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      Genome-Wide Analysis Reveals Selection for Important Traits in Domestic Horse Breeds

      1 , * , 1 , 2 , 1 , 3 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 10 , 11 , 12 , 13 , 4 , 14 , 15 , 16 , 17 , 18 , 19 , 3 , 17 , 9 , 18 ,   3 , 20 , 5 , 21 , 17 , 22 , 23 , 13 , 24 , 25 , 13 , 15 , 1

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          Intense selective pressures applied over short evolutionary time have resulted in homogeneity within, but substantial variation among, horse breeds. Utilizing this population structure, 744 individuals from 33 breeds, and a 54,000 SNP genotyping array, breed-specific targets of selection were identified using an F ST-based statistic calculated in 500-kb windows across the genome. A 5.5-Mb region of ECA18, in which the myostatin (MSTN) gene was centered, contained the highest signature of selection in both the Paint and Quarter Horse. Gene sequencing and histological analysis of gluteal muscle biopsies showed a promoter variant and intronic SNP of MSTN were each significantly associated with higher Type 2B and lower Type 1 muscle fiber proportions in the Quarter Horse, demonstrating a functional consequence of selection at this locus. Signatures of selection on ECA23 in all gaited breeds in the sample led to the identification of a shared, 186-kb haplotype including two doublesex related mab transcription factor genes ( DMRT2 and 3). The recent identification of a DMRT3 mutation within this haplotype, which appears necessary for the ability to perform alternative gaits, provides further evidence for selection at this locus. Finally, putative loci for the determination of size were identified in the draft breeds and the Miniature horse on ECA11, as well as when signatures of selection surrounding candidate genes at other loci were examined. This work provides further evidence of the importance of MSTN in racing breeds, provides strong evidence for selection upon gait and size, and illustrates the potential for population-based techniques to find genomic regions driving important phenotypes in the modern horse.

          Author Summary

          A breed of the horse typically consists of individuals sharing very similar aesthetic and performance traits. However, a great deal of variation in traits exists between breeds. The range of variation observed among breeds can be illustrated by the size difference between the Miniature horse (0.74 m and 100 kg) and draft horse (1.8 m and 900 kg), or by comparing the optimum racing distance of the Quarter Horse (1/4 mile) to that of the Arabian (100 miles or more). In this study, we exploited the breed structure of the horse to identify regions of the genome that are significantly different between breeds and therefore may harbor genes and genetic variants targeted by selective breeding. This work resulted in the identification of variants in the Paint and Quarter Horse significantly associated with altered muscle fiber type proportions favorable for increased sprinting ability. A strong signature of selection was also identified in breeds that perform alternative gaits, and several genomic regions identified are hypothesized to be involved in the determination of size. This study has demonstrated the utility of this approach for studying the equine genome and is the first to show a functional consequence of selective breeding in the horse.

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          Most cited references 56

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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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            A mutation creating a potential illegitimate microRNA target site in the myostatin gene affects muscularity in sheep.

            Texel sheep are renowned for their exceptional meatiness. To identify the genes underlying this economically important feature, we performed a whole-genome scan in a Romanov x Texel F2 population. We mapped a quantitative trait locus with a major effect on muscle mass to chromosome 2 and subsequently fine-mapped it to a chromosome interval encompassing the myostatin (GDF8) gene. We herein demonstrate that the GDF8 allele of Texel sheep is characterized by a G to A transition in the 3' UTR that creates a target site for mir1 and mir206, microRNAs (miRNAs) that are highly expressed in skeletal muscle. This causes translational inhibition of the myostatin gene and hence contributes to the muscular hypertrophy of Texel sheep. Analysis of SNP databases for humans and mice demonstrates that mutations creating or destroying putative miRNA target sites are abundant and might be important effectors of phenotypic variation.
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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.

