24
views
0
recommends
+1 Recommend
1 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found

      Prehistoric genomes reveal the genetic foundation and cost of horse domestication

      1 , 1 , 2 , 1 , 1 , 1 , 3 , 4 , 5 , 4 , 5 , 6 , 7 , 1 , 1 , 8 , 1 , 1 , 1 , 9 , 10 , 11 , 11 , 11 , 12 , 13 , 14 , 15 , 9 , 6 , 10 , 16 , 17 , 18 , 1 , 19 , 4 , 5 ,   1 , 2 , 1
      Proceedings of the National Academy of Sciences
      Proceedings of the National Academy of Sciences

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Significance

          The domestication of the horse revolutionized warfare, trade, and the exchange of people and ideas. This at least 5,500-y-long process, which ultimately transformed wild horses into the hundreds of breeds living today, is difficult to reconstruct from archeological data and modern genetics alone. We therefore sequenced two complete horse genomes, predating domestication by thousands of years, to characterize the genetic footprint of domestication. These ancient genomes reveal predomestic population structure and a significant fraction of genetic variation shared with the domestic breeds but absent from Przewalski’s horses. We find positive selection on genes involved in various aspects of locomotion, physiology, and cognition. Finally, we show that modern horse genomes contain an excess of deleterious mutations, likely representing the genetic cost of domestication.

          Abstract

          The domestication of the horse ∼5.5 kya and the emergence of mounted riding, chariotry, and cavalry dramatically transformed human civilization. However, the genetics underlying horse domestication are difficult to reconstruct, given the near extinction of wild horses. We therefore sequenced two ancient horse genomes from Taymyr, Russia (at 7.4- and 24.3-fold coverage), both predating the earliest archeological evidence of domestication. We compared these genomes with genomes of domesticated horses and the wild Przewalski’s horse and found genetic structure within Eurasia in the Late Pleistocene, with the ancient population contributing significantly to the genetic variation of domesticated breeds. We furthermore identified a conservative set of 125 potential domestication targets using four complementary scans for genes that have undergone positive selection. One group of genes is involved in muscular and limb development, articular junctions, and the cardiac system, and may represent physiological adaptations to human utilization. A second group consists of genes with cognitive functions, including social behavior, learning capabilities, fear response, and agreeableness, which may have been key for taming horses. We also found that domestication is associated with inbreeding and an excess of deleterious mutations. This genetic load is in line with the “cost of domestication” hypothesis also reported for rice, tomatoes, and dogs, and it is generally attributed to the relaxation of purifying selection resulting from the strong demographic bottlenecks accompanying domestication. Our work demonstrates the power of ancient genomes to reconstruct the complex genetic changes that transformed wild animals into their domesticated forms, and the population context in which this process took place.

          Related collections

          Most cited references87

          • Record: found
          • Abstract: found
          • Article: not found

          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.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data

            High-throughput sequencing platforms are generating massive amounts of genetic variation data for diverse genomes, but it remains a challenge to pinpoint a small subset of functionally important variants. To fill these unmet needs, we developed the ANNOVAR tool to annotate single nucleotide variants (SNVs) and insertions/deletions, such as examining their functional consequence on genes, inferring cytogenetic bands, reporting functional importance scores, finding variants in conserved regions, or identifying variants reported in the 1000 Genomes Project and dbSNP. ANNOVAR can utilize annotation databases from the UCSC Genome Browser or any annotation data set conforming to Generic Feature Format version 3 (GFF3). We also illustrate a ‘variants reduction’ protocol on 4.7 million SNVs and indels from a human genome, including two causal mutations for Miller syndrome, a rare recessive disease. Through a stepwise procedure, we excluded variants that are unlikely to be causal, and identified 20 candidate genes including the causal gene. Using a desktop computer, ANNOVAR requires ∼4 min to perform gene-based annotation and ∼15 min to perform variants reduction on 4.7 million variants, making it practical to handle hundreds of human genomes in a day. ANNOVAR is freely available at http://www.openbioinformatics.org/annovar/ .
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models.

