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      Genome-wide SNP genotyping as a simple and practical tool to accelerate the development of inbred lines in outbred tree species: An example in cacao ( Theobroma cacao L.)

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          Abstract

          Cacao is a globally important crop with a long history of domestication and selective breeding. Despite the increased use of elite clones by cacao farmers, worldwide plantations are established mainly using hybrid progeny material derived from heterozygous parents, therefore displaying high tree-to-tree variability. The deliberate development of hybrids from advanced inbred lines produced by successive generations of self-pollination has not yet been fully considered in cacao breeding. This is largely due to the self-incompatibility of the species, the long generation cycles (3–5 years) and the extensive trial areas needed to accomplish the endeavor. We propose a simple and accessible approach to develop inbred lines based on accelerating the buildup of homozygosity based on regular selfing assisted by genome-wide SNP genotyping. In this study we genotyped 90 clones from the Brazilian CEPEC´s germplasm collection and 49 inbred offspring of six S 1 or S 2 cacao families derived from self-pollinating clones CCN-51, PS-13.19, TSH-1188 and SIAL-169. A set of 3,380 SNPs distributed across the cacao genome were interrogated on the EMBRAPA multi-species 65k Infinium chip. The 90 cacao clones showed considerable variation in genome-wide SNP homozygosity (mean 0.727± 0.182) and 19 of them with homozygosity ≥90%. By assessing the increase in homozygosity across two generations of self-pollinations, SNP data revealed the wide variability in homozygosity within and between S 1 and S 2 families. Even in small families (<10 sibs), individuals were identified with up to ~1.5 standard deviations above the family mean homozygosity. From baseline homozygosities of 0.476 and 0.454, offspring with homozygosities of 0.862 and 0.879 were recovered for clones TSH-1188 and CCN-51 respectively, in only two generations of selfing (81–93% increase). SNP marker assisted monitoring and selection of inbred individuals can be a practical tool to optimize and accelerate the development of inbred lines of outbred tree species. This approach will allow a faster and more accurate exploitation of hybrid breeding strategies in cacao improvement programs and potentially in other perennial fruit and forest trees.

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          MEGA X: Molecular Evolutionary Genetics Analysis across Computing Platforms.

          The Molecular Evolutionary Genetics Analysis (Mega) software implements many analytical methods and tools for phylogenomics and phylomedicine. Here, we report a transformation of Mega to enable cross-platform use on Microsoft Windows and Linux operating systems. Mega X does not require virtualization or emulation software and provides a uniform user experience across platforms. Mega X has additionally been upgraded to use multiple computing cores for many molecular evolutionary analyses. Mega X is available in two interfaces (graphical and command line) and can be downloaded from www.megasoftware.net free of charge.
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            GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research—an update

            Summary: GenAlEx: Genetic Analysis in Excel is a cross-platform package for population genetic analyses that runs within Microsoft Excel. GenAlEx offers analysis of diploid codominant, haploid and binary genetic loci and DNA sequences. Both frequency-based (F-statistics, heterozygosity, HWE, population assignment, relatedness) and distance-based (AMOVA, PCoA, Mantel tests, multivariate spatial autocorrelation) analyses are provided. New features include calculation of new estimators of population structure: G′ST, G′′ST, Jost’s D est and F′ST through AMOVA, Shannon Information analysis, linkage disequilibrium analysis for biallelic data and novel heterogeneity tests for spatial autocorrelation analysis. Export to more than 30 other data formats is provided. Teaching tutorials and expanded step-by-step output options are included. The comprehensive guide has been fully revised. Availability and implementation: GenAlEx is written in VBA and provided as a Microsoft Excel Add-in (compatible with Excel 2003, 2007, 2010 on PC; Excel 2004, 2011 on Macintosh). GenAlEx, and supporting documentation and tutorials are freely available at: http://biology.anu.edu.au/GenAlEx. Contact: rod.peakall@anu.edu.au
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              Spatial autocorrelation analysis of individual multiallele and multilocus genetic structure.

