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      Worldwide patterns of genomic variation and admixture in gray wolves

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          Abstract

          The gray wolf ( Canis lupus) is a widely distributed top predator and ancestor of the domestic dog. To address questions about wolf relationships to each other and dogs, we assembled and analyzed a data set of 34 canine genomes. The divergence between New and Old World wolves is the earliest branching event and is followed by the divergence of Old World wolves and dogs, confirming that the dog was domesticated in the Old World. However, no single wolf population is more closely related to dogs, supporting the hypothesis that dogs were derived from an extinct wolf population. All extant wolves have a surprisingly recent common ancestry and experienced a dramatic population decline beginning at least ∼30 thousand years ago (kya). We suggest this crisis was related to the colonization of Eurasia by modern human hunter–gatherers, who competed with wolves for limited prey but also domesticated them, leading to a compensatory population expansion of dogs. We found extensive admixture between dogs and wolves, with up to 25% of Eurasian wolf genomes showing signs of dog ancestry. Dogs have influenced the recent history of wolves through admixture and vice versa, potentially enhancing adaptation. Simple scenarios of dog domestication are confounded by admixture, and studies that do not take admixture into account with specific demographic models are problematic.

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          Fast gapped-read alignment with Bowtie 2.

          As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
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            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.
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              A framework for variation discovery and genotyping using next-generation DNA sequencing data

              Recent advances in sequencing technology make it possible to comprehensively catalogue genetic variation in population samples, creating a foundation for understanding human disease, ancestry and evolution. The amounts of raw data produced are prodigious and many computational steps are required to translate this output into high-quality variant calls. We present a unified analytic framework to discover and genotype variation among multiple samples simultaneously that achieves sensitive and specific results across five sequencing technologies and three distinct, canonical experimental designs. Our process includes (1) initial read mapping; (2) local realignment around indels; (3) base quality score recalibration; (4) SNP discovery and genotyping to find all potential variants; and (5) machine learning to separate true segregating variation from machine artifacts common to next-generation sequencing technologies. We discuss the application of these tools, instantiated in the Genome Analysis Toolkit (GATK), to deep whole-genome, whole-exome capture, and multi-sample low-pass (~4×) 1000 Genomes Project datasets.
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                Author and article information

                Journal
                Genome Res
                Genome Res
                genome
                genome
                GENOME
                Genome Research
                Cold Spring Harbor Laboratory Press
                1088-9051
                1549-5469
                February 2016
                : 26
                : 2
                : 163-173
                Affiliations
                [1 ]Key Laboratory of Bioresources and Ecoenvironment (Ministry of Education), College of Life Sciences, Sichuan University, Chengdu 610064, People's Republic of China;
                [2 ]Department of Ecology and Evolutionary Biology, University of California, Los Angeles, California 90095-1606, USA;
                [3 ]CIBIO-UP, University of Porto, Vairão, 4485-661, Portugal;
                [4 ]Efi Arazi School of Computer Science, the Herzliya Interdisciplinary Center (IDC), Herzliya 46150, Israel;
                [5 ]Department of Genetics, Rutgers, the State University of New Jersey, Piscataway, New Jersey 08854, USA;
                [6 ]Institute of Evolutionary Biology (UPF-CSIC), PRBB, 08003 Barcelona, Spain;
                [7 ]ICREA at Institute of Evolutionary Biology (UPF-CSIC), PRBB, 08003 Barcelona, Spain;
                [8 ]ISPRA, Ozzano dell'Emilia, 40064, Italy;
                [9 ]Interdepartmental Program in Bioinformatics, University of California, Los Angeles, California 90095-1606, USA;
                [10 ]Sichuan Key Laboratory of Conservation Biology on Endangered Wildlife, Chengdu Research Base of Giant Panda Breeding, Chengdu, Sichuan Province, People's Republic of China, 610081;
                [11 ]Human Genetics Institute of New Jersey, Rutgers, the State University of New Jersey, Piscataway, New Jersey 08854, USA;
                [12 ]Centro Nacional de Análisis Genómico (CNAG), Parc Científic de Barcelona, 08028 Barcelona, Spain
                Author notes
                [13]

                These authors contributed equally to this work.

                [14]

                Present address: Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA

                Article
                9509184
                10.1101/gr.197517.115
                4728369
                26680994
                06aa5f95-9ea6-40bd-85cb-de5fdac330c8
                © 2016 Fan et al.; Published by Cold Spring Harbor Laboratory Press

                This article is distributed exclusively by Cold Spring Harbor Laboratory Press for the first six months after the full-issue publication date (see http://genome.cshlp.org/site/misc/terms.xhtml). After six months, it is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/.

                History
                : 29 July 2015
                : 15 December 2015
                Page count
                Pages: 11
                Funding
                Funded by: National Science Foundation (NSF) http://dx.doi.org/10.13039/100000001
                Award ID: EF-1021397
                Funded by: National Key Technology R&D Program of China
                Award ID: 2012BAC01B06
                Funded by: ICREA
                Funded by: EMBO http://dx.doi.org/10.13039/100004410
                Award ID: YIP 2013
                Funded by: MICINN
                Award ID: BFU2014-55090-P
                Funded by: National Human Genome Research Institute (NHGRI) http://dx.doi.org/10.13039/100000051
                Award ID: R00HG005846
                Funded by: UC MEXUS-CONACYT
                Award ID: 213627
                Funded by: Chengdu Giant Panda Breeding Research Foundation
                Award ID: CPF-yan-2012-10
                Funded by: PRIC from Fundació Barcelona Zoo
                Funded by: Ajuntament de Barcelona
                Categories
                Research

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