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      An SNP-based second-generation genetic map of Daphnia magna and its application to QTL analysis of phenotypic traits

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          Abstract

          Background

          Although Daphnia is increasingly recognized as a model for ecological genomics and biomedical research, there is, as of yet, no high-resolution genetic map for the genus. Such a map would provide an important tool for mapping phenotypes and assembling the genome. Here we estimate the genome size of Daphnia magna and describe the construction of an SNP array based linkage map. We then test the suitability of the map for life history and behavioural trait mapping. The two parent genotypes used to produce the map derived from D. magna populations with and without fish predation, respectively and are therefore expected to show divergent behaviour and life-histories.

          Results

          Using flow cytometry we estimated the genome size of D. magna to be about 238 mb. We developed an SNP array tailored to type SNPs in a D. magna F2 panel and used it to construct a D. magna linkage map, which included 1,324 informative markers. The map produced ten linkage groups ranging from 108.9 to 203.6 cM, with an average distance between markers of 1.13 cM and a total map length of 1,483.6 cM (Kosambi corrected). The physical length per cM is estimated to be 160 kb. Mapping infertility genes, life history traits and behavioural traits on this map revealed several significant QTL peaks and showed a complex pattern of underlying genetics, with different traits showing strongly different genetic architectures.

          Conclusions

          The new linkage map of D. magna constructed here allowed us to characterize genetic differences among parent genotypes from populations with ecological differences. The QTL effect plots are partially consistent with our expectation of local adaptation under contrasting predation regimes. Furthermore, the new genetic map will be an important tool for the Daphnia research community and will contribute to the physical map of the D. magna genome project and the further mapping of phenotypic traits. The clones used to produce the linkage map are maintained in a stock collection and can be used for mapping QTLs of traits that show variance among the F2 clones.

          Electronic supplementary material

          The online version of this article (doi:10.1186/1471-2164-15-1033) contains supplementary material, which is available to authorized users.

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          Most cited references46

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          The genomic basis of adaptive evolution in threespine sticklebacks

          Summary Marine stickleback fish have colonized and adapted to innumerable streams and lakes formed since the last ice age, providing an exceptional opportunity to characterize genomic mechanisms underlying repeated ecological adaptation in nature. Here we develop a high quality reference genome assembly for threespine sticklebacks. By sequencing the genomes of 20 additional individuals from a global set of marine and freshwater populations, we identify a genome-wide set of loci that are consistently associated with marine-freshwater divergence. Our results suggest that reuse of globally-shared standing genetic variation, including chromosomal inversions, plays an important role in repeated evolution of distinct marine and freshwater sticklebacks, and in the maintenance of divergent ecotypes during early stages of reproductive isolation. Both coding and regulatory changes occur in the set of loci underlying marine-freshwater evolution, with regulatory changes likely predominating in this classic example of repeated adaptive evolution in nature.
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            Predation, Body Size, and Composition of Plankton.

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              Individuality in gut microbiota composition is a complex polygenic trait shaped by multiple environmental and host genetic factors.

              In vertebrates, including humans, individuals harbor gut microbial communities whose species composition and relative proportions of dominant microbial groups are tremendously varied. Although external and stochastic factors clearly contribute to the individuality of the microbiota, the fundamental principles dictating how environmental factors and host genetic factors combine to shape this complex ecosystem are largely unknown and require systematic study. Here we examined factors that affect microbiota composition in a large (n = 645) mouse advanced intercross line originating from a cross between C57BL/6J and an ICR-derived outbred line (HR). Quantitative pyrosequencing of the microbiota defined a core measurable microbiota (CMM) of 64 conserved taxonomic groups that varied quantitatively across most animals in the population. Although some of this variation can be explained by litter and cohort effects, individual host genotype had a measurable contribution. Testing of the CMM abundances for cosegregation with 530 fully informative SNP markers identified 18 host quantitative trait loci (QTL) that show significant or suggestive genome-wide linkage with relative abundances of specific microbial taxa. These QTL affect microbiota composition in three ways; some loci control individual microbial species, some control groups of related taxa, and some have putative pleiotropic effects on groups of distantly related organisms. These data provide clear evidence for the importance of host genetic control in shaping individual microbiome diversity in mammals, a key step toward understanding the factors that govern the assemblages of gut microbiota associated with complex diseases.
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                Author and article information

                Contributors
                jarkko.routtu@gmail.com
                matthew.hall@monash.edu
                balbere55@gmail.com
                christian.beisel@bsse.ethz.ch
                rdb@cs.unh.edu
                anurag.chaturvedi@bio.kuleuven.be
                jechoi@gru.edu
                j.k.colbourne@bham.ac.uk
                Luc.DeMeester@bio.kuleuven.be
                mpullins@nd.edu
                claus-peter.stelzer@uibk.ac.at
                eleanne.solorzano@unh.edu
                kelley.thomas@unh.edu
                Michael.Pfrender.1@nd.edu
                dieter.ebert@unibas.ch
                Journal
                BMC Genomics
                BMC Genomics
                BMC Genomics
                BioMed Central (London )
                1471-2164
                27 November 2014
                2014
                : 15
                : 1
                : 1033
                Affiliations
                [ ]Zoologisches Institut, Universität Basel, Vesalgasse 1, 4051 Basel, Switzerland
                [ ]Department of Computer Science, University of New Hampshire, Durham, NH 03824 USA
                [ ]Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland
                [ ]Laboratory of Aquatic Ecology, Evolution and Conservation, University of Leuven, Charles Deberiotstraat 32, B-3000 Leuven, Belgium
                [ ]Department of Biostatistics, Georgia Regents University, Augusta, GA 30912-4900 USA
                [ ]School of Biosciences, The University of Birmingham, Birmingham, B15 2TT UK
                [ ]Department of Biological Sciences, Galvin Life Science Center, Notre Dame, IN 46556 USA
                [ ]Universität Innsbruck, Forschungsinstitut für Limnologie, 5310 Mondsee, Austria
                [ ]Hubbard Center for Genome Studies, University of New Hampshire, Durham, NH 03824 USA
                [ ]Peter T. Paul College of Business and Economics, University of New Hampshire, Durham, NH 03824 USA
                [ ]Department of Zoology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel
                [ ]School of Biological Sciences, Monash University, Melbourne, Victoria 3800 Australia
                Article
                6845
                10.1186/1471-2164-15-1033
                4301878
                25431334
                adedf7d1-5f0a-4062-9fbe-8a40fc8e6b64
                © Routtu et al.; licensee BioMed Central Ltd. 2014

                This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. 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
                : 17 July 2014
                : 12 November 2014
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2014

                Genetics
                Genetics

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