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      Structural variants in genes associated with human Williams-Beuren syndrome underlie stereotypical hypersociability in domestic dogs

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          Abstract

          We hypothesize that selection during dog domestication targeted CNVs associated with hypersociability.

          Abstract

          Although considerable progress has been made in understanding the genetic basis of morphologic traits (for example, body size and coat color) in dogs and wolves, the genetic basis of their behavioral divergence is poorly understood. An integrative approach using both behavioral and genetic data is required to understand the molecular underpinnings of the various behavioral characteristics associated with domestication. We analyze a 5-Mb genomic region on chromosome 6 previously found to be under positive selection in domestic dog breeds. Deletion of this region in humans is linked to Williams-Beuren syndrome (WBS), a multisystem congenital disorder characterized by hypersocial behavior. We associate quantitative data on behavioral phenotypes symptomatic of WBS in humans with structural changes in the WBS locus in dogs. We find that hypersociability, a central feature of WBS, is also a core element of domestication that distinguishes dogs from wolves. We provide evidence that structural variants in GTF2I and GTF2IRD1, genes previously implicated in the behavioral phenotype of patients with WBS and contained within the WBS locus, contribute to extreme sociability in dogs. This finding suggests that there are commonalities in the genetic architecture of WBS and canine tameness and that directional selection may have targeted a unique set of linked behavioral genes of large phenotypic effect, allowing for rapid behavioral divergence of dogs and wolves, facilitating coexistence with humans.

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          Most cited references 102

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          The Sequence Alignment/Map format and SAMtools

          Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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            Fast and accurate short read alignment with Burrows–Wheeler transform

            Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk
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              PLINK: a tool set for whole-genome association and population-based linkage analyses.

              Whole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis.
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                Author and article information

                Journal
                Sci Adv
                Sci Adv
                SciAdv
                advances
                Science Advances
                American Association for the Advancement of Science
                2375-2548
                July 2017
                19 July 2017
                : 3
                : 7
                Affiliations
                [1 ]Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA.
                [2 ]Translational Genetics and Genomics Unit, National Institute of Arthritis and Musculoskeletal and Skin Disorders, National Institutes of Health, U.S. Department of Health and Human Services, Bethesda, MD 20892, USA.
                [3 ]Cancer Genetics and Comparative Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA.
                [4 ]Department of Animal and Rangeland Sciences, Oregon State University, OR 97331, USA.
                [5 ]Yellowstone Center for Resources, National Park Service, Yellowstone National Park, WY 82190, USA.
                [6 ]Department of Psychology, Arizona State University, Tempe, AZ 85287, USA.
                [7 ]Departments of Human Genetics and Biomathematics, David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, CA 90095, USA.
                Author notes
                [*]

                These authors contributed equally to this work.

                []Corresponding author. vonHoldt@ 123456princeton.edu
                Article
                1700398
                10.1126/sciadv.1700398
                5517105
                Copyright © 2017 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).

                This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license, which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.

                Funding
                Funded by: National Institutes of Health (US);
                Award ID: award321887
                Award ID: GM086887
                Funded by: National Science Foundation (US);
                Award ID: award321886
                Award ID: DEB-1245373
                Funded by: National Science Foundation (US);
                Award ID: award321888
                Award ID: DMS 1264153
                Funded by: Princeton University (US);
                Award ID: award321885
                Award ID: EEB, Dean of the College, Council on Science and Technology
                Categories
                Research Article
                Research Articles
                SciAdv r-articles
                Life Sciences
                Genetics
                Custom metadata
                Ken Marvin Ortega

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