dating and sequence sampling dates for tip-dating analyses. They are supported by new options for tagging sequences with spatiotemporal sampling information, an expanded interactive Node Calibrations Editor , and an extended Tree Explorer to display timetrees. Also added is a Bayesian method for estimating neutral evolutionary probabilities of alleles in a species using multispecies sequence alignments and a machine learning method to test for the autocorrelation of evolutionary rates in phylogenies. The computer memory requirements for the maximum likelihood analysis are reduced significantly through reprogramming, and the graphical user interface has been made more responsive and interactive for very big data sets. These enhancements will improve the user experience, quality of results, and the pace of biological discovery. Natively compiled graphical user interface and command-line versions of MEGA11 are available for Microsoft Windows, Linux, and macOS from www.megasoftware.net .
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                Author and article information

                Contributors
                URI : https://loop.frontiersin.org/people/2053012Role: Role: Role: Role: Role:
                URI : https://loop.frontiersin.org/people/653275Role: Role: Role: Role:
                URI : https://loop.frontiersin.org/people/582973Role: Role: Role:
                URI : https://loop.frontiersin.org/people/1298545Role: Role: Role:
                URI : https://loop.frontiersin.org/people/416191Role: Role: Role:
                URI : https://loop.frontiersin.org/people/202373Role: Role: Role:
                URI : https://loop.frontiersin.org/people/31647Role: Role: Role: Role: Role: Role:
                Journal
                Front Plant Sci
                Front Plant Sci
                Front. Plant Sci.
                Frontiers in Plant Science
                Frontiers Media S.A.
                1664-462X
                13 March 2024
                2024
                : 15
                : 1328690
                Affiliations
                [1] 1 Área de Mejora Vegetal y Biotecnología, IFAPA Centro “Alameda del Obispo” , Córdoba, Spain
                [2] 2 INRA, Centre Nouvelle-Aquitaine-Poitiers, UR4 (URP3F) , Lusignan, France
                [3] 3 Agrovegetal S.A. , Sevilla, Spain
                [4] 4 Institute for Forage Crops , Kruševac, Serbia
                [5] 5 Institute of Biological, Environmental and Rural Sciences (IBERS), Aberystwyth University , Aberystwyth, United Kingdom
                Author notes

                Edited by: Åshild Ergon, Norwegian University of Life Sciences, Norway

                Reviewed by: Wolfgang Link, University of Göttingen, Germany

                Susanne Windju, Graminor, Norway

                *Correspondence: Natalia Gutierrez, natalia.gutierrez.leiva@ 123456juntadeandalucia.es
                Article
                10.3389/fpls.2024.1328690
                10965552
                38545396
                33d03f47-ba71-40e0-b41a-5cb2c26e7865
                Copyright © 2024 Gutierrez, Pégard, Solis, Sokolovic, Lloyd, Howarth 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
                : 27 October 2023
                : 26 February 2024
                Page count
                Figures: 8, Tables: 3, Equations: 5, References: 112, Pages: 25, Words: 13469
                Funding
                The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This project has received funding from the European Union’s Horizon 2020 Programme for Research and Innovation under grant agreement n°727312 (EUCLEG Project), PID2020-114952RR-I00 and PR.AVA23.INV2023.009 co-financed by ERDF.
                Categories
                Plant Science
                Original Research
                Custom metadata
                Plant Breeding

                Plant science & Botany
                yield,heritability,population structure,linkage disequilibrium,gwas,mta markers,candidate genes,faba bean

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