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

AUTOR(ES)

Liu, Xiaoming

FONTE

Oxford University Press

RESUMO

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 θ=4Nμ for diploids. In this paper, we propose a new approach based on the generalized least squares (GLS) method to estimate θ, given a SNP frequency spectrum in a random sample of DNA sequences from a population. With this approach, error rate ε can be either known or unknown. In the latter case, ε can be estimated given an estimation of θ. Using coalescent simulation, we compared our estimators with other estimators of θ. The results showed that the GLS estimators are more efficient than other θ estimators with error, and the estimation of ε is usable in practice when the θ 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 θ estimators with error.

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