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      Whole-genome resequencing reveals signatures of selection and timing of duck domestication

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          Abstract

          Background

          The genetic basis of animal domestication remains poorly understood, and systems with substantial phenotypic differences between wild and domestic populations are useful for elucidating the genetic basis of adaptation to new environments as well as the genetic basis of rapid phenotypic change. Here, we sequenced the whole genome of 78 individual ducks, from two wild and seven domesticated populations, with an average sequencing depth of 6.42X per individual.

          Results

          Our population and demographic analyses indicate a complex history of domestication, with early selection for separate meat and egg lineages. Genomic comparison of wild to domesticated populations suggests that genes that affect brain and neuronal development have undergone strong positive selection during domestication. Our F ST analysis also indicates that the duck white plumage is the result of selection at the melanogenesis-associated transcription factor locus.

          Conclusions

          Our results advance the understanding of animal domestication and selection for complex phenotypic traits.

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          Most cited references43

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          The genomic signature of dog domestication reveals adaptation to a starch-rich diet.

          The domestication of dogs was an important episode in the development of human civilization. The precise timing and location of this event is debated and little is known about the genetic changes that accompanied the transformation of ancient wolves into domestic dogs. Here we conduct whole-genome resequencing of dogs and wolves to identify 3.8 million genetic variants used to identify 36 genomic regions that probably represent targets for selection during dog domestication. Nineteen of these regions contain genes important in brain function, eight of which belong to nervous system development pathways and potentially underlie behavioural changes central to dog domestication. Ten genes with key roles in starch digestion and fat metabolism also show signals of selection. We identify candidate mutations in key genes and provide functional support for an increased starch digestion in dogs relative to wolves. Our results indicate that novel adaptations allowing the early ancestors of modern dogs to thrive on a diet rich in starch, relative to the carnivorous diet of wolves, constituted a crucial step in the early domestication of dogs.
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            Moderated statistical tests for assessing differences in tag abundance.

            Digital gene expression (DGE) technologies measure gene expression by counting sequence tags. They are sensitive technologies for measuring gene expression on a genomic scale, without the need for prior knowledge of the genome sequence. As the cost of sequencing DNA decreases, the number of DGE datasets is expected to grow dramatically. Various tests of differential expression have been proposed for replicated DGE data using binomial, Poisson, negative binomial or pseudo-likelihood (PL) models for the counts, but none of the these are usable when the number of replicates is very small. We develop tests using the negative binomial distribution to model overdispersion relative to the Poisson, and use conditional weighted likelihood to moderate the level of overdispersion across genes. Not only is our strategy applicable even with the smallest number of libraries, but it also proves to be more powerful than previous strategies when more libraries are available. The methodology is equally applicable to other counting technologies, such as proteomic spectral counts. An R package can be accessed from http://bioinf.wehi.edu.au/resources/
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              SNPhylo: a pipeline to construct a phylogenetic tree from huge SNP data

              Background Phylogenetic trees are widely used for genetic and evolutionary studies in various organisms. Advanced sequencing technology has dramatically enriched data available for constructing phylogenetic trees based on single nucleotide polymorphisms (SNPs). However, massive SNP data makes it difficult to perform reliable analysis, and there has been no ready-to-use pipeline to generate phylogenetic trees from these data. Results We developed a new pipeline, SNPhylo, to construct phylogenetic trees based on large SNP datasets. The pipeline may enable users to construct a phylogenetic tree from three representative SNP data file formats. In addition, in order to increase reliability of a tree, the pipeline has steps such as removing low quality data and considering linkage disequilibrium. A maximum likelihood method for the inference of phylogeny is also adopted in generation of a tree in our pipeline. Conclusions Using SNPhylo, users can easily produce a reliable phylogenetic tree from a large SNP data file. Thus, this pipeline can help a researcher focus more on interpretation of the results of analysis of voluminous data sets, rather than manipulations necessary to accomplish the analysis.
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                Author and article information

                Journal
                Gigascience
                Gigascience
                gigascience
                GigaScience
                Oxford University Press
                2047-217X
                09 April 2018
                April 2018
                09 April 2018
                : 7
                : 4
                : giy027
                Affiliations
                [1 ]State Key Laboratory of Animal Nutrition, Department of Animal Genetics and Breeding, National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China
                [2 ]Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
                [3 ]Department of Genetics, Evolution and Environment, University College London, London, UK
                [4 ]Department of Organismal Biology, Evolutionary Biology Centre, Uppsala University, Uppsala, Sweden
                [5 ]Centre of Evolutionary and Ecological Studies, Marine Evolution and Conservation Group, University of Groningen, Groningen, The Netherlands
                [6 ]Department of Animal Sciences, Center for Reproductive Biology, Veterinary and Biomedical Research Building, Washington State University, Pullman, United States
                [7 ]Beijing Municipal General Station of Animal Science, Beijing, China
                [8 ]Institute of Animal Husbandry and Veterinary Medicine, Anhui Academy of Agricultural Sciences, Hefei, China
                [9 ]Poultry Institute, Chinese Academy of Agriculture Science, Yangzhou, China
                [10 ]Institute of Pekin Duck, Beijing, China
                [11 ]Cherry Valley farms (xianghe) Co., Ltd, Langfang, China
                Author notes
                Corresponding address.Department of Animal Genetics and Breeding, National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China. E-mail: quluj@ 123456163.com

                These authors contributed equally to this work.

                Author information
                http://orcid.org/0000-0003-2748-9101
                Article
                giy027
                10.1093/gigascience/giy027
                6007426
                29635409
                58a3ba7d-879c-405d-827a-73d75eb8fe5c
                © The Author(s) 2018. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 06 November 2017
                : 10 January 2018
                : 18 March 2018
                Page count
                Pages: 11
                Funding
                Funded by: Modern Agro-industry Technology Research System
                Award ID: BAIC04–2017
                Funded by: European Research Council 10.13039/501100000781
                Award ID: 680951
                Categories
                Research

                duck,domestication,intensive selection,neuronal development,energy metabolism,plumage colouration

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