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      Population genomics of parallel hybrid zones in the mimetic butterflies, H. melpomene and H. erato

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          Abstract

          Hybrid zones can be valuable tools for studying evolution and identifying genomic regions responsible for adaptive divergence and underlying phenotypic variation. Hybrid zones between subspecies of Heliconius butterflies can be very narrow and are maintained by strong selection acting on color pattern. The comimetic species, H. erato and H. melpomene, have parallel hybrid zones in which both species undergo a change from one color pattern form to another. We use restriction-associated DNA sequencing to obtain several thousand genome-wide sequence markers and use these to analyze patterns of population divergence across two pairs of parallel hybrid zones in Peru and Ecuador. We compare two approaches for analysis of this type of data—alignment to a reference genome and de novo assembly—and find that alignment gives the best results for species both closely ( H. melpomene) and distantly ( H. erato, ∼15% divergent) related to the reference sequence. Our results confirm that the color pattern controlling loci account for the majority of divergent regions across the genome, but we also detect other divergent regions apparently unlinked to color pattern differences. We also use association mapping to identify previously unmapped color pattern loci, in particular the Ro locus. Finally, we identify a new cryptic population of H. timareta in Ecuador, which occurs at relatively low altitude and is mimetic with H. melpomene malleti.

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          Basic local alignment search tool.

          A new approach to rapid sequence comparison, basic local alignment search tool (BLAST), directly approximates alignments that optimize a measure of local similarity, the maximal segment pair (MSP) score. Recent mathematical results on the stochastic properties of MSP scores allow an analysis of the performance of this method as well as the statistical significance of alignments it generates. The basic algorithm is simple and robust; it can be implemented in a number of ways and applied in a variety of contexts including straightforward DNA and protein sequence database searches, motif searches, gene identification searches, and in the analysis of multiple regions of similarity in long DNA sequences. In addition to its flexibility and tractability to mathematical analysis, BLAST is an order of magnitude faster than existing sequence comparison tools of comparable sensitivity.
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            Inference of Population Structure Using Multilocus Genotype Data

            We describe a model-based clustering method for using multilocus genotype data to infer population structure and assign individuals to populations. We assume a model in which there are K populations (where K may be unknown), each of which is characterized by a set of allele frequencies at each locus. Individuals in the sample are assigned (probabilistically) to populations, or jointly to two or more populations if their genotypes indicate that they are admixed. Our model does not assume a particular mutation process, and it can be applied to most of the commonly used genetic markers, provided that they are not closely linked. Applications of our method include demonstrating the presence of population structure, assigning individuals to populations, studying hybrid zones, and identifying migrants and admixed individuals. We show that the method can produce highly accurate assignments using modest numbers of loci—e.g., seven microsatellite loci in an example using genotype data from an endangered bird species. The software used for this article is available from http://www.stats.ox.ac.uk/~pritch/home.html.
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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
                August 2014
                : 24
                : 8
                : 1316-1333
                Affiliations
                [1 ]Department of Zoology, University of Cambridge, Cambridge CB2 3EJ, United Kingdom;
                [2 ]Department of Animal and Plant Sciences, University of Sheffield, Sheffield, S10 2TN, United Kingdom;
                [3 ]Department of Biology and Center for Applied Tropical Ecology and Conservation, University of Puerto Rico, Rio Piedras, San Juan, Puerto Rico 00921;
                [4 ]Centro de Investigación en Biodiversidad y Cambio Climático (BioCamb), Universidad Tecnológica Indoamérica, Quito, Ecuador;
                [5 ]Department of Biology, Mississippi State University, Mississippi 39762, USA;
                [6 ]High Performance Computing Facility, University of Puerto Rico, San Juan, Puerto Rico, 00921;
                [7 ]Department of Computer Science, University of Puerto Rico, Rio Piedras, San Juan, Puerto Rico 00921;
                [8 ]Smithsonian Tropical Research Institute, Apartado 0843-03092, Balboa, Ancón, Panama
                Author notes
                Corresponding author: c.jiggins@ 123456zoo.cam.ac.uk
                Author information
                http://orcid.org/0000-0002-9319-921X
                http://orcid.org/0000-0003-2805-2745
                http://orcid.org/0000-0002-7809-062X
                Article
                9518021
                10.1101/gr.169292.113
                4120085
                24823669
                a596bd47-08a6-4f08-9fdd-3de19c02523b
                © 2014 Nadeau 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
                : 6 November 2013
                : 5 May 2014
                Page count
                Pages: 18
                Categories
                Research

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