Maximum-likelihood estimation of site-specific mutation rates in human mitochondrial DNA from partial phylogenetic classification

Saharon Rosset*, R. Spencer Wells, David F. Soria-Hernanz, Chris Tyler-Smith, Ajay K. Royyuru, Doron M. Behar, Theodore G. Schurr, Fabricio R. Santos, Lluis Quintana-Murci, Jaume Bertranpetit, David Comas, Elena Balanovska, Oleg Balanovsky, R. John Mitchell, Li Jin, Himla Soodyall, Ramasamy Pitchappan, Alan Cooper, Jason Blue-Smith, David Soria Hernanz

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

The mitochondrial DNA hypervariable segment I (HVS-I) is widely used in studies of human evolutionary genetics, and therefore accurate estimates of mutation rates among nucleotide sites inthis regionare essential. We have developed a novel maximum-likelihood methodology for estimating site-specific mutation rates from partial phylogenetic information, such as haplogroup association. The resulting estimation problem is a generalized linear model, with a nonstandard link function. We develop inference and bias correction tools for our estimates and a hypothesis-testing approach for site independence. We demonstrate our methodology using 16,609 HVS-I samples from the Genographic Project. Our results suggest that mutation rates among nucleotide sites in HVS-I are highly variable. The 16,400-16,500 region exhibits significantly lower rates compared to other regions, suggesting potential functional constraints. Several loci identified in the literature as possible termination-associated sequences (TAS) do not yield statistically slower rates than the rest of HVS-I, casting doubt on their functional importance. Our tests do not reject the null hypothesis of independent mutation rates among nucleotide sites, supporting the use of site-independence assumption for analyzing HVS-I. Potential extensions of our methodology include its application to estimation of mutation rates in other genetic regions, like Y chromosome short tandem repeats.

Original languageEnglish
Pages (from-to)1511-1524
Number of pages14
JournalGenetics
Volume180
Issue number3
DOIs
StatePublished - Nov 2008

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