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      Signatures of selection and environmental adaptation across the goat genome post-domestication

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          Abstract

          Background

          Since goat was domesticated 10,000 years ago, many factors have contributed to the differentiation of goat breeds and these are classified mainly into two types: (i) adaptation to different breeding systems and/or purposes and (ii) adaptation to different environments. As a result, approximately 600 goat breeds have developed worldwide; they differ considerably from one another in terms of phenotypic characteristics and are adapted to a wide range of climatic conditions. In this work, we analyzed the AdaptMap goat dataset, which is composed of data from more than 3000 animals collected worldwide and genotyped with the CaprineSNP50 BeadChip. These animals were partitioned into groups based on geographical area, production uses, available records on solid coat color and environmental variables including the sampling geographical coordinates, to investigate the role of natural and/or artificial selection in shaping the genome of goat breeds.

          Results

          Several signatures of selection on different chromosomal regions were detected across the different breeds, sub-geographical clusters, phenotypic and climatic groups. These regions contain genes that are involved in important biological processes, such as milk-, meat- or fiber-related production, coat color, glucose pathway, oxidative stress response, size, and circadian clock differences. Our results confirm previous findings in other species on adaptation to extreme environments and human purposes and provide new genes that could explain some of the differences between goat breeds according to their geographical distribution and adaptation to different environments.

          Conclusions

          These analyses of signatures of selection provide a comprehensive first picture of the global domestication process and adaptation of goat breeds and highlight possible genes that may have contributed to the differentiation of this species worldwide.

          Electronic supplementary material

          The online version of this article (10.1186/s12711-018-0421-y) contains supplementary material, which is available to authorized users.

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          Most cited references80

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          A fast and flexible statistical model for large-scale population genotype data: applications to inferring missing genotypes and haplotypic phase.

          We present a statistical model for patterns of genetic variation in samples of unrelated individuals from natural populations. This model is based on the idea that, over short regions, haplotypes in a population tend to cluster into groups of similar haplotypes. To capture the fact that, because of recombination, this clustering tends to be local in nature, our model allows cluster memberships to change continuously along the chromosome according to a hidden Markov model. This approach is flexible, allowing for both "block-like" patterns of linkage disequilibrium (LD) and gradual decline in LD with distance. The resulting model is also fast and, as a result, is practicable for large data sets (e.g., thousands of individuals typed at hundreds of thousands of markers). We illustrate the utility of the model by applying it to dense single-nucleotide-polymorphism genotype data for the tasks of imputing missing genotypes and estimating haplotypic phase. For imputing missing genotypes, methods based on this model are as accurate or more accurate than existing methods. For haplotype estimation, the point estimates are slightly less accurate than those from the best existing methods (e.g., for unrelated Centre d'Etude du Polymorphisme Humain individuals from the HapMap project, switch error was 0.055 for our method vs. 0.051 for PHASE) but require a small fraction of the computational cost. In addition, we demonstrate that the model accurately reflects uncertainty in its estimates, in that probabilities computed using the model are approximately well calibrated. The methods described in this article are implemented in a software package, fastPHASE, which is available from the Stephens Lab Web site.
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            An atlas of combinatorial transcriptional regulation in mouse and man.

            Combinatorial interactions among transcription factors are critical to directing tissue-specific gene expression. To build a global atlas of these combinations, we have screened for physical interactions among the majority of human and mouse DNA-binding transcription factors (TFs). The complete networks contain 762 human and 877 mouse interactions. Analysis of the networks reveals that highly connected TFs are broadly expressed across tissues, and that roughly half of the measured interactions are conserved between mouse and human. The data highlight the importance of TF combinations for determining cell fate, and they lead to the identification of a SMAD3/FLI1 complex expressed during development of immunity. The availability of large TF combinatorial networks in both human and mouse will provide many opportunities to study gene regulation, tissue differentiation, and mammalian evolution. (c) 2010 Elsevier Inc. All rights reserved.
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              Sequencing and automated whole-genome optical mapping of the genome of a domestic goat (Capra hircus).

