Statistical Science

Ancestral Inference in Population Genetics

R. C. Griffiths and Simon Tavare

Full-text: Open access

Abstract

Mitochondrial DNA sequence variation is now being used to study the history of our species. In this paper we discuss some aspects of estimation and inference that arise in the study of such variability, focusing in particular on the estimation of substitution rates and their use in calibrating estimates of the time since the most recent common ancestor of a sample of sequences. Observed DNA sequence variation is generated by superimposing the effects of mutation on the ancestral tree of the sequences. For data of the type studied here, this ancestral tree has to be modeled as a random process. Superimposing the effects of mutation produces complicated sampling distributions that form the basis of any statistical model for the data. Using such distributions--for example, for maximum likelihood estimation of rates--poses some difficult computational problems. We describe a Monte Carlo method, a cousin of the popular "Markov chain Monte Carlo," that has proved very useful in addressing some of these issues.

Article information

Source
Statist. Sci. Volume 9, Number 3 (1994), 307-319.

Dates
First available in Project Euclid: 19 April 2007

Permanent link to this document
http://projecteuclid.org/euclid.ss/1177010378

Digital Object Identifier
doi:10.1214/ss/1177010378

Mathematical Reviews number (MathSciNet)
MR1325431

Zentralblatt MATH identifier
0955.62644

JSTOR
links.jstor.org

Keywords
Coalescent ancestral inference mitochondrial Eve infinitely-many-sites mitochondrial DNA Markov chain Monte Carlo Monte Carlo likelihoods

Citation

Griffiths, R. C.; Tavare, Simon. Ancestral Inference in Population Genetics. Statist. Sci. 9 (1994), no. 3, 307--319. doi:10.1214/ss/1177010378. http://projecteuclid.org/euclid.ss/1177010378.


Export citation