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MBE Advance Access originally published online on March 24, 2009
Molecular Biology and Evolution 2009 26(7):1479-1490; doi:10.1093/molbev/msp059
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© The Author 2009. 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 Articles

Inferring Population Mutation Rate and Sequencing Error Rate Using the SNP Frequency Spectrum in a Sample of DNA Sequences

Xiaoming Liu, Taylor J. Maxwell, Eric Boerwinkle and Yun-Xin Fu

Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston

E-mail: yunxin.fu{at}uth.tmc.edu.

Accepted for publication March 18, 2009.

One challenge of analyzing samples of DNA sequences is to account for the nonnegligible polymorphisms produced by error when the sequencing error rate is high or the sample size is large. Specifically, those artificial sequence variations will bias the observed single nucleotide polymorphism (SNP) frequency spectrum, which in turn may further bias the estimators of the population mutation rate Formula for diploids. In this paper, we propose a new approach based on the generalized least squares (GLS) method to estimate {theta}, given a SNP frequency spectrum in a random sample of DNA sequences from a population. With this approach, error rate {varepsilon} can be either known or unknown. In the latter case, {varepsilon} can be estimated given an estimation of {theta}. Using coalescent simulation, we compared our estimators with other estimators of {theta}. The results showed that the GLS estimators are more efficient than other {theta} estimators with error, and the estimation of {varepsilon} is usable in practice when the {theta} per bp is small. We demonstrate the application of the estimators with 10-kb noncoding region sequence sampled from a human population and provide suggestions for choosing {theta} estimators with error.

Key Words: coalescent theory • sequencing error • mutation rate • SNP frequency spectrum • generalized least squares

Hideki Innan, Associate Editor


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 Genome ResHome page
X. Liu, Y.-X. Fu, T. J. Maxwell, and E. Boerwinkle
Estimating population genetic parameters and comparing model goodness-of-fit using DNA sequences with error
Genome Res., January 1, 2010; 20(1): 101 - 109.
[Abstract] [Full Text] [PDF]


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