              Population genetic theory predicts that plant populations will exhibit internal spatial autocorrelation when propagule flow is restricted, but as an empirical reality, spatial structure is rarely consistent across loci or sites, and is generally weak. A lack of sensitivity in the statistical procedures may explain the discrepancy. Most work to date, based on allozymes, has involved pattern analysis for individual alleles, but new PCR-based genetic markers are coming into vogue, with vastly increased numbers of alleles. The field is badly in need of an explicitly multivariate approach to autocorrelation analysis, and our purpose here is to introduce a new approach that is applicable to multiallelic codominant, multilocus arrays. The procedure treats the genetic data set as a whole, strengthening the spatial signal and reducing the stochastic (allele-to-allele, and locus-to-locus) noise. We (i) develop a very general multivariate method, based on genetic distance methods, (ii) illustrate it for multiallelic codominant loci, and (iii) provide nonparametric permutational testing procedures for the full correlogram. We illustrate the new method with an example data set from the orchid Caladenia tentaculata, for which we show (iv) how the multivariate treatment compares with the single-allele treatment, (v) that intermediate frequency alleles from highly polymorphic loci perform well and rare alleles poorly, (vi) that a multilocus treatment provides clearer answers than separate single-locus treatments, and (vii) that weighting alleles differentially improves our resolution minimally. The results, though specific to Caladenia, offer encouragement for wider application.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: ResourcesRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: Funding acquisitionRole: MethodologyRole: Resources
                Role: Data curationRole: Funding acquisitionRole: Resources
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: MethodologyRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                PLOS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                26 October 2022
                2022
                : 17
                : 10
                : e0270437
                Affiliations
                [1 ] Cacao Research Center (CEPEC/CEPLAC), Ilhéus, BA, Brazil
                [2 ] Plant Genetics Laboratory, EMBRAPA Genetic Resources and Biotechnology, Brasilia, Brazil
                Institute of Mediterranean Forest Ecosystems of Athens, GREECE
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0003-4037-1554
                https://orcid.org/0000-0002-0050-970X
                Article
                PONE-D-22-16694
                10.1371/journal.pone.0270437
                9604995
                36288356
                9e307ac9-7113-40fb-857d-f755f98a0e64
                © 2022 Lopes et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 9 June 2022
                : 13 October 2022
                Page count
                Figures: 2, Tables: 2, Pages: 21
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100005668, Fundação de Apoio à Pesquisa do Distrito Federal;
                Award ID: RECGENOMICS 00193-00000924/2021-92
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100005668, Fundação de Apoio à Pesquisa do Distrito Federal;
                Award ID: NEXTFRUT 0193.001.198/2016
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100006181, Fundação de Amparo à Pesquisa do Estado da Bahia;
                Award ID: DTE0027/2013
                Award Recipient :
                Funded by: CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico)
                Award ID: 313699/2018-6
                Award Recipient :
                Funded by: CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico)
                Award ID: 306866/2018-8
                Award Recipient :
                This work was partially supported by FAP-DF (Fundação de Apoio à Pesquisa do Distrito Federal) through grants RECGENOMICS 00193-00000924/2021-92 and NEXTREE 0193.001.198/2016; FAPESB (Fundação de Amparo à Pesquisa do Estado da Bahia) through grant DTE0027/2013 and CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico) productivity fellowships 313699/2018-6 to KPG and 306866/2018-8 to DG. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Genetics
                Heredity
                Homozygosity
                Biology and Life Sciences
                Genetics
                Single Nucleotide Polymorphisms
                Biology and Life Sciences
                Molecular Biology
                Molecular Biology Techniques
                Cloning
                Research and Analysis Methods
                Molecular Biology Techniques
                Cloning
                Biology and Life Sciences
                Genetics
                Heredity
                Inbreeding
                Biology and Life Sciences
                Genetics
                Heredity
                Heterozygosity
                Research and Analysis Methods
                Animal Studies
                Experimental Organism Systems
                Inbred Strains
                Biology and Life Sciences
                Genetics
                Heredity
                Genetic Mapping
                Variant Genotypes
                Biology and Life Sciences
                Molecular Biology
                Molecular Biology Techniques
                Genotyping
                Research and Analysis Methods
                Molecular Biology Techniques
                Genotyping
                Custom metadata
                All relevant data are within the paper and its Supporting Information files.

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