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MBE Advance Access published online on November 2, 2007
Molecular Biology and Evolution, doi:10.1093/molbev/msm239
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© The Author 2007. 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

Research Article

Accounting for Bias from Sequencing Error in Population Genetic Estimates

Philip L. F. Johnson* and Montgomery Slatkin{dagger}

* Biophysics Graduate Group, University of California, Berkeley
{dagger} Department of Integrative Biology, University of California, Berkeley

Corresponding author: Philip Johnson, plfjohnson{at}berkeley.edu, (510) 643-6299, 3060 VLSB, University of California, Berkeley, CA 94720-3140

Received for publication June 13, 2007. Revision received August 23, 2007. Revision received September 25, 2007. Accepted for publication October 27, 2007.

Sequencing error presents a significant challenge to population genetic analyses using low-coverage sequence in general and single-pass reads in particular. Bias in parameter estimates becomes severe when the level of polymorphism ("signal") is low relative to the amount of error ("noise"). Choosing an arbitrary quality score cutoff yields biased estimates, particularly with newer, non-Sanger sequencing technologies that have different quality score distributions. We propose a rule of thumb to judge when a given threshold will lead to significant bias and suggest alternative approaches that reduce bias.

Key Words: sequencing error • population genetics • bias • quality score


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