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      In absence of local adaptation, plasticity and spatially varying selection rule: a view from genomic reaction norms in a panmictic species ( Anguilla rostrata)

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          Abstract

          Background

          American eel ( Anguilla rostrata) is one of the few species for which panmixia has been demonstrated at the scale of the entire species. As such, the development of long term local adaptation is impossible. However, both plasticity and spatially varying selection have been invoked in explaining how American eel may cope with an unusual broad scope of environmental conditions. Here, we address this question through transcriptomic analyses and genomic reaction norms of eels from two geographic origins reared in controlled environments.

          Results

          The null hypothesis of no difference in gene expression between eels from the two origins was rejected. Many unique transcripts and two out of seven gene clusters showed significant difference in expression, both at time of capture and after three months of common rearing. Differences in expression were observed at numerous genes representing many functional groups when comparing eels from a same origin reared under different salinity conditions. Plastic response to different rearing conditions varied among gene clusters with three clusters showing significant origin-environment interactions translating into differential genomic norms of reaction. Most genes and functional categories showing differences between origins were previously shown to be differentially expressed in a study comparing transcription profiles between adult European eels acclimated to different salinities.

          Conclusions

          These results emphasize that while plasticity in expression may be important, there is also a role for local genetic (and/or epigenetic) differences in explaining differences in gene expression between eels from different geographic origins. Such differences match those reported in genetically distinct populations in other fishes, both in terms of the proportion of genes that are differentially expressed and the diversity of biological functions involved. We thus propose that genetic differences between glass eels of different origins caused by spatially varying selection due to local environmental conditions translates into transcriptomic differences (including different genomic norms of reaction) which may in turn explain part of the phenotypic variance observed between different habitats colonized by eels.

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          Most cited references25

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          Epigenetics for ecologists.

          There is now mounting evidence that heritable variation in ecologically relevant traits can be generated through a suite of epigenetic mechanisms, even in the absence of genetic variation. Moreover, recent studies indicate that epigenetic variation in natural populations can be independent from genetic variation, and that in some cases environmentally induced epigenetic changes may be inherited by future generations. These novel findings are potentially highly relevant to ecologists because they could significantly improve our understanding of the mechanisms underlying natural phenotypic variation and the responses of organisms to environmental change. To understand the full significance of epigenetic processes, however, it is imperative to study them in an ecological context. Ecologists should therefore start using a combination of experimental approaches borrowed from ecological genetics, novel techniques to analyse and manipulate epigenetic variation, and genomic tools, to investigate the extent and structure of epigenetic variation within and among natural populations, as well as the interrelations between epigenetic variation, phenotypic variation and ecological interactions.
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            Assessing gene significance from cDNA microarray expression data via mixed models.

            The determination of a list of differentially expressed genes is a basic objective in many cDNA microarray experiments. We present a statistical approach that allows direct control over the percentage of false positives in such a list and, under certain reasonable assumptions, improves on existing methods with respect to the percentage of false negatives. The method accommodates a wide variety of experimental designs and can simultaneously assess significant differences between multiple types of biological samples. Two interconnected mixed linear models are central to the method and provide a flexible means to properly account for variability both across and within genes. The mixed model also provides a convenient framework for evaluating the statistical power of any particular experimental design and thus enables a researcher to a priori select an appropriate number of replicates. We also suggest some basic graphics for visualizing lists of significant genes. Analyses of published experiments studying human cancer and yeast cells illustrate the results.
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              Improved statistical tests for differential gene expression by shrinking variance components estimates.

              Combining information across genes in the statistical analysis of microarray data is desirable because of the relatively small number of data points obtained for each individual gene. Here we develop an estimator of the error variance that can borrow information across genes using the James-Stein shrinkage concept. A new test statistic (FS) is constructed using this estimator. The new statistic is compared with other statistics used to test for differential expression: the gene-specific F test (F1), the pooled-variance F statistic (F3), a hybrid statistic (F2) that uses the average of the individual and pooled variances, the regularized t-statistic, the posterior odds statistic B, and the SAM t-test. The FS-test shows best or nearly best power for detecting differentially expressed genes over a wide range of simulated data in which the variance components associated with individual genes are either homogeneous or heterogeneous. Thus FS provides a powerful and robust approach to test differential expression of genes that utilizes information not available in individual gene testing approaches and does not suffer from biases of the pooled variance approach.
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                Author and article information

                Contributors
                Journal
                BMC Genomics
                BMC Genomics
                BMC Genomics
                BioMed Central
                1471-2164
                2014
                27 May 2014
                : 15
                : 403
                Affiliations
                [1 ]Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec G1V 0A6, Canada
                [2 ]Institut Maurice-Lamontagne, Ministère des Pêches et des Océans, 850 Route de la Mer, Mont-Joli, Québec G5H 3ZH, Canada
                [3 ]University of St Andrews, School of Biology, St Andrews, Fife KY16 9AJ, Scotland
                Article
                1471-2164-15-403
                10.1186/1471-2164-15-403
                4229938
                24884429
                7cd1bd2c-6a2a-4fe6-9e10-e16ab57f4728
                Copyright © 2014 Côté et al.; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), 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
                : 7 November 2013
                : 7 May 2014
                Categories
                Research Article

                Genetics
                anguilla,panmixia,trancriptome,microarrays,genomic reaction norm,plasticity,gene-environment interactions,conservation

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