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      Demographically-Based Evaluation of Genomic Regions under Selection in Domestic Dogs

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          Abstract

          Controlling for background demographic effects is important for accurately identifying loci that have recently undergone positive selection. To date, the effects of demography have not yet been explicitly considered when identifying loci under selection during dog domestication. To investigate positive selection on the dog lineage early in the domestication, we examined patterns of polymorphism in six canid genomes that were previously used to infer a demographic model of dog domestication. Using an inferred demographic model, we computed false discovery rates (FDR) and identified 349 outlier regions consistent with positive selection at a low FDR. The signals in the top 100 regions were frequently centered on candidate genes related to brain function and behavior, including LHFPL3, CADM2, GRIK3, SH3GL2, MBP, PDE7B, NTAN1, and GLRA1. These regions contained significant enrichments in behavioral ontology categories. The 3 rd top hit, CCRN4L, plays a major role in lipid metabolism, that is supported by additional metabolism related candidates revealed in our scan, including SCP2D1 and PDXC1. Comparing our method to an empirical outlier approach that does not directly account for demography, we found only modest overlaps between the two methods, with 60% of empirical outliers having no overlap with our demography-based outlier detection approach. Demography-aware approaches have lower-rates of false discovery. Our top candidates for selection, in addition to expanding the set of neurobehavioral candidate genes, include genes related to lipid metabolism, suggesting a dietary target of selection that was important during the period when proto-dogs hunted and fed alongside hunter-gatherers.

          Author Summary

          Identification of the genomic regions under selection during dog domestication is extremely challenging because the demographic fluctuations associated with domestication can produce signals in polymorphism data that mimic those imposed by selective sweeps. We perform the first analysis of selection on the dog lineage that explicitly incorporates a demographic model, that by controlling for the rate of false discovery, more robustly identifies targets of selection. To do so, we conduct a selection scan using three wolf genomes representing the putative centers of dog domestication, two basal dog breeds (Basenji and Dingo), and a golden jackal as outgroup, for which we previously inferred a demographic model. We find that our demographically informed analyses filters out many signals that would be otherwise classified as putative selection signals under an empirical outlier approach. We identify 68 regions of the genome that have likely experienced positive selection. Besides identifying a number of new neurobehavioral candidate genes, our candidate regions contain genes related to lipid metabolism, including CCRN4L, which is centered in the 3 rd ranked region. This suggests a previously unreported locus of dietary adaptation, potentially due to the change in diet composition as hunting efficiency increased when proto dogs began hunting alongside hunter-gatherers.

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          Most cited references52

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          Genomic scans for selective sweeps using SNP data.

          Detecting selective sweeps from genomic SNP data is complicated by the intricate ascertainment schemes used to discover SNPs, and by the confounding influence of the underlying complex demographics and varying mutation and recombination rates. Current methods for detecting selective sweeps have little or no robustness to the demographic assumptions and varying recombination rates, and provide no method for correcting for ascertainment biases. Here, we present several new tests aimed at detecting selective sweeps from genomic SNP data. Using extensive simulations, we show that a new parametric test, based on composite likelihood, has a high power to detect selective sweeps and is surprisingly robust to assumptions regarding recombination rates and demography (i.e., has low Type I error). Our new test also provides estimates of the location of the selective sweep(s) and the magnitude of the selection coefficient. To illustrate the method, we apply our approach to data from the Seattle SNP project and to Chromosome 2 data from the HapMap project. In Chromosome 2, the most extreme signal is found in the lactase gene, which previously has been shown to be undergoing positive selection. Evidence for selective sweeps is also found in many other regions, including genes known to be associated with disease risk such as DPP10 and COL4A3.
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            The genomic signature of dog domestication reveals adaptation to a starch-rich diet.

            The domestication of dogs was an important episode in the development of human civilization. The precise timing and location of this event is debated and little is known about the genetic changes that accompanied the transformation of ancient wolves into domestic dogs. Here we conduct whole-genome resequencing of dogs and wolves to identify 3.8 million genetic variants used to identify 36 genomic regions that probably represent targets for selection during dog domestication. Nineteen of these regions contain genes important in brain function, eight of which belong to nervous system development pathways and potentially underlie behavioural changes central to dog domestication. Ten genes with key roles in starch digestion and fat metabolism also show signals of selection. We identify candidate mutations in key genes and provide functional support for an increased starch digestion in dogs relative to wolves. Our results indicate that novel adaptations allowing the early ancestors of modern dogs to thrive on a diet rich in starch, relative to the carnivorous diet of wolves, constituted a crucial step in the early domestication of dogs.
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              Efficient mapping of mendelian traits in dogs through genome-wide association.

