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Advances in Bioinformatics
Volume 2008, Article ID 257864, 9 pages
http://dx.doi.org/10.1155/2008/257864
Review Article

A Tutorial of the Poisson Random Field Model in Population Genetics

1Department of Genetics, School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
2Department of Computer and Information Sciences, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA

Received 6 February 2008; Accepted 15 May 2008

Academic Editor: Alexander Zelikovsky

Copyright © 2008 Praveen Sethupathy and Sridhar Hannenhalli. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Population genetics is the study of allele frequency changes driven by various evolutionary forces such as mutation, natural selection, and random genetic drift. Although natural selection is widely recognized as a bona-fide phenomenon, the extent to which it drives evolution continues to remain unclear and controversial. Various qualitative techniques, or so-called “tests of neutrality”, have been introduced to detect signatures of natural selection. A decade and a half ago, Stanley Sawyer and Daniel Hartl provided a mathematical framework, referred to as the Poisson random field (PRF), with which to determine quantitatively the intensity of selection on a particular gene or genomic region. The recent availability of large-scale genetic polymorphism data has sparked widespread interest in genome-wide investigations of natural selection. To that end, the original PRF model is of particular interest for geneticists and evolutionary genomicists. In this article, we will provide a tutorial of the mathematical derivation of the original Sawyer and Hartl PRF model.