Population genetic inference from genomic sequence variation
John E. Pool1,2, Ines Hellmann3, Jeffrey D. Jensen1,5 and Rasmus Nielsen1,4,6
+Author Affiliations
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.
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.
Footnotes
↵6 Corresponding author.
E-mail rasmus_nielsen@berkeley.edu; fax (510) 643-6264.
Article published online before print. Article and publication date are at http://www.genome.org/cgi/doi/10.1101/gr.079509.108.
Copyright © 2010 by Cold Spring Harbor Laboratory Press
+++++