              With several hundred genetic diseases and an advantageous genome structure, dogs are ideal for mapping genes that cause disease. Here we report the development of a genotyping array with approximately 27,000 SNPs and show that genome-wide association mapping of mendelian traits in dog breeds can be achieved with only approximately 20 dogs. Specifically, we map two traits with mendelian inheritance: the major white spotting (S) locus and the hair ridge in Rhodesian ridgebacks. For both traits, we map the loci to discrete regions of <1 Mb. Fine-mapping of the S locus in two breeds refines the localization to a region of approximately 100 kb contained within the pigmentation-related gene MITF. Complete sequencing of the white and solid haplotypes identifies candidate regulatory mutations in the melanocyte-specific promoter of MITF. Our results show that genome-wide association mapping within dog breeds, followed by fine-mapping across multiple breeds, will be highly efficient and generally applicable to trait mapping, providing insights into canine and human health.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Genet
                PLoS Genet
                plos
                plosgen
                PLoS Genetics
                Public Library of Science (San Francisco, CA USA )
                1553-7390
                1553-7404
                4 March 2016
                March 2016
                : 12
                : 3
                : e1005851
                Affiliations
                [1 ]Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, California, United States of America
                [2 ]National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, United States of America
                [3 ]Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York, United States of America
                [4 ]CIBIO-UP, University of Porto, Vairão, Portugal
                [5 ]ISPRA, Ozzano dell'Emilia, Italy
                [6 ]Key Laboratory of Bioresources and Ecoenvironment, Sichuan University, Chengdu, China
                [7 ]Department of Measurement and Information Systems, Budapest University of Technology and Economics, Budapest, Hungary
                [8 ]ICREA at Institut de Biologia Evolutiva (CSIC-Universitat Pompeu Fabra), Barcelona, Spain
                [9 ]Department of Computer Science, University of California, Los Angeles, Los Angeles, California, United States of America
                [10 ]Bilkent University, Ankara, Turkey
                [11 ]Estación Biológia de Doñana EBD-CSIC, Sevilla, Spain
                [12 ]Department of Human Genetics, University of California, Los Angeles, Los Angeles, California, United States of America
                [13 ]Department of Zoology, Tel Aviv University, Tel Aviv, Israel
                [14 ]Department of Biology, University of Zagreb, Zagreb, Croatia
                [15 ]Department of Biomedical Sciences, Cornell University, Ithaca, New York, United States of America
                [16 ]Life Technologies, Foster City, California, United States of America
                [17 ]Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America
                [18 ]Stanford School of Medicine, Stanford, California, United States of America
                [19 ]Centro Nacional de Analisis Genomico (CNAG/PCB), Baldiri Reixach 4–8, Barcelona, Spain
                Uppsala University, SWEDEN
                Author notes

                CL, VT, and TTH were employed by Life Technologies during the time the research was conducted. Construction and sequencing of SOLiD Exact Call Chemistry libraries were also provided by Life Technologies. Life Technologies Corporation had no input into the development of the study, article preparation or decision to publish. No other competing interests are declared.

                Conceived and designed the experiments: AHF CDB TTH SFN EAO TMB RKW JN. Performed the experiments: RMS BLG OR CV KS ARB HGP CL VT. Analyzed the data: AHF RMS IG EH BWD DODV PMS MG ZF PM BLG OR FH CA VT AS JN. Contributed reagents/materials/analysis tools: CV EG JK EAO RKW IG. Wrote the paper: AHF JN RKW.

                [¤]

                Current address: Department of Human Genetics, University of Chicago, Chicago, Illinois, United States of America

                Article
                PGENETICS-D-15-02144
                10.1371/journal.pgen.1005851
                4778760
                26943675
                3843bc18-059f-428f-a8bf-11f9b181776e

                This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

                History
                : 26 August 2015
                : 18 January 2016
                Page count
                Figures: 5, Tables: 2, Pages: 23
                Funding
                The authors received no specific funding for this work.
                Categories
                Research Article
                Biology and Life Sciences
                Genetics
                Genomics
                Animal Genomics
                Mammalian Genomics
                Biology and Life Sciences
                Organisms
                Animals
                Animal Types
                Pets and Companion Animals
                Biology and Life Sciences
                Zoology
                Animal Types
                Pets and Companion Animals
                Biology and Life Sciences
                Organisms
                Animals
                Animal Types
                Domestic Animals
                Biology and Life Sciences
                Zoology
                Animal Types
                Domestic Animals
                Biology and Life Sciences
                Organisms
                Animals
                Vertebrates
                Mammals
                Dogs
                Biology and Life Sciences
                Computational Biology
                Genomics Statistics
                Biology and Life Sciences
                Genetics
                Genomics
                Genomics Statistics
                Biology and Life Sciences
                Organisms
                Animals
                Vertebrates
                Mammals
                Wolves
                Biology and Life Sciences
                Evolutionary Biology
                Evolutionary Processes
                Natural Selection
                Biology and Life Sciences
                Evolutionary Biology
                Population Genetics
                Natural Selection
                Biology and Life Sciences
                Genetics
                Population Genetics
                Natural Selection
                Biology and Life Sciences
                Population Biology
                Population Genetics
                Natural Selection
                People and Places
                Demography
                Custom metadata
                Sequence data are available at http://www.ncbi.nlm.nih.gov/bioproject/PRJNA274504.

                Genetics
                Genetics

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