Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - A coalescent-based method for detecting and estimating recombination from gene sequences.
AU - McVean, Gil
AU - Awadalla, Philip
AU - Fearnhead, Paul
PY - 2002/3
Y1 - 2002/3
N2 - Determining the amount of recombination in the genealogical history of a sample of genes is important to both evolutionary biology and medical population genetics. However, recurrent mutation can produce patterns of genetic diversity similar to those generated by recombination and can bias estimates of the population recombination rate. HUDSON 2001 has suggested an approximate-likelihood method based on coalescent theory to estimate the population recombination rate, 4Ner, under an infinite-sites model of sequence evolution. Here we extend the method to the estimation of the recombination rate in genomes, such as those of many viruses and bacteria, where the rate of recurrent mutation is high. In addition, we develop a powerful permutation-based method for detecting recombination that is both more powerful than other permutation-based methods and robust to misspecification of the model of sequence evolution. We apply the method to sequence data from viruses, bacteria, and human mitochondrial DNA. The extremely high level of recombination detected in both HIV1 and HIV2 sequences demonstrates that recombination cannot be ignored in the analysis of viral population genetic data.
AB - Determining the amount of recombination in the genealogical history of a sample of genes is important to both evolutionary biology and medical population genetics. However, recurrent mutation can produce patterns of genetic diversity similar to those generated by recombination and can bias estimates of the population recombination rate. HUDSON 2001 has suggested an approximate-likelihood method based on coalescent theory to estimate the population recombination rate, 4Ner, under an infinite-sites model of sequence evolution. Here we extend the method to the estimation of the recombination rate in genomes, such as those of many viruses and bacteria, where the rate of recurrent mutation is high. In addition, we develop a powerful permutation-based method for detecting recombination that is both more powerful than other permutation-based methods and robust to misspecification of the model of sequence evolution. We apply the method to sequence data from viruses, bacteria, and human mitochondrial DNA. The extremely high level of recombination detected in both HIV1 and HIV2 sequences demonstrates that recombination cannot be ignored in the analysis of viral population genetic data.
M3 - Journal article
VL - 160
SP - 1231
EP - 1241
JO - Genetics
JF - Genetics
SN - 0016-6731
IS - 3
ER -