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      Museum samples reveal rapid evolution by wild honey bees exposed to a novel parasite

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          Abstract

          Understanding genetic changes caused by novel pathogens and parasites can reveal mechanisms of adaptation and genetic robustness. Using whole-genome sequencing of museum and modern specimens, we describe the genomic changes in a wild population of honey bees in North America following the introduction of the ectoparasitic mite, Varroa destructor. Even though colony density in the study population is the same today as in the past, a major loss of haplotypic diversity occurred, indicative of a drastic mitochondrial bottleneck, caused by massive colony mortality. In contrast, nuclear genetic diversity did not change, though hundreds of genes show signs of selection. The genetic diversity within each bee colony, particularly as a consequence of polyandry by queens, may enable preservation of genetic diversity even during population bottlenecks. These findings suggest that genetically diverse honey bee populations can recover from introduced diseases by evolving rapid tolerance, while maintaining much of the standing genetic variation.

          Abstract

          Introduction of pathogens can cause colony collapse in honey bees. Here, the authors use museum specimens to show widespread colony mortality but unaffected nuclear genetic diversity in a wild population of honey bees in North America following the introduction of ectoparasitic Varroa mites.

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          Most cited references23

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          Stampy: a statistical algorithm for sensitive and fast mapping of Illumina sequence reads.

          High-volume sequencing of DNA and RNA is now within reach of any research laboratory and is quickly becoming established as a key research tool. In many workflows, each of the short sequences ("reads") resulting from a sequencing run are first "mapped" (aligned) to a reference sequence to infer the read from which the genomic location derived, a challenging task because of the high data volumes and often large genomes. Existing read mapping software excel in either speed (e.g., BWA, Bowtie, ELAND) or sensitivity (e.g., Novoalign), but not in both. In addition, performance often deteriorates in the presence of sequence variation, particularly so for short insertions and deletions (indels). Here, we present a read mapper, Stampy, which uses a hybrid mapping algorithm and a detailed statistical model to achieve both speed and sensitivity, particularly when reads include sequence variation. This results in a higher useable sequence yield and improved accuracy compared to that of existing software.
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            A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data

            (2013)
            Motivation: Most existing methods for DNA sequence analysis rely on accurate sequences or genotypes. However, in applications of the next-generation sequencing (NGS), accurate genotypes may not be easily obtained (e.g. multi-sample low-coverage sequencing or somatic mutation discovery). These applications press for the development of new methods for analyzing sequence data with uncertainty. Results: We present a statistical framework for calling SNPs, discovering somatic mutations, inferring population genetical parameters and performing association tests directly based on sequencing data without explicit genotyping or linkage-based imputation. On real data, we demonstrate that our method achieves comparable accuracy to alternative methods for estimating site allele count, for inferring allele frequency spectrum and for association mapping. We also highlight the necessity of using symmetric datasets for finding somatic mutations and confirm that for discovering rare events, mismapping is frequently the leading source of errors. Availability: http://samtools.sourceforge.net. Contact: hengli@broadinstitute.org.
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              MSMS: a coalescent simulation program including recombination, demographic structure and selection at a single locus

              Motivation: We have implemented a coalescent simulation program for a structured population with selection at a single diploid locus. The program includes the functionality of the simulator ms to model population structure and demography, but adds a model for deme- and time-dependent selection using forward simulations. The program can be used, e.g. to study hard and soft selective sweeps in structured populations or the genetic footprint of local adaptation. The implementation is designed to be easily extendable and widely deployable. The interface and output format are compatible with ms. Performance is comparable even with selection included. Availability: The program is freely available from http://www.mabs.at/ewing/msms/ along with manuals and examples. The source is freely available under a GPL type license. Contact: gregory.ewing@univie.ac.at Supplementary information: Supplementary data are available at Bioinformatics online.
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                Author and article information

                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group
                2041-1723
                06 August 2015
                2015
                : 6
                : 7991
                Affiliations
                [1 ]Okinawa Institute of Science and Technology , 1919-1 Tancha, Onna-son, Kunigami-gun 904-0412, Japan
                [2 ]Research School of Biology, Australian National University , Canberra, Australian Capital Territory 0200, Australia
                [3 ]Department of Paediatrics and Adolescent Medicine, Medical University of Vienna , Lazarettgasse 14, AKH BT 25.3, 1090 Vienna, Austria
                [4 ]Department of Neurobiology and Behavior, Cornell University , Ithaca, New York 14853, USA
                Author notes
                Author information
                http://orcid.org/0000-0003-4369-1019
                http://orcid.org/0000-0002-7754-9566
                Article
                ncomms8991
                10.1038/ncomms8991
                4918369
                26246313
                8c374768-f035-4a00-adf0-6981bf0139e1
                Copyright © 2015, Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.

                This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                History
                : 05 February 2015
                : 06 July 2015
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