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      Identifying genome-wide immune gene variation underlying infectious disease in wildlife populations – a next generation sequencing approach in the gopher tortoise

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          Abstract

          Background

          Infectious disease is the single greatest threat to taxa such as amphibians (chytrid fungus), bats (white nose syndrome), Tasmanian devils (devil facial tumor disease), and black-footed ferrets (canine distemper virus, plague). Although understanding the genetic basis to disease susceptibility is important for the long-term persistence of these groups, most research has been limited to major-histocompatibility and Toll-like receptor genes. To better understand the genetic basis of infectious disease susceptibility in a species of conservation concern, we sequenced all known/predicted immune response genes (i.e., the immunomes) in 16 Florida gopher tortoises, Gopherus polyphemus. All tortoises produced antibodies against Mycoplasma agassizii (an etiologic agent of infectious upper respiratory tract disease; URTD) and, at the time of sampling, either had ( n = 10) or lacked ( n = 6) clinical signs.

          Results

          We found several variants associated with URTD clinical status in complement and lectin genes, which may play a role in Mycoplasma immunity. Thirty-five genes deviated from neutrality according to Tajima’s D. These genes were enriched in functions relating to macromolecule and protein modifications, which are vital to immune system functioning.

          Conclusions

          These results are suggestive of genetic differences that might contribute to disease severity, a finding that is consistent with other mycoplasmal diseases. This has implications for management because tortoises across their range may possess genetic variation associated with a more severe response to URTD. More generally: 1) this approach demonstrates that a broader consideration of immune genes is better able to identify important variants, and; 2) this data pipeline can be adopted to identify alleles associated with disease susceptibility or resistance in other taxa, and therefore provide information on a population’s risk of succumbing to disease, inform translocations to increase genetic variation for disease resistance, and help to identify potential treatments.

          Electronic supplementary material

          The online version of this article (10.1186/s12864-018-4452-0) contains supplementary material, which is available to authorized users.

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          Most cited references48

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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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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              FatiGO: a web tool for finding significant associations of Gene Ontology terms with groups of genes.

              We present a simple but powerful procedure to extract Gene Ontology (GO) terms that are significantly over- or under-represented in sets of genes within the context of a genome-scale experiment (DNA microarray, proteomics, etc.). Said procedure has been implemented as a web application, FatiGO, allowing for easy and interactive querying. FatiGO, which takes the multiple-testing nature of statistical contrast into account, currently includes GO associations for diverse organisms (human, mouse, fly, worm and yeast) and the TrEMBL/Swissprot GOAnnotations@EBI correspondences from the European Bioinformatics Institute.
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                Author and article information

                Contributors
                jean.elbers@gmail.com
                mbbrown@ufl.edu
                staylor@lsu.edu
                Journal
                BMC Genomics
                BMC Genomics
                BMC Genomics
                BioMed Central (London )
                1471-2164
                19 January 2018
                19 January 2018
                2018
                : 19
                : 64
                Affiliations
                [1 ]ISNI 0000 0000 9070 1054, GRID grid.250060.1, School of Renewable Natural Resources, 227 RNR Bldg., , Louisiana State University and AgCenter, ; Baton Rouge, LA 70803 USA
                [2 ]ISNI 0000 0004 1936 8091, GRID grid.15276.37, Department of Infectious Diseases and Pathology, College of Veterinary Medicine, , University of Florida, ; Gainesville, FL 32611 USA
                Author information
                http://orcid.org/0000-0002-5593-4447
                Article
                4452
                10.1186/s12864-018-4452-0
                5775545
                29351737
                f02adcdc-05c2-4b9c-8ff4-3920f808acde
                © The Author(s). 2018

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 16 March 2017
                : 15 January 2018
                Funding
                Funded by: U.S. Department of Agriculture, McIntire Stennis
                Award ID: LAB04066
                Award ID: LAB94169
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: 8P20GM103528
                Award ID: 2P30DK072476
                Award ID: 5K08AI57722
                Funded by: FundRef http://dx.doi.org/10.13039/100000155, Division of Environmental Biology;
                Award ID: DEB-0224953
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2018

                Genetics
                genome-wide association study,target enrichment,sequence capture,next-generation sequencing,immunogenetics,gopherus polyphemus,immunomes

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