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      Screen for Footprints of Selection during Domestication/Captive Breeding of Atlantic Salmon

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          Domesticated animals provide a unique opportunity to identify genomic targets of artificial selection to the captive environment. Here, we screened three independent domesticated/captive Atlantic salmon ( Salmo salar) strains and their wild progenitor populations in an effort to detect potential signals of domestication selection by typing of 261 SNPs and 70 microsatellite loci. By combining information from four different neutrality tests, in total ten genomic regions showed signs of directional selection based on multiple sources of evidence. Most of the identified candidate regions were rather small ranging from zero to a few centimorgans (cM) in the female Atlantic salmon linkage map. We also evaluated how adaptation from standing variation affects adjacent SNP and microsatellite variation along the chromosomes and, by using forward simulations with strong selection, we were able to generate genetic differentiation patterns comparable to the observed data. This study highlights the significance of standing genetic variation during the early stages of adaptation and represents a useful step towards identifying functional variants involved in domestication of Atlantic salmon.

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          Most cited references 113

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          Statistical significance for genomewide studies.

          With the increase in genomewide experiments and the sequencing of multiple genomes, the analysis of large data sets has become commonplace in biology. It is often the case that thousands of features in a genomewide data set are tested against some null hypothesis, where a number of features are expected to be significant. Here we propose an approach to measuring statistical significance in these genomewide studies based on the concept of the false discovery rate. This approach offers a sensible balance between the number of true and false positives that is automatically calibrated and easily interpreted. In doing so, a measure of statistical significance called the q value is associated with each tested feature. The q value is similar to the well known p value, except it is a measure of significance in terms of the false discovery rate rather than the false positive rate. Our approach avoids a flood of false positive results, while offering a more liberal criterion than what has been used in genome scans for linkage.
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            Estimating F-Statistics for the Analysis of Population Structure

             B Weir,  C. Cockerham (1984)
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              GENEPOP (Version 1.2): Population Genetics Software for Exact Tests and Ecumenicism

               M. Raymond,  F Rousset (1995)

                Author and article information

                Comp Funct Genomics
                Comp. Funct. Genomics
                Comparative and Functional Genomics
                Hindawi Publishing Corporation
                27 December 2012
                : 2012
                1Division of Genetics and Physiology, Department of Biology, University of Turku, 20014 Turku, Finland
                2Department of Aquaculture, Institute of Veterinary Medicine and Animal Science, Estonian University of Life Sciences, 51014 Tartu, Estonia
                3Department of Wildlife, Fish, and Environmental Studies, Swedish University of Agricultural Sciences, 901 83 Umeå, Sweden
                4Aquaculture and Fisheries Development Centre, School of Biological, Earth, and Environmental Sciences, University College Cork, Cork, Ireland
                5Marine Institute, Furnace, Newport, Co. Mayo, Ireland
                6Population Ecology Division, Department of Fisheries and Oceans, Bedford Institute of Oceanography, Challenger Drive, Dartmouth, NS, Canada B2Y 4A2
                7Fisheries and Oceans Canada, Department of Fisheries and Oceans, St. Andrews Biological Station, St. Andrews, NB, Canada E0G 2X0
                8Department of Genetics, The University of Texas M. D. Anderson Cancer Center, Houston, TX 77030, USA
                9Department of Animal and Aquacultural Sciences, Centre for Integrative Genetics, Norwegian University of Life Sciences, 1432 Aas, Norway
                Author notes
                *Anti Vasemägi: antvas@

                Academic Editor: Mohamed Salem

                Copyright © 2012 Anti Vasemägi et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                Research Article



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