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      Genetic diversity and population structure of fine aroma cacao (Theobroma cacao L.) from north Peru revealed by single nucleotide polymorphism (SNP) markers

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      Frontiers in Ecology and Evolution
      Frontiers Media SA

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          Abstract

          Cacao (Theobroma cacao L.) is the basis of the lucrative confectionery industry with “fine or flavour” cocoa attracting higher prices due to desired sensory and quality profiles. The Amazonas Region (north Peru) has a designation of origin, Fine Aroma Cacao, based on sensory quality, productivity and morphological descriptors but its genetic structure and ancestry is underexplored. We genotyped 143 Fine Aroma Cacao trees from northern Peru (Bagua, Condorcanqui, Jaén, Mariscal Cáceres, and Utcubamba; mainly Amazonas Region), using 192 single nucleotide polymorphic markers. Identity, group, principal coordinate, phylogenetic and ancestry analyses were conducted. There were nine pairs of matched trees giving 134 unique samples. The only match within 1,838 reference cacao profiles was to a putative CCN 51 by a Condorcanqui sample. The “Peru Uniques” group was closest to Nacional and Amelonado-Nacional genetic clusters based on F ST analysis. The provinces of Bagua and Utcubamba were genetically identical (D est = 0.001; P = 0.285) but differed from Condorcanqui (D est = 0.016–0.026; P = 0.001–0.006). Sixty-five (49%) and 39 (29%) of the Peru Uniques were mixed from three and four genetic clusters, respectively. There was a common and strong Nacional background with 104 individuals having at least 30% Nacional ancestry. The fine aroma of cacao from Northern Peru is likely due to the prevalent Nacional background with some contribution from Criollo. A core set of 53 trees was identified. These findings are used to support the continuance of the fine or flavour industry in Peru.

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          Detecting the number of clusters of individuals using the software structure: a simulation study

          The identification of genetically homogeneous groups of individuals is a long standing issue in population genetics. A recent Bayesian algorithm implemented in the software STRUCTURE allows the identification of such groups. However, the ability of this algorithm to detect the true number of clusters (K) in a sample of individuals when patterns of dispersal among populations are not homogeneous has not been tested. The goal of this study is to carry out such tests, using various dispersal scenarios from data generated with an individual-based model. We found that in most cases the estimated 'log probability of data' does not provide a correct estimation of the number of clusters, K. However, using an ad hoc statistic DeltaK based on the rate of change in the log probability of data between successive K values, we found that STRUCTURE accurately detects the uppermost hierarchical level of structure for the scenarios we tested. As might be expected, the results are sensitive to the type of genetic marker used (AFLP vs. microsatellite), the number of loci scored, the number of populations sampled, and the number of individuals typed in each sample.
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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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              Revising how the computer program CERVUS accommodates genotyping error increases success in paternity assignment.

              Genotypes are frequently used to identify parentage. Such analysis is notoriously vulnerable to genotyping error, and there is ongoing debate regarding how to solve this problem. Many scientists have used the computer program CERVUS to estimate parentage, and have taken advantage of its option to allow for genotyping error. In this study, we show that the likelihood equations used by versions 1.0 and 2.0 of CERVUS to accommodate genotyping error miscalculate the probability of observing an erroneous genotype. Computer simulation and reanalysis of paternity in Rum red deer show that correcting this error increases success in paternity assignment, and that there is a clear benefit to accommodating genotyping errors when errors are present. A new version of CERVUS (3.0) implementing the corrected likelihood equations is available at http://www.fieldgenetics.com.
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                Author and article information

                Journal
                Frontiers in Ecology and Evolution
                Front. Ecol. Evol.
                Frontiers Media SA
                2296-701X
                July 15 2022
                July 15 2022
                : 10
                Article
                10.3389/fevo.2022.895056
                2c3881fd-7e57-4b3c-a77f-48d8da1bb941
                © 2022

                Free to read

                https://creativecommons.org/licenses/by/4.0/

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