2
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Genome-wide diversity and demographic dynamics of Cameroon goats and their divergence from east African, north African, and Asian conspecifics

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Indigenous goats make significant contributions to Cameroon’s national and local economy, but little effort has been devoted to identifying the populations. Here, we assessed the genetic diversity and demographic dynamics of Cameroon goat populations using mitochondrial DNA (two populations) and autosomal markers (four populations) generated with the Caprine 50K SNP chip. To infer genetic relationships at continental and global level, genotype data on six goat populations from Ethiopia and one population each from Egypt, Morocco, Iran, and China were included in the analysis. The mtDNA analysis revealed 83 haplotypes, all belonging to haplogroup A, in Cameroon goats. Four haplotypes were shared between goats found in Cameroon, Mozambique, Namibia, Zimbabwe, Kenya, and Ethiopia. Analysis of autosomal SNPs in Cameroon goats revealed the lowest H O (0.335±0.13) and H E (0.352±0.15) in the North-west Highland and Central Highland populations, respectively. Overall, the highest H O (0.401±0.12) and H E (0.422±0.12) were found for Barki and Iranian goats, respectively. Barki goats had the highest average MAF, while Central Highland Cameroon goats had the lowest. Overall, Cameroon goats demonstrated high F IS. AMOVA revealed that 13.29% of the variation was explained by genetic differences between the six population groups. Low average F ST (0.01) suggests intermixing among Cameroon goats. All measures indicated that Cameroon goats are closer to Moroccan goats than to other goat populations. PCA and STRUCTURE analyses poorly differentiated the Cameroon goats, as did genetic distance, Neighbor-Net network, and neighbor-joining tree analyses. The haplotype analysis of mtDNA showed the initial dispersion of goats to Cameroon and central Africa from north-east Africa following the Nile Delta. Whereas, the approximate Bayesian computation indicated Cameroon goats were separated from Moroccan goats after 506 generations in later times (~1518 YA), as supported by the phylogenetic net-work and admixture outputs. Overall, indigenous goats in Cameroon show weak phylogenetic structure, suggesting either extensive intermixing.

          Related collections

          Most cited references51

          • Record: found
          • Abstract: found
          • Article: not found

          Signature of ancient population growth in a low-resolution mitochondrial DNA mismatch distribution.

          A mismatch distribution is a tabulation of the number of pairwise differences among all DNA sequences in a sample. In a population that has been stationary for a long time these distributions from nonrecombinant DNA sequences become ragged and erratic, whereas a population that has been growing generates mismatch distributions that are smooth and have a peak. The position of the peak reflects the time of the population growth. The signature of an ancient population expansion is apparent even in the low-resolution mtDNA typings described by Merriwether et al. (1991). The smoothness of the mismatch distribution, an indicator of population expansion, is hardly affected by population structure, whereas mean sequence divergence increases in a pooled sample from highly isolated subpopulations.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            Inferring population history with DIY ABC: a user-friendly approach to approximate Bayesian computation

            Summary: Genetic data obtained on population samples convey information about their evolutionary history. Inference methods can extract part of this information but they require sophisticated statistical techniques that have been made available to the biologist community (through computer programs) only for simple and standard situations typically involving a small number of samples. We propose here a computer program (DIY ABC) for inference based on approximate Bayesian computation (ABC), in which scenarios can be customized by the user to fit many complex situations involving any number of populations and samples. Such scenarios involve any combination of population divergences, admixtures and population size changes. DIY ABC can be used to compare competing scenarios, estimate parameters for one or more scenarios and compute bias and precision measures for a given scenario and known values of parameters (the current version applies to unlinked microsatellite data). This article describes key methods used in the program and provides its main features. The analysis of one simulated and one real dataset, both with complex evolutionary scenarios, illustrates the main possibilities of DIY ABC. Availability: The software DIY ABC is freely available at http://www.montpellier.inra.fr/CBGP/diyabc. Contact: j.cornuet@imperial.ac.uk Supplementary information: Supplementary data are also available at http://www.montpellier.inra.fr/CBGP/diyabc
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Balancing Selection and Its Effects on Sequences in Nearby Genome Regions