              RAxML-VI-HPC (randomized axelerated maximum likelihood for high performance computing) is a sequential and parallel program for inference of large phylogenies with maximum likelihood (ML). Low-level technical optimizations, a modification of the search algorithm, and the use of the GTR+CAT approximation as replacement for GTR+Gamma yield a program that is between 2.7 and 52 times faster than the previous version of RAxML. A large-scale performance comparison with GARLI, PHYML, IQPNNI and MrBayes on real data containing 1000 up to 6722 taxa shows that RAxML requires at least 5.6 times less main memory and yields better trees in similar times than the best competing program (GARLI) on datasets up to 2500 taxa. On datasets > or =4000 taxa it also runs 2-3 times faster than GARLI. RAxML has been parallelized with MPI to conduct parallel multiple bootstraps and inferences on distinct starting trees. The program has been used to compute ML trees on two of the largest alignments to date containing 25,057 (1463 bp) and 2182 (51,089 bp) taxa, respectively. icwww.epfl.ch/~stamatak
                Bookmark

                Author and article information

                Contributors
                (View ORCID Profile)
                (View ORCID Profile)
                (View ORCID Profile)
                (View ORCID Profile)
                Journal
                Proceedings of the National Academy of Sciences
                Proc. Natl. Acad. Sci. U.S.A.
                Proceedings of the National Academy of Sciences
                0027-8424
                1091-6490
                December 30 2014
                December 15 2014
                December 30 2014
                : 111
                : 52
                Affiliations
                [1 ]Centre for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, 1350K Copenhagen, Denmark;
                [2 ]Department of Ecology and Evolutionary Biology, University of California, Santa Cruz, CA 95064;
                [3 ]The Bioinformatics Centre, Department of Biology, University of Copenhagen, 2200N Copenhagen, Denmark;
                [4 ]Institute of Ecology and Evolution, University of Berne, 3012 Berne, Switzerland;
                [5 ]Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland;
                [6 ]Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, 2800 Lyngby, Denmark;
                [7 ]UCL Genetics Institute, Department of Genetics, Evolution, and Environment, University College London, London WC1E 6BT, United Kingdom;
                [8 ]National High-Throughput DNA Sequencing Center, University of Copenhagen, 1353K Copenhagen, Denmark;
                [9 ]Department of Energy Joint Genome Institute, Walnut Creek, CA 94598;
                [10 ]Science for Life Laboratory, Department of Medical Biochemistry and Microbiology, Uppsala University, SE-751 23 Uppsala, Sweden;
                [11 ]Zoology Department, College of Science, King Saud University, Riyadh 11451, Saudi Arabia;
                [12 ]Animal Genomics Laboratory, UCD School of Agriculture and Food Science, University College Dublin, Belfield, Dublin 4, Ireland;
                [13 ]UCD Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin 4, Ireland;
                [14 ]Biochemistry and Molecular Biology, School of Medicine, University of Louisville, Louisville, KY 40292;
                [15 ]Department of Veterinary Science, Gluck Equine Research Center, University of Kentucky, Lexington, KY 40546;
                [16 ]Institute for Biochemistry and Biology, Faculty for Mathematics and Natural Sciences, University of Potsdam, 14476 Potsdam, Germany;
                [17 ]Instituticó Catalana de Recerca i Estudis Avançats, Institut de Biologia Evolutiva (Universitat Pompeu Fabra/Consejo Superior de Investigaciones Cientificas), 08003 Barcelona, Spain;
                [18 ]Centro Nacional de Análisis Genómico, 08028 Barcelona, Spain; and
                [19 ]Departments of Integrative Biology and Statistics, University of California, Berkeley, CA 94720
                Article
                10.1073/pnas.1416991111
                4284583
                25512547
                9ea8b071-87a7-43da-8906-44d0eda7cd94
                © 2014
                History

                Comments

                Comment on this article