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MBE Advance Access published online on January 11, 2006
Molecular Biology and Evolution, doi:10.1093/molbev/msj094
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© The Author 2006. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org
Accepted December 30, 2005

Research Article

Accurate Inference and Estimation in Population Genomics

Matthew W. Hahn 1 *

1 Center for Population Biology, University of California, Davis, CA 95616, USA; Department of Biology and School of Informatics, Indiana University, Bloomington, IN 47405, USA

* To whom correspondence should be addressed.
Matthew W. Hahn, E-mail: mwh{at}indiana.edu


  Abstract

Both intra- and inter-specific genomic comparisons have revealed local similarities in the level and frequency of mutational variation, as well as in patterns of gene expression. This autocorrelation between measurements leads to violations of assumptions of independence in many statistical methods, resulting in misleading and incorrect inferences. Here I show that autocorrelation can be due to many factors and is present across the genome. Using a 1-dimensional spatial stochastic model, I further show how previous results can be employed to correct for autocorrelation along chromosomes in population and comparative genomics research. When multiple hypothesis tests are autocorrelated, I demonstrate that a simple correction can lead to increased power in statistical inference. I present a preliminary analysis of population genomic data from Drosophila simulans to show the ubiquity of autocorrelation and applicability of the methods proposed here.

Keywords: autocorrelation; time series; association study; comparative genomics; natural selection.
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