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      Relative genomic impacts of translocation history, hatchery practices, and farm selection in Pacific oyster Crassostrea gigas throughout the Northern Hemisphere

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          Abstract

          Abstract Pacific oyster Crassostrea gigas, endemic to coastal Asia, has been translocated globally throughout the past century, resulting in self‐sustaining introduced populations (naturalized). Oyster aquaculture industries in many parts of the world depend on commercially available seed (hatchery‐farmed) or naturalized/wild oysters to move onto a farm (naturalized‐farmed). It is therefore important to understand genetic variation among populations and farm types. Here, we genotype naturalized/wild populations from France, Japan, China, and most extensively in coastal British Columbia, Canada. We also genotype cultured populations from throughout the Northern Hemisphere to compare with naturalized populations. In total, 16,942 markers were identified using double‐digest RAD‐sequencing in 182 naturalized, 112 hatchery‐farmed, and 72 naturalized‐farmed oysters (n = 366). Consistent with previous studies, very low genetic differentiation was observed around Vancouver Island (mean F ST = 0.0019) and low differentiation between countries in the Japan–Canada–France historical translocation lineage (France–Canada F ST = 0.0024; Japan–Canada F ST = 0.0060). Chinese populations were more differentiated (China–Japan F ST = 0.0241). Hatchery‐propagated populations had higher interindividual relatedness suggesting family structure. Within‐population inbreeding was not detected on farms, but nucleotide diversity and polymorphism rate were lower in one farm population. Moving oysters from nature onto farms did not result in strong within‐generation selection. Private alleles at substantial frequency were identified in several hatchery populations grown in BC, suggesting nonlocal origins. Tests of selection identified outlier loci consistent with selective differences associated with domestication, in some cases consistently identified in multiple farms. Top outlier candidates were nearby genes involved in calcium signaling and calmodulin activity. Implications of potential introgression from hatchery‐farmed oysters depend on whether naturalized populations are valued as a locally adapted resource or as an introduced, invasive species. Given the value of the industry in BC and the challenges the industry faces (e.g., climate change, crop losses, biotic stressors), this remains an important question.

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          Most cited references45

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          Estimators for pairwise relatedness and individual inbreeding coefficients

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            An estimator for pairwise relatedness using molecular markers.

            I propose a new estimator for jointly estimating two-gene and four-gene coefficients of relatedness between individuals from an outbreeding population with data on codominant genetic markers and compare it, by Monte Carlo simulations, to previous ones in precision and accuracy for different distributions of population allele frequencies, numbers of alleles per locus, actual relationships, sample sizes, and proportions of relatives included in samples. In contrast to several previous estimators, the new estimator is well behaved and applies to any number of alleles per locus and any allele frequency distribution. The estimates for two- and four-gene coefficients of relatedness from the new estimator are unbiased irrespective of the sample size and have sampling variances decreasing consistently with an increasing number of alleles per locus to the minimum asymptotic values determined by the variation in identity-by-descent among loci per se, regardless of the actual relationship. The new estimator is also robust for small sample sizes and for unknown relatives being included in samples for estimating allele frequencies. Compared to previous estimators, the new one is generally advantageous, especially for highly polymorphic loci and/or small sample sizes.
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              related: an R package for analysing pairwise relatedness from codominant molecular markers.

              Analyses of pairwise relatedness represent a key component to addressing many topics in biology. However, such analyses have been limited because most available programs provide a means to estimate relatedness based on only a single estimator, making comparison across estimators difficult. Second, all programs to date have been platform specific, working only on a specific operating system. This has the undesirable outcome of making choice of relatedness estimator limited by operating system preference, rather than being based on scientific rationale. Here, we present a new R package, called related, that can calculate relatedness based on seven estimators, can account for genotyping errors, missing data and inbreeding, and can estimate 95% confidence intervals. Moreover, simulation functions are provided that allow for easy comparison of the performance of different estimators and for analyses of how much resolution to expect from a given data set. Because this package works in R, it is platform independent. Combined, this functionality should allow for more appropriate analyses and interpretation of pairwise relatedness and will also allow for the integration of relatedness data into larger R workflows.
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                Author and article information

                Journal
                Evolutionary Applications
                Evol Appl
                Wiley
                1752-4571
                1752-4571
                April 17 2020
                Affiliations
                [1 ]Pacific Biological Station, Fisheries and Oceans Canada Nanaimo BC Canada
                [2 ]Department of Earth, Ocean and Atmospheric Sciences University of British Columbia Vancouver BC Canada
                [3 ]Department of Biology Universite Laval Quebec QC Canada
                [4 ]RKS Laboratories Ltd. Nanaimo BC Canada
                [5 ]Institute of Oceanology Chinese Academy of Sciences Qingdao China
                [6 ]Department of Zoology & Biodiversity Research Centre University of British Columbia Vancouver BC Canada
                [7 ]Department of Microbiology and Immunology and the Institute for the Oceans and Fisheries University of British Columbia Vancouver BC Canada
                [8 ]Department of Botany University of British Columbia Vancouver BC Canada
                Article
                10.1111/eva.12965
                78ecad86-7222-4b4e-94f9-9d2a37661bb0
                © 2020

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

                http://doi.wiley.com/10.1002/tdm_license_1.1

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