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      Five genetic variants explain over 70% of hair coat pheomelanin intensity variation in purebred and mixed breed domestic dogs

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          Abstract

          In mammals, the pigment molecule pheomelanin confers red and yellow color to hair, and the intensity of this coloration is caused by variation in the amount of pheomelanin. Domestic dogs exhibit a wide range of pheomelanin intensity, ranging from the white coat of the Samoyed to the deep red coat of the Irish Setter. While several genetic variants have been associated with specific coat intensity phenotypes in certain dog breeds, they do not explain the majority of phenotypic variation across breeds. In order to gain further insight into the extent of multigenicity and epistatic interactions underlying coat pheomelanin intensity in dogs, we leveraged a large dataset obtained via a direct-to-consumer canine genetic testing service. This consisted of genome-wide single nucleotide polymorphism (SNP) genotype data and owner-provided photos for 3,057 pheomelanic mixed breed and purebred dogs from 63 breeds and varieties spanning the full range of canine coat pheomelanin intensity. We first performed a genome-wide association study (GWAS) on 2,149 of these dogs to search for additional genetic variants that underlie intensity variation. GWAS identified five loci significantly associated with intensity, of which two (CFA15 29.8 Mb and CFA20 55.8 Mb) replicate previous findings and three (CFA2 74.7 Mb, CFA18 12.9 Mb, CFA21 10.9 Mb) have not previously been reported. In order to assess the combined predictive power of these loci across dog breeds, we used our GWAS data set to fit a linear model, which explained over 70% of variation in coat pheomelanin intensity in an independent validation dataset of 908 dogs. These results introduce three novel pheomelanin intensity loci, and further demonstrate the multigenic nature of coat pheomelanin intensity determination in domestic dogs.

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          The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.

          Next-generation DNA sequencing (NGS) projects, such as the 1000 Genomes Project, are already revolutionizing our understanding of genetic variation among individuals. However, the massive data sets generated by NGS--the 1000 Genome pilot alone includes nearly five terabases--make writing feature-rich, efficient, and robust analysis tools difficult for even computationally sophisticated individuals. Indeed, many professionals are limited in the scope and the ease with which they can answer scientific questions by the complexity of accessing and manipulating the data produced by these machines. Here, we discuss our Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce. The GATK provides a small but rich set of data access patterns that encompass the majority of analysis tool needs. Separating specific analysis calculations from common data management infrastructure enables us to optimize the GATK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and shared memory parallelization. We highlight the capabilities of the GATK by describing the implementation and application of robust, scale-tolerant tools like coverage calculators and single nucleotide polymorphism (SNP) calling. We conclude that the GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.
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            Matplotlib: A 2D Graphics Environment

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              Second-generation PLINK: rising to the challenge of larger and richer datasets

              PLINK 1 is a widely used open-source C/C++ toolset for genome-wide association studies (GWAS) and research in population genetics. However, the steady accumulation of data from imputation and whole-genome sequencing studies has exposed a strong need for even faster and more scalable implementations of key functions. In addition, GWAS and population-genetic data now frequently contain probabilistic calls, phase information, and/or multiallelic variants, none of which can be represented by PLINK 1's primary data format. To address these issues, we are developing a second-generation codebase for PLINK. The first major release from this codebase, PLINK 1.9, introduces extensive use of bit-level parallelism, O(sqrt(n))-time/constant-space Hardy-Weinberg equilibrium and Fisher's exact tests, and many other algorithmic improvements. In combination, these changes accelerate most operations by 1-4 orders of magnitude, and allow the program to handle datasets too large to fit in RAM. This will be followed by PLINK 2.0, which will introduce (a) a new data format capable of efficiently representing probabilities, phase, and multiallelic variants, and (b) extensions of many functions to account for the new types of information. The second-generation versions of PLINK will offer dramatic improvements in performance and compatibility. For the first time, users without access to high-end computing resources can perform several essential analyses of the feature-rich and very large genetic datasets coming into use.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: InvestigationRole: MethodologyRole: Writing – review & editing
                Role: Data curationRole: MethodologyRole: Project administrationRole: Writing – review & editing
                Role: InvestigationRole: ValidationRole: Writing – review & editing
                Role: ConceptualizationRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: SupervisionRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                27 May 2021
                2021
                : 16
                : 5
                : e0250579
                Affiliations
                [1 ] Embark Veterinary, Inc., Boston, Massachusetts, United States of America
                [2 ] Department of Biomedical Sciences, Cornell University College of Veterinary Medicine, Ithaca, New York, United States of America
                HudsonAlpha Institute for Biotechnology, UNITED STATES
                Author notes

                Competing Interests: AJ Slavney, MKJ, TK, TRN, AJ Sams and AR Boyko are employees of Embark Veterinary, Inc., that offers dog DNA testing as a commercial service. Adam R Boyko is a co-founder and partial owner of Embark Veterinary, Inc, and ARB, AJ Sams, AJ Slavney, MKJ and TK are co-inventors on US Provisional Patent application 63/004,204. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

                Author information
                https://orcid.org/0000-0001-7791-9267
                https://orcid.org/0000-0002-9204-6852
                https://orcid.org/0000-0003-0092-8506
                Article
                PONE-D-21-10334
                10.1371/journal.pone.0250579
                8158882
                34043658
                58f36fe9-cec4-449d-92b4-b4cddcb8e35b
                © 2021 Slavney 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
                : 29 March 2021
                : 8 April 2021
                Page count
                Figures: 5, Tables: 5, Pages: 23
                Funding
                This study was funded by Embark Veterinary, Inc. and the participants that provided DNA and phenotypic information via Embark’s web-based platform. The funders provided support in the form of salaries for all authors (AJ Slavney, MKJ, TK, TRN, AJ Sams, and AR Boyko), but had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the author contributions section.
                Categories
                Research Article
                Biology and Life Sciences
                Computational Biology
                Genome Analysis
                Genome-Wide Association Studies
                Biology and Life Sciences
                Genetics
                Genomics
                Genome Analysis
                Genome-Wide Association Studies
                Biology and Life Sciences
                Genetics
                Human Genetics
                Genome-Wide Association Studies
                Biology and Life Sciences
                Organisms
                Eukaryota
                Animals
                Pets and Companion Animals
                Biology and Life Sciences
                Zoology
                Animals
                Pets and Companion Animals
                Biology and Life Sciences
                Organisms
                Eukaryota
                Animals
                Vertebrates
                Amniotes
                Mammals
                Dogs
                Biology and Life Sciences
                Zoology
                Animals
                Vertebrates
                Amniotes
                Mammals
                Dogs
                Biology and Life Sciences
                Genetics
                Genetic Loci
                Alleles
                Biology and Life Sciences
                Genetics
                Genetic Loci
                Biology and Life Sciences
                Genetics
                Phenotypes
                Biology and Life Sciences
                Genetics
                Heredity
                Genetic Mapping
                Variant Genotypes
                Research and Analysis Methods
                Mathematical and Statistical Techniques
                Statistical Methods
                Forecasting
                Physical Sciences
                Mathematics
                Statistics
                Statistical Methods
                Forecasting
                Custom metadata
                All relevant data have been deposited in Dryad and are available at https://doi.org/10.5061/dryad.ttdz08kxt.

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