                Author and article information

                Role: Editor
                PLoS Genet
                PLoS Genet
                PLoS Genetics
                Public Library of Science (San Francisco, USA )
                January 2013
                January 2013
                17 January 2013
                : 9
                : 1
                [1 ]College of Veterinary Medicine, University of Minnesota, St. Paul, Minnesota, United States of America
                [2 ]School of Statistics, University of Minnesota, Minneapolis, Minnesota, United States of America
                [3 ]Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Uppsala, Sweden
                [4 ]Department of Veterinary Science, University of Kentucky, Lexington, Kentucky, United States of America
                [5 ]School of Veterinary Medicine, University of California Davis, Davis, California, United States of America
                [6 ]Equine Analysis, Midway, Kentucky, United States of America
                [7 ]Department of Veterinary Clinical Science, University Estadual Paulista, Botucatu, Brazil
                [8 ]School of Veterinary Medicine, University College Dublin, Dublin, Ireland
                [9 ]Department of Agriculture, University of the Azores, Angra do Heroísmo, Portugal
                [10 ]Faculty of Veterinary Medicine, University of Perugia, Perugia, Italy
                [11 ]College of Veterinary Medicine and Biomedical Science, Texas A&M University, College Station, Texas, United States of America
                [12 ]Institute for Animal Breeding and Genetics, University of Veterinary Medicine Hannover, Hannover, Germany
                [13 ]Animal Health Trust, Lanwades Park, Newmarket, Suffolk, United Kingdom
                [14 ]Animal Genetics and Integrative Biology Unit, French National Institute for Agricultural Research, Jouy en Josas, France
                [15 ]Faculty of Veterinary Science, University of Sydney, New South Wales, Australia
                [16 ]Nihon Bioresource College, Koga, Ibaraki, Japan
                [17 ]Faculty of Veterinary Medicine, University of Helsinki, Helsinki, Finland
                [18 ]College of Agriculture, Food Science and Veterinary Medicine, University College Dublin, Dublin, Ireland
                [19 ]Institute of Genetics, University of Bern, Bern, Switzerland
                [20 ]Breakthrough Breast Cancer Research Centre, Institute of Cancer Research, London, United Kingdom
                [21 ]Comparative Neuromuscular Diseases Laboratory, Royal Veterinary College, London, United Kingdom
                [22 ]Swiss National Stud Farm SNSTF, Agroscope Liebefeld-Posieux Research Station, Avenches, Switzerland
                [23 ]Department of Basic Sciences and Aquatic Medicine, Norwegian School of Veterinary Science, Oslo, Norway
                [24 ]Animal DNA Diagnostics, Cambridge, United Kingdom
                [25 ]Department of Molecular Genetics, Laboratory of Racing Chemistry, Utsunomiya, Tochigi, Japan
                University of Washington, United States of America
                Author notes

                Equinome Ltd. (EWH, Director) has been granted a license for commercial use of MSTN data as contained within patent applications: U.S. Provisional Serial Number 61/136553; Irish Patent Application Numbers 2008/0735 and 2010/0151; and Patent Cooperation Treaty number PCT/IE2009/000062. The PCT publication WO2010/029527A published 18 March 2010. Title: “A method for predicting athletics performance potential” and U.S. publication US2011/0262915 published 27 October 2011. Title: “Method for predicting the athletic performance potential of a subject.” EWH, NO, and BAM are named on the applications. MMB works for The Genetic Edge, previously published a paper on the association between SNPs in the MSTN region and best racing distance for elite Thoroughbred horses [46], and uses these markers in commercial tests. LSA and GL are co-applicants on a patent application concerning the commercial utilization of the DMRT3 mutation. These commercial ventures had no influence on the interpretation of the results relating to myostatin or gait presented in the paper.

                Conceived and designed the experiments: JLP JRM SJV MEM. Performed the experiments: JLP AKR SJV MEM. Analyzed the data: JLP AKR MEM. Contributed reagents/materials/analysis tools: JLP JRM AKR SJV LSA JA EB DB MMB ASB PB AdCM SC KC EGC OD LF-C KTG GG BH TH KH EWH TL GL HL MSL BAM SM NO MCTP RJP MR SR KHR JS TT MV CMW MEM. Wrote the paper: JLP JRM MEM.


                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.

                Page count
                Pages: 17
                This work was supported by National Research Initiative Competitive Grants 2008-35205-18766, 2009-55205-05254, and 2012-67015-19432 from the USDA-NIFA; Foundation for the Advancement of the Tennessee Walking Show Horse and Tennessee Walking Horse Foundation; NIH-NAIMS grant 1K08AR055713-01A2 (MEM salary support) and 2T32AR007612 (JLP salary support); American Quarter Horse Foundation grant “Selective Breeding Practices in the American Quarter Horse: Impact on Health and Disease 2011-2012”; Morris Animal Foundation Grant D07EQ-500; The Swedish Research Council FORMAS (Contract 221-2009-1631 and 2008-617); The Swedish-Norwegian Foundation for Equine Research (Contract H0847211 and H0947256); The Carl Tryggers Stiftelse (Contract CTS 08:29); support to IBB-CBA-UAç (University of the Azores) by FCT and DRCT, and to MSL by FRCT/2011/317/005; Science Foundation Ireland Award (04/Y11/B539) to EWH; Volkswagen Stiftung und Niedersächsisches Ministerium für Wissenschaft und Kultur, Germany (VWZN2012). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Research Article
                Evolutionary Biology
                Evolutionary Genetics
                Genomic Evolution
                Population Genetics
                Genetic Mutation
                Mutational Hypotheses
                Population Genetics
                Genetic Drift
                Genetic Polymorphism
                Natural Selection
                Animal Genetics
                Gene Function
                Genome Evolution



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