              Our understanding of balancing selection is currently becoming greatly clarified by new sequence data being gathered from genes in which polymorphisms are known to be maintained by selection. The data can be interpreted in conjunction with results from population genetics models that include recombination between selected sites and nearby neutral marker variants. This understanding is making possible tests for balancing selection using molecular evolutionary approaches. Such tests do not necessarily require knowledge of the functional types of the different alleles at a locus, but such information, as well as information about the geographic distribution of alleles and markers near the genes, can potentially help towards understanding what form of balancing selection is acting, and how long alleles have been maintained.
                Bookmark

                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: MethodologyRole: SupervisionRole: Writing – review & editing
                Role: Data curationRole: MethodologyRole: Writing – review & editing
                Role: Data curationRole: MethodologyRole: ResourcesRole: Writing – review & editing
                Role: Data curationRole: MethodologyRole: ResourcesRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: MethodologyRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: MethodologyRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: MethodologyRole: SupervisionRole: Writing – review & editing
                Role: MethodologyRole: ResourcesRole: Writing – review & editing
                Role: MethodologyRole: ResourcesRole: Writing – review & editing
                Role: Data curationRole: MethodologyRole: Writing – review & editing
                Role: Data curationRole: MethodologyRole: Writing – review & editing
                Role: MethodologyRole: ResourcesRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Funding acquisitionRole: MethodologyRole: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: MethodologyRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: MethodologyRole: SupervisionRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                19 April 2019
                2019
                : 14
                : 4
                : e0214843
                Affiliations
                [1 ] Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Uppsala, Sweden
                [2 ] Department of Animal Production and Technology, Bahir Dar University, Bahir Dar, Ethiopia
                [3 ] Biosciences Eastern and Central Africa-International Livestock Research Institute (BecA-ILRI) Hub, Nairobi, Kenya
                [4 ] Faculty of Agronomy and Agriculture, University of Dschang, Dschang, Cameroon
                [5 ] International Livestock Research Institute (ILRI), Nairobi, Kenya
                [6 ] Department of Animal Science, Chungbuk National University, Cheongju, Korea
                [7 ] Nei Mongol BioNew Technology Co.Ltd, Hohhot, China
                [8 ] College of Animal Science, Inner Mongolia Agricultural University, Hohhot, China
                [9 ] International Livestock Research Institute (ILRI), Addis Ababa, Ethiopia
                [10 ] Department of Microbial, Cellular and Molecular Biology, Addis Ababa University, Addis Ababa, Ethiopia
                [11 ] The University of Queensland, Queensland, Australia
                [12 ] Centre for Tropical Livestock Genetics and Health, The University of Edinburgh, Scotland, United Kingdom
                National Cheng Kung University, TAIWAN
                Author notes

                Competing Interests: Nei Mongol BioNew Technology Co.Ltd, Hohhot, 010020, China. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

                Author information
                http://orcid.org/0000-0001-7221-2473
                http://orcid.org/0000-0003-3558-0626
                Article
                PONE-D-18-21149
                10.1371/journal.pone.0214843
                6474588
                31002664
                cd26849a-d36d-4762-ba36-cef7eb86b24a
                © 2019 Tarekegn 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
                : 30 July 2018
                : 21 March 2019
                Page count
                Figures: 10, Tables: 5, Pages: 22
                Funding
                This project was supported by the BecA-ILRI Hub through the Africa Biosciences Challenge Fund (ABCF) program. The ABCF Program is funded by the Australian Department for Foreign Affairs and Trade (DFAT) through the BecA-CSIRO partnership; the Syngenta Foundation for Sustainable Agriculture (SFSA); the Bill & Melinda Gates Foundation (BMGF); the UK Department for International Development (DFID); and the Swedish International Development Cooperation Agency (Sida). Bin Liu is employed in a private company (Nei Mongol BioNew Technology Co.Ltd). However, the company had no any role in funding any part of the research except paying salary of the author (Bin Liu). The specific role of the author is articulated in the ‘author contributions’ section.
                Categories
                Research Article
                Custom metadata
                Mitochondrial DNA sequences of Cameroon goats are deposited in the GenBank (Accession No. MH621412-MH621504). Similarly, genotypic data of all the animals (included in the study) representing 14 goat populations are deposited and available at ( https://datadryad.org/review?doi=doi:10.5061/dryad.mc40jt6).

                Uncategorized
                Uncategorized

                Comments

                Comment on this article