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      Microsatellite Support for Active Inbreeding in a Cichlid Fish

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          Abstract

          In wild animal populations, the degree of inbreeding differs between species and within species between populations. Because mating with kin often results in inbreeding depression, observed inbreeding is usually regarded to be caused by limited outbreeding opportunities due to demographic factors like small population size or population substructuring. However, theory predicts inclusive benefits from mating with kin, and thus part of the observed variation in inbreeding might be due to active inbreeding preferences. Although some recent studies indeed report kin mating preferences, the evidence is still highly ambiguous. Here, we investigate inbreeding in a natural population of the West African cichlid fish Pelvicachromis taeniatus which showed clear kin mating preferences in standardized laboratory experiments but no inbreeding depression. The presented microsatellite analysis reveals that the natural population has, in comparison to two reference populations, a reduced allelic diversity (A = 3) resulting in a low heterozygosity (H o = 0.167) pointing to a highly inbred population. Furthermore, we found a significant heterozygote deficit not only at population (F is = 0.116) but also at subpopulation level (F is = 0.081) suggesting that inbreeding is not only a by-product of population substructuring but possibly a consequence of behavioral kin preferences.

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          Arlequin (version 3.0): An integrated software package for population genetics data analysis

          Arlequin ver 3.0 is a software package integrating several basic and advanced methods for population genetics data analysis, like the computation of standard genetic diversity indices, the estimation of allele and haplotype frequencies, tests of departure from linkage equilibrium, departure from selective neutrality and demographic equilibrium, estimation or parameters from past population expansions, and thorough analyses of population subdivision under the AMOVA framework. Arlequin 3 introduces a completely new graphical interface written in C++, a more robust semantic analysis of input files, and two new methods: a Bayesian estimation of gametic phase from multi-locus genotypes, and an estimation of the parameters of an instantaneous spatial expansion from DNA sequence polymorphism. Arlequin can handle several data types like DNA sequences, microsatellite data, or standard multi-locus genotypes. A Windows version of the software is freely available on http://cmpg.unibe.ch/software/arlequin3.
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            How to track and assess genotyping errors in population genetics studies.

            Genotyping errors occur when the genotype determined after molecular analysis does not correspond to the real genotype of the individual under consideration. Virtually every genetic data set includes some erroneous genotypes, but genotyping errors remain a taboo subject in population genetics, even though they might greatly bias the final conclusions, especially for studies based on individual identification. Here, we consider four case studies representing a large variety of population genetics investigations differing in their sampling strategies (noninvasive or traditional), in the type of organism studied (plant or animal) and the molecular markers used [microsatellites or amplified fragment length polymorphisms (AFLPs)]. In these data sets, the estimated genotyping error rate ranges from 0.8% for microsatellite loci from bear tissues to 2.6% for AFLP loci from dwarf birch leaves. Main sources of errors were allelic dropouts for microsatellites and differences in peak intensities for AFLPs, but in both cases human factors were non-negligible error generators. Therefore, tracking genotyping errors and identifying their causes are necessary to clean up the data sets and validate the final results according to the precision required. In addition, we propose the outline of a protocol designed to limit and quantify genotyping errors at each step of the genotyping process. In particular, we recommend (i) several efficient precautions to prevent contaminations and technical artefacts; (ii) systematic use of blind samples and automation; (iii) experience and rigor for laboratory work and scoring; and (iv) systematic reporting of the error rate in population genetics studies.
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              Noninvasive genetic sampling: look before you leap.

              Noninvasive sampling allows genetic studies of free-ranging animals without the need to capture or even observe them, and thus allows questions to be addressed that cannot be answered using conventional methods. Initially, this sampling strategy promised to exploit fully the existing DNA-based technology for studies in ethology, conservation biology and population genetics. However, recent work now indicates the need for a more cautious approach, which includes quantifying the genotyping error rate. Despite this, many of the difficulties of noninvasive sampling will probably be overcome with improved methodology.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2011
                30 September 2011
                : 6
                : 9
                : e24689
                Affiliations
                [1 ]Institute for Evolutionary Biology and Ecology, University of Bonn, Bonn, Germany
                [2 ]Zoologisches Forschungsmuseum Alexander Koenig, Bonn, Germany
                [3 ]Zentrum für Didaktik der Biologie, University of Münster, Münster, Germany
                Lund University, Sweden
                Author notes

                Conceived and designed the experiments: TCMB HK TT. Analyzed the data: KL JS TT. Wrote the paper: KL TT. Performed the fieldwork: HK TT. Performed the laboratory work: KL JS. Discussed the results and improved the manuscript: KL JS HK TCMB TT.

                Article
                PONE-D-11-02242
                10.1371/journal.pone.0024689
                3184091
                21980351
                fd621bda-50ce-4e79-868a-06701f66f331
                Langen 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
                : 27 January 2011
                : 18 August 2011
                Page count
                Pages: 9
                Categories
                Research Article
                Biology
                Evolutionary Biology
                Population Genetics
                Gene Flow
                Genetic Drift
                Genetic Polymorphism
                Evolutionary Genetics
                Population Biology
                Population Genetics
                Gene Flow
                Genetic Drift
                Genetic Polymorphism
                Zoology
                Ichthyology

                Uncategorized
                Uncategorized

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