Journal of Heredity Advance Access originally published online on July 4, 2007
Journal of Heredity 2007 98(4):331-336; doi:10.1093/jhered/esm031
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Ascertainment Bias in Spatially Structured Populations: A Case Study in the Eastern Fence Lizard
From the Museum of Vertebrate Zoology, University of California, Berkeley CA 94720 (Rosenblum); and the Department of Integrative Biology, University of California, Berkeley CA 94720 (Novembre). Erica Bree Rosenblum is now at the Department of Molecular and Cell Biology, University of California, Berkeley CA 94720; and John Novembre is now at the Department of Human Genetics, University of Chicago, Chicago, IL 60637
Address correspondence to J. Novembre at the address above, or e-mail: jnovembre{at}uchicago.edu.
Despite increased interest in applying single nucleotide polymorphism (SNP) data to questions in natural systems, one unresolved issue is to what extent the ascertainment bias induced during the SNP discovery phase will impact available analysis methods. Although most studies addressing ascertainment bias have focused on human populations, it is not clear whether existing methods will work when applied to other species with more complex demographic histories and more significant levels of population structure. Here we present findings from an empirical approach to exploring the effect of population structure on issues of ascertainment bias in the Eastern Fence Lizard, Sceloporus undulatus. We find that frequency spectra and summary statistics were highly sensitive to SNP discovery strategy, necessitating careful selection of the initial ascertainment panel. Randomly selected ascertainment panels performed equally well as ascertainment panels chosen to jointly sample geographic, phenotypic, and genetic diversity. Geographically restricted panels resulted in larger biases. Additionally, we found existing ascertainment bias correction methods, which were not developed for geographically structured data sets, were largely effective at reducing the impact of ascertainment bias. Because bias correction methods performed well even when underlying assumptions were violated, our results suggest tools are currently available to analyze SNP data in structured populations.
Corresponding Editor: Robert Wayne
Received May 30, 2006
Accepted April 17, 2007
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