              We report the ∼2.66-Gb genome sequence of a female Yunnan black goat. The sequence was obtained by combining short-read sequencing data and optical mapping data from a high-throughput whole-genome mapping instrument. The whole-genome mapping data facilitated the assembly of super-scaffolds >5× longer by the N50 metric than scaffolds augmented by fosmid end sequencing (scaffold N50 = 3.06 Mb, super-scaffold N50 = 16.3 Mb). Super-scaffolds are anchored on chromosomes based on conserved synteny with cattle, and the assembly is well supported by two radiation hybrid maps of chromosome 1. We annotate 22,175 protein-coding genes, most of which were recovered in the RNA-seq data of ten tissues. Comparative transcriptomic analysis of the primary and secondary follicles of a cashmere goat reveal 51 genes that are differentially expressed between the two types of hair follicles. This study, whose results will facilitate goat genomics, shows that whole-genome mapping technology can be used for the de novo assembly of large genomes.
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                Author and article information

                Contributors
                fbert@iastate.edu , franb@dtu.dk
                bertrand.servin@inra.fr
                andrea.talenti@unimi.it
                estelle.rochat@epfl.ch
                euisoo.kim@recombinetics.com
                claire.oget@inra.fr
                isabelle.palhiere@inra.fr
                alessandra.crisa@crea.gov.it
                gennaro.catillo@entecra.it
                roberto.steri@crea.gov.it
                marcel.amills@cragenomica.es
                licia.colli@unicatt.it
                gmarras@uoguelph.ca
                marco.milanesi.mm@gmail.com
                ezequielluis.nicolazzi@gmail.com
                ben.rosen@ars.usda.gov
                curt.vantassell@ars.usda.gov
                bernt.guldbrandtsen@mbg.au.dk
                tad@recombinetics.com
                gwenola.tosser-klopp@inra.fr
                alessandra.stella@tecnoparco.org
                mfrothsc@iastate.edu
                stephane.joost@epfl.ch
                paola.crepaldi@unimi.it
                Journal
                Genet Sel Evol
                Genet. Sel. Evol
                Genetics, Selection, Evolution : GSE
                BioMed Central (London )
                0999-193X
                1297-9686
                19 November 2018
                19 November 2018
                2018
                : 50
                : 57
                Affiliations
                [1 ]ISNI 0000 0004 1936 7312, GRID grid.34421.30, Department of Animal Science, , Iowa State University, ; Ames, IA 50011 USA
                [2 ]ISNI 0000 0001 2181 8870, GRID grid.5170.3, National Institute of Aquatic Resources, , Technical University of Denmark (DTU), ; 2800 Lyngby, Denmark
                [3 ]GenPhySE, INRA, Université de Toulouse, INPT, ENVT, 31326 Castanet Tolosan, France
                [4 ]ISNI 0000 0004 1757 2822, GRID grid.4708.b, Dipartimento di Medicina Veterinaria, , Università degli Studi di Milano, ; 20133 Milan, Italy
                [5 ]ISNI 0000000121839049, GRID grid.5333.6, Laboratory of Geographic Information Systems (LASIG), School of Architecture, Civil and Environmental Engineering (ENAC), , Ecole Polytechnique Fédérale de Lausanne (EPFL), ; 1015 Lausanne, Switzerland
                [6 ]GRID grid.427259.f, Recombinetics Inc, ; St Paul, 55104 MN USA
                [7 ]Consiglio per la ricerca in agricoltura e l’analisi dell’economia agraria (CREA) - Research Centre for Animal Production and Acquaculture, 00015 Monterotondo, Roma, Italy
                [8 ]GRID grid.7080.f, Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, Campus Universitat Autonoma de Barcelona, ; Bellaterra, 08193 Barcelona, Spain
                [9 ]ISNI 0000 0001 0941 3192, GRID grid.8142.f, DIANA Dipartimento di Scienze Animali, della Nutrizione e degli Alimenti, , Università Cattolica del S. Cuore, ; 29100 Piacenza, Italy
                [10 ]ISNI 0000 0001 0941 3192, GRID grid.8142.f, BioDNA Centro di Ricerca sulla Biodiversità e sul DNA Antico, , Università Cattolica del S. Cuore, ; 29100 Piacenza, Italy
                [11 ]ISNI 0000 0004 0604 0732, GRID grid.425375.2, Fondazione Parco Tecnologico Padano (PTP), ; 26900 Lodi, Italy
                [12 ]ISNI 0000 0001 2188 478X, GRID grid.410543.7, Department of Support, Production and Animal Health, School of Veterinary Medicine, , São Paulo State University (UNESP), ; Araçatuba, Brazil
                [13 ]Animal Genomics and Improvement Laboratory, ARS USDA, Beltsville, MD 20705 USA
                [14 ]ISNI 0000 0001 1956 2722, GRID grid.7048.b, Center for Quantitative Genetics and Genomics, , Aarhus University, ; 8830 Tjele, Denmark
                Author information
                http://orcid.org/0000-0003-4181-3895
                Article
                421
                10.1186/s12711-018-0421-y
                6240954
                30449276
                4574f040-c5b0-43f1-b479-e4f482fb7e36
                © The Author(s) 2018

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 10 November 2017
                : 15 October 2018
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                © The Author(s) 2018

                Genetics
                Genetics

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