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      A High Density SNP Array for the Domestic Horse and Extant Perissodactyla: Utility for Association Mapping, Genetic Diversity, and Phylogeny Studies

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          Abstract

          An equine SNP genotyping array was developed and evaluated on a panel of samples representing 14 domestic horse breeds and 18 evolutionarily related species. More than 54,000 polymorphic SNPs provided an average inter-SNP spacing of ∼43 kb. The mean minor allele frequency across domestic horse breeds was 0.23, and the number of polymorphic SNPs within breeds ranged from 43,287 to 52,085. Genome-wide linkage disequilibrium (LD) in most breeds declined rapidly over the first 50–100 kb and reached background levels within 1–2 Mb. The extent of LD and the level of inbreeding were highest in the Thoroughbred and lowest in the Mongolian and Quarter Horse. Multidimensional scaling (MDS) analyses demonstrated the tight grouping of individuals within most breeds, close proximity of related breeds, and less tight grouping in admixed breeds. The close relationship between the Przewalski's Horse and the domestic horse was demonstrated by pair-wise genetic distance and MDS. Genotyping of other Perissodactyla (zebras, asses, tapirs, and rhinoceros) was variably successful, with call rates and the number of polymorphic loci varying across taxa. Parsimony analysis placed the modern horse as sister taxa to Equus przewalski. The utility of the SNP array in genome-wide association was confirmed by mapping the known recessive chestnut coat color locus ( MC1R) and defining a conserved haplotype of ∼750 kb across all breeds. These results demonstrate the high quality of this SNP genotyping resource, its usefulness in diverse genome analyses of the horse, and potential use in related species.

          Author Summary

          We utilized the previously generated horse genome sequence and a large SNP database to design an ∼54,000 SNP assay for use in the domestic horse and related species. The utility of this SNP array was demonstrated through genome-wide linkage disequilibrium, inbreeding and genetic distance measurements within breeds, as well as multidimensional scaling and parsimony analysis. Association mapping confirmed a large conserved segment containing the chestnut coat color locus in domestic horses. We also assess the utility of the SNP array in related species, including the Przewalski's Horse, zebras, asses, tapirs, and rhinoceros. This SNP genotyping tool will facilitate many genetics applications in equids, including identification of genes for health and performance traits, and compelling studies of the origins of the domestic horse, diversity within breeds, and evolutionary relationships among related species.

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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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            Development and Characterization of a High Density SNP Genotyping Assay for Cattle

            The success of genome-wide association (GWA) studies for the detection of sequence variation affecting complex traits in human has spurred interest in the use of large-scale high-density single nucleotide polymorphism (SNP) genotyping for the identification of quantitative trait loci (QTL) and for marker-assisted selection in model and agricultural species. A cost-effective and efficient approach for the development of a custom genotyping assay interrogating 54,001 SNP loci to support GWA applications in cattle is described. A novel algorithm for achieving a compressed inter-marker interval distribution proved remarkably successful, with median interval of 37 kb and maximum predicted gap of <350 kb. The assay was tested on a panel of 576 animals from 21 cattle breeds and six outgroup species and revealed that from 39,765 to 46,492 SNP are polymorphic within individual breeds (average minor allele frequency (MAF) ranging from 0.24 to 0.27). The assay also identified 79 putative copy number variants in cattle. Utility for GWA was demonstrated by localizing known variation for coat color and the presence/absence of horns to their correct genomic locations. The combination of SNP selection and the novel spacing algorithm allows an efficient approach for the development of high-density genotyping platforms in species having full or even moderate quality draft sequence. Aspects of the approach can be exploited in species which lack an available genome sequence. The BovineSNP50 assay described here is commercially available from Illumina and provides a robust platform for mapping disease genes and QTL in cattle.
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              Genome-wide survey of SNP variation uncovers the genetic structure of cattle breeds.

              The imprints of domestication and breed development on the genomes of livestock likely differ from those of companion animals. A deep draft sequence assembly of shotgun reads from a single Hereford female and comparative sequences sampled from six additional breeds were used to develop probes to interrogate 37,470 single-nucleotide polymorphisms (SNPs) in 497 cattle from 19 geographically and biologically diverse breeds. These data show that cattle have undergone a rapid recent decrease in effective population size from a very large ancestral population, possibly due to bottlenecks associated with domestication, selection, and breed formation. Domestication and artificial selection appear to have left detectable signatures of selection within the cattle genome, yet the current levels of diversity within breeds are at least as great as exists within humans.
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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
                January 2012
                January 2012
                12 January 2012
                : 8
                : 1
                : e1002451
                Affiliations
                [1 ]College of Veterinary Medicine, University of Minnesota, St. Paul, Minnesota, United States of America
                [2 ]Department of Population Health and Reproduction, School of Veterinary Medicine, University of California Davis, Davis, California, United States of America
                [3 ]Faculty of Veterinary Science, University of Sydney, Sydney, Australia
                [4 ]Maxwell H. Gluck Equine Research Center, Department of Veterinary Science, University of Kentucky, Lexington, Kentucky, United States of America
                [5 ]Equine Analysis Systems, Midway, Kentucky, United States of America
                [6 ]Institute of Animal Breeding and Genetics, University of Veterinary Medicine Hannover, Hannover, Germany
                [7 ]INRA, UMR 1313, Génétique Animale et Biologie Intégrative, Biologie Intégrative et Génétique Equine, Jouy-en-Josas, France
                [8 ]Equine Research Institute, Japan Racing Association, Utsunomiya, Japan
                [9 ]Animal Genomics Laboratory, School of Agriculture and Food Science, University College Dublin, Dublin, Ireland
                [10 ]Institute of Genetics, Vetsuisse Faculty, University of Bern, Berne, Switzerland
                [11 ]Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Uppsala, Sweden
                [12 ]Veterinary Genetics Laboratory, University of California Davis, Davis, California, United States of America
                [13 ]Department of Basic Sciences and Aquatic Medicine, Norwegian School of Veterinary Science, Oslo, Norway
                [14 ]San Diego Zoo's Institute for Conservation Research, Escondido, California, United States of America
                [15 ]Animal Health Trust, Suffolk, United Kingdom
                [16 ]Department of Molecular Genetics, Laboratory of Racing Chemistry, Utsunomiya, Japan
                [17 ]Science for Life Laboratory, Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden
                [18 ]Broad Institute, Cambridge, Massachusetts, United States of America
                University of Liège, Belgium
                Author notes

                Conceived and designed the experiments: MEM DLB MMB KL-T CMW JRM OAR. Analyzed the data: MEM JLP JG DLB CMW. Contributed reagents/materials/analysis tools: EB MMB OD GG TH EWH TL GL MCTP KHR OAR JES TT SJV MV. Wrote the paper: MEM JLP JRM.

                Article
                PGENETICS-D-10-00603
                10.1371/journal.pgen.1002451
                3257288
                22253606
                8e40420b-c556-479e-adeb-e7853b56107b
                McCue 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
                : 22 December 2010
                : 21 November 2011
                Page count
                Pages: 14
                Categories
                Research Article
                Agriculture
                Biology
                Genetics
                Genomics
                Population Biology
                Veterinary Science
                Animal Types

                Genetics
                Genetics

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