Review

Population genetic inference from genomic sequence variation

    • 1 Department of Integrative Biology, University of California, Berkeley, Berkeley, California 94720, USA;
    • 2 Center for Population Biology, University of California, Davis, Davis, California 95616, USA;
    • 3 Mathematics and Biosciences Group, Max F. Perutz Laboratories, Vienna 1030, Austria;
    • 4 Department of Statistics, University of California, Berkeley, Berkeley, California 94720, USA
    • 5 Present address: Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA.
    • 6 Corresponding author. E-mail [email protected]; fax (510) 643-6264.
Published January 12, 2010. Vol 20 Issue 3, pp. 291-300. https://doi.org/10.1101/gr.079509.108
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cover of Genome Research Vol 36 Issue 4
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Abstract

Population genetics has evolved from a theory-driven field with little empirical data into a data-driven discipline in which genome-scale data sets test the limits of available models and computational analysis methods. In humans and a few model organisms, analyses of whole-genome sequence polymorphism data are currently under way. And in light of the falling costs of next-generation sequencing technologies, such studies will soon become common in many other organisms as well. Here, we assess the challenges to analyzing whole-genome sequence polymorphism data, and we discuss the potential of these data to yield new insights concerning population history and the genomic prevalence of natural selection.

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