Originally published as Genetics Published Articles Ahead of Print on February 9, 2009.

Genetics, Vol. 181, 1567-1578, April 2009, Copyright © 2009
doi:10.1534/genetics.108.100032

Detecting Selective Sweeps: A New Approach Based on Hidden Markov Models

Simon Boitard^*,1, Christian Schlötterer and Andreas Futschik^*

^* Institute of Statistics and Decision Support Systems, University of Vienna, 1010 Vienna, Austria and Institut für Populationsgenetik, Veterinärmedizinische Universität, 1210 Vienna, Austria

¹ Corresponding author: UR444 Laboratoire de Génétique Cellulaire, INRA, Chemin de Borde Rouge, BP 52627, 31326 Castanet Tolosan Cédex, France.
E-mail: simon.boitard{at}toulouse.inra.fr

Detecting and localizing selective sweeps on the basis of SNP data has recently received considerable attention. Here we introduce the use of hidden Markov models (HMMs) for the detection of selective sweeps in DNA sequences. Like previously published methods, our HMMs use the site frequency spectrum, and the spatial pattern of diversity along the sequence, to identify selection. In contrast to earlier approaches, our HMMs explicitly model the correlation structure between linked sites. The detection power of our methods, and their accuracy for estimating the selected site location, is similar to that of competing methods for constant size populations. In the case of population bottlenecks, however, our methods frequently showed fewer false positives.

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Online ISSN: 1943-2361  Print ISSN: 0016-6731
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