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      Identification of Genomic Regions Associated with Phenotypic Variation between Dog Breeds using Selection Mapping

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          Abstract

          The extraordinary phenotypic diversity of dog breeds has been sculpted by a unique population history accompanied by selection for novel and desirable traits. Here we perform a comprehensive analysis using multiple test statistics to identify regions under selection in 509 dogs from 46 diverse breeds using a newly developed high-density genotyping array consisting of >170,000 evenly spaced SNPs. We first identify 44 genomic regions exhibiting extreme differentiation across multiple breeds. Genetic variation in these regions correlates with variation in several phenotypic traits that vary between breeds, and we identify novel associations with both morphological and behavioral traits. We next scan the genome for signatures of selective sweeps in single breeds, characterized by long regions of reduced heterozygosity and fixation of extended haplotypes. These scans identify hundreds of regions, including 22 blocks of homozygosity longer than one megabase in certain breeds. Candidate selection loci are strongly enriched for developmental genes. We chose one highly differentiated region, associated with body size and ear morphology, and characterized it using high-throughput sequencing to provide a list of variants that may directly affect these traits. This study provides a catalogue of genomic regions showing extreme reduction in genetic variation or population differentiation in dogs, including many linked to phenotypic variation. The many blocks of reduced haplotype diversity observed across the genome in dog breeds are the result of both selection and genetic drift, but extended blocks of homozygosity on a megabase scale appear to be best explained by selection. Further elucidation of the variants under selection will help to uncover the genetic basis of complex traits and disease.

          Author Summary

          There are hundreds of dog breeds that exhibit massive differences in appearance and behavior sculpted by tightly controlled selective breeding. This large-scale natural experiment has provided an ideal resource that geneticists can use to search for genetic variants that control these differences. With this goal, we developed a high-density array that surveys variable sites at more than 170,000 positions in the dog genome and used it to analyze genetic variation in 46 breeds. We identify 44 chromosomal regions that are extremely variable between breeds and are likely to control many of the traits that vary between them, including curly tails and sociality. Many other regions also bear the signature of strong artificial selection. We characterize one such region, known to associate with body size and ear type, in detail using “next-generation” sequencing technology to identify candidate mutations that may control these traits. Our results suggest that artificial selection has targeted genes involved in development and metabolism and that it may have increased the incidence of disease in dog breeds. Knowledge of these regions will be of great importance for uncovering the genetic basis of variation between dog breeds and for finding mutations that cause disease.

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          Most cited references41

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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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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              The genetics of human adaptation: hard sweeps, soft sweeps, and polygenic adaptation.

              There has long been interest in understanding the genetic basis of human adaptation. To what extent are phenotypic differences among human populations driven by natural selection? With the recent arrival of large genome-wide data sets on human variation, there is now unprecedented opportunity for progress on this type of question. Several lines of evidence argue for an important role of positive selection in shaping human variation and differences among populations. These include studies of comparative morphology and physiology, as well as population genetic studies of candidate loci and genome-wide data. However, the data also suggest that it is unusual for strong selection to drive new mutations rapidly to fixation in particular populations (the 'hard sweep' model). We argue, instead, for alternatives to the hard sweep model: in particular, polygenic adaptation could allow rapid adaptation while not producing classical signatures of selective sweeps. We close by discussing some of the likely opportunities for progress in the field. Copyright 2010 Elsevier Ltd. All rights reserved.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Genet
                plos
                plosgen
                PLoS Genetics
                Public Library of Science (San Francisco, USA )
                1553-7390
                1553-7404
                October 2011
                October 2011
                13 October 2011
                : 7
                : 10
                : e1002316
                Affiliations
                [1 ]Institut de Génétique et Développement de Rennes, CNRS-UMR6061, Université de Rennes 1, Rennes, France
                [2 ]Science for Life Laboratory, Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden
                [3 ]Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
                [4 ]Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
                [5 ]Department of Veterinary Biosciences, Research Programs Unit, Molecular Medicine, University of Helsinki and Folkhälsan Research Center, Helsinki, Finland
                [6 ]Illumina, San Diego, California, United States of America
                [7 ]FAS Center for Systems Biology, Harvard University, Cambridge, Massachusetts, United States of America
                [8 ]Department of Population Health and Reproduction, School of Veterinary Medicine, University of California Davis, Davis, California, United States of America
                [9 ]Department of Integrative Ecology, Doñana Biological Station (CSIC), Seville, Spain
                [10 ]Faculty of Life Sciences, Division of Genetics and Bioinformatics, Department of Basic Animal and Veterinary Sciences, University of Copenhagen, Frederiksberg, Denmark
                [11 ]Department of Clinical Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden
                University of Washington, United States of America
                Author notes

                Conceived and designed the experiments: KL-T CH MTW. Performed the experiments: GRP SS MSTH CTL. Analyzed the data: AV (di, FDR analysis), AR (phasing and imputation, Si, XP-EHH, shared haplotype analysis), TD (browser display), EA (coalescent simulations), KL-T (input on all analyses), CH (GO analysis), MTW (SNP discovery analysis, HD array design, dataset construction, breed relationships, across-breed GWAS, FST, chromosome 10 resequencing, FDR analysis). Contributed reagents/materials/analysis tools: TF EKK EHS DB CV HL FG MF JH ÅH CA KL-T. Wrote the paper: AV AR KL-T CH MTW.

                Article
                PGENETICS-D-11-00264
                10.1371/journal.pgen.1002316
                3192833
                22022279
                774a11b7-3fbf-49c7-a5ea-7f32d8831383
                Vaysse et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                History
                : 1 February 2011
                : 30 July 2011
                Page count
                Pages: 21
                Categories
                Research Article
                Biology
                Computational Biology
                Genomics
                Genome Analysis Tools
                Gene Ontologies
                Genetic Maps
                Genome Scans
                Genome-Wide Association Studies
                Comparative Genomics
                Genome Evolution
                Genome Sequencing
                Population Genetics
                Effective Population Size
                Gene Flow
                Gene Pool
                Genetic Drift
                Genetic Polymorphism
                Haplotypes
                Mutation
                Natural Selection
                Neutral Theory
                Evolutionary Modeling
                Evolutionary Biology
                Evolutionary Genetics
                Genetics
                Animal Genetics

                Genetics
                Genetics

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