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      Population genetics of Indian giant river-catfish, Sperata seenghala(Sykes, 1839) using microsatellite markers

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          Abstract

          The giant river-catfish Sperata seenghalais one of the commercially important freshwater catfishes of India with wide distribution in all major rivers and reservoirs. This fish has huge demand in domestic market due to high nutritional value and low number of intramuscular bones. Conversely, the culture practices for this fish have not yet been standardized and capture fisheries is the only source to meet the demand. This may lead to over exploitation of resources and subsequent population reduction. Knowledge on genetic structure of populations is prerequisite to formulate sustainable management and conservation measures. In the present study, 15 microsatellites were used to characterize population genetics of S. seenghalacollected from river Brahmaputra, Ganga, Godavari, Mahanadi and Narmada. Locus-wise, the number of alleles varied from 8 to 19 with an average of 12 alleles per locus. The mean observed and expected heterozygosity values varied from 0.622 to 0.699 and 0.733 to 0.774, respectively. Several loci have shown deviation from Hardy–Weinberg equilibrium and no significant linkage disequilibrium between pairs of loci was detected. Pair-wise F STvalues between populations ranged from 0.135 (Brahmaputra–Ganga) to 0.173 (Brahmaputra–Narmada) and confirmed the moderate to high genetic differentiation among the populations. AMOVA, Structure and Principal Co-ordinate analyses showed significant genetic differentiation among the sampled populations of S. seenghala. A total of 65 private alleles were recorded across populations. This study confirmed the distinctiveness of each population of S. seenghalafrom five major rivers of India. These populations could be treated as distinct management units (MUs) for assessment and management purpose.

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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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            Inference of Population Structure Using Multilocus Genotype Data

            We describe a model-based clustering method for using multilocus genotype data to infer population structure and assign individuals to populations. We assume a model in which there are K populations (where K may be unknown), each of which is characterized by a set of allele frequencies at each locus. Individuals in the sample are assigned (probabilistically) to populations, or jointly to two or more populations if their genotypes indicate that they are admixed. Our model does not assume a particular mutation process, and it can be applied to most of the commonly used genetic markers, provided that they are not closely linked. Applications of our method include demonstrating the presence of population structure, assigning individuals to populations, studying hybrid zones, and identifying migrants and admixed individuals. We show that the method can produce highly accurate assignments using modest numbers of loci—e.g., seven microsatellite loci in an example using genotype data from an endangered bird species. The software used for this article is available from http://www.stats.ox.ac.uk/~pritch/home.html.
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              genalex 6: genetic analysis in Excel. Population genetic software for teaching and research

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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                Aquatic Living Resources
                Aquat. Living Resour.
                EDP Sciences
                1765-2952
                2019
                February 15 2019
                2019
                : 32
                : 4
                Article
                10.1051/alr/2019002
                14241dba-fc73-4323-934f-79979905959f
                © 2019

                https://www.edpsciences.org/en/authors/copyright-and-licensing

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