Manel20103760

Référence

Manel, S., Joost, S., Epperson, B.K., Holderegger, R., Storfer, A., Rosenberg, M.S., Scribner, K.T., Bonin, A., Fortin, M.-J. (2010) Perspectives on the use of landscape genetics to detect genetic adaptive variation in the field. Molecular Ecology, 19(17):3760-3772. (Scopus )

Résumé

Understanding the genetic basis of species adaptation in the context of global change poses one of the greatest challenges of this century. Although we have begun to understand the molecular basis of adaptation in those species for which whole genome sequences are available, the molecular basis of adaptation is still poorly understood for most non-model species. In this paper, we outline major challenges and future research directions for correlating environmental factors with molecular markers to identify adaptive genetic variation, and point to research gaps in the application of landscape genetics to real-world problems arising from global change, such as the ability of organisms to adapt over rapid time scales. High throughput sequencing generates vast quantities of molecular data to address the challenge of studying adaptive genetic variation in non-model species. Here, we suggest that improvements in the sampling design should consider spatial dependence among sampled individuals. Then, we describe available statistical approaches for integrating spatial dependence into landscape analyses of adaptive genetic variation. © 2010 Blackwell Publishing Ltd.

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@ARTICLE { Manel20103760,
    AUTHOR = { Manel, S. and Joost, S. and Epperson, B.K. and Holderegger, R. and Storfer, A. and Rosenberg, M.S. and Scribner, K.T. and Bonin, A. and Fortin, M.-J. },
    TITLE = { Perspectives on the use of landscape genetics to detect genetic adaptive variation in the field },
    JOURNAL = { Molecular Ecology },
    YEAR = { 2010 },
    VOLUME = { 19 },
    NUMBER = { 17 },
    PAGES = { 3760-3772 },
    NOTE = { cited By 127 },
    ABSTRACT = { Understanding the genetic basis of species adaptation in the context of global change poses one of the greatest challenges of this century. Although we have begun to understand the molecular basis of adaptation in those species for which whole genome sequences are available, the molecular basis of adaptation is still poorly understood for most non-model species. In this paper, we outline major challenges and future research directions for correlating environmental factors with molecular markers to identify adaptive genetic variation, and point to research gaps in the application of landscape genetics to real-world problems arising from global change, such as the ability of organisms to adapt over rapid time scales. High throughput sequencing generates vast quantities of molecular data to address the challenge of studying adaptive genetic variation in non-model species. Here, we suggest that improvements in the sampling design should consider spatial dependence among sampled individuals. Then, we describe available statistical approaches for integrating spatial dependence into landscape analyses of adaptive genetic variation. © 2010 Blackwell Publishing Ltd. },
    AFFILIATION = { Laboratoire Population Environnement Développement, UMR 151 UP/IRD, Université de Provence, 3 place Victor Hugo, 13331 Marseille Cedex 03, France; Laboratoire d'Ecologie Alpine, UMR-CNRS 5553, Université Joseph Fourier, BP53, 38041 Grenoble Cedex 9, France; Laboratoire de Systèmes d'Information Géographique (LASIG), Ecole Polytechnique Fédérale de Lausanne (EPFL), Batiment GC, 1015 Lausanne, Switzerland; Department of Forestry, Michigan State University, East Lansing, MI 48824, United States; WSL Swiss Federal Research Institute, Zurcherstrasse 111, CH-8903 Birmensdorf, Switzerland; School of Biological Sciences, Washington State University, Pullman, WA 99164-4236, United States; Center for Evolutionary Medicine and Informatics, School of Life Sciences, Arizona State University, Tempe, AZ 85287-4501, United States; Department of Fisheries and Wildlife, Michigan State University, East Lansing, MI 48824, United States; Department of Botany, University of British Columbia, 3529-6270 University Boulevard, Vancouver, BC V6T 1Z4, Canada; Department of and Evolutionary Biology, University of Toronto, Toronto, ON M5S 3G5, Canada },
    AUTHOR_KEYWORDS = { computational approach; genome scan; landscape genomics; local adaptation; molecular techniques; regression analysis },
    DOCUMENT_TYPE = { Article },
    DOI = { 10.1111/j.1365-294X.2010.04717.x },
    SOURCE = { Scopus },
    URL = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-77956087880&doi=10.1111%2fj.1365-294X.2010.04717.x&partnerID=40&md5=cecc2d6278e0f6e2177c3b21c6203a66 },
}

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