MBE Advance Access originally published online on January 11, 2006
Molecular Biology and Evolution 2006 23(5):911-918; doi:10.1093/molbev/msj094
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Proceedings of the SMBE Tri-National Young Investigators' Workshop 2005 |
Accurate Inference and Estimation in Population Genomics
* Center for Population Biology, University of California, Davis; and Department of Biology and School of Informatics, Indiana University, Bloomington
E-mail: mwh{at}indiana.edu.
Both intra- and interspecific 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 one-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.
Key Words: autocorrelation • time series • association study • comparative genomics • natural selection