Abstract

Accurate divergence date estimates improve scenarios of primate evolutionary history and aid in interpretation of the natural history of disease-causing agents. While molecule-based estimates of divergence dates of taxa within the superfamily Hominoidea (apes and humans) are common in the literature, few such estimates are available for the Cercopithecoidea (Old World monkeys), the sister taxon of the hominoids in the primate infraorder Catarrhini. To help fill this gap, we have sequenced the entire mitochondrial DNA (mtDNA) genomes from a representative of three cercopithecoid tribes, Cercopithecini ( Chlorocebus aethiops), Colobini ( Colobus guereza), and Presbytini ( Trachypithecus obscurus), and analyzed these new data together with other catarrhine mtDNA genomes available in public databases. Molecular divergence date estimates are dependent on calibration points gleaned from the paleontological record. We defined criteria for the selection of good calibration points and identified three points meeting these criteria: Homo- Pan, 6.0 Ma; Pongo-hominines, 14.0 Ma; hominoid/cercopithecoid, 23.0 Ma. Because a uniform molecular clock does not fit the catarrhine mtDNA data, we estimated divergence dates using a penalized likelihood and a Bayesian method, both of which take into account the effects of rate differences on lineages, phylogenetic tree structure, and multiple calibration points. The penalized likelihood method applied to the coding regions of the mtDNA genome yielded the following divergence date estimates, with approximate 95% confidence intervals: cercopithecine-colobine, 16.2 (14.4-17.9) Ma; colobin-presbytin, 10.9 (9.6-12.3) Ma; cercopithecin-papionin, 11.6 (10.3-12.9) Ma; and Macaca- Papio, 9.8 (8.6-10.9) Ma. Within the hominoids, the following dates were inferred: hylobatid-hominid, 16.8 (15.0-18.5) Ma; Gorilla- Homo + Pan, 8.1 (7.1-9.0) Ma; Pongo pygmaeus pygmaeus- P. p. abelii, 4.1 (3.5-4.7) Ma; and Pan troglodytes- P. paniscus, 2.4 (2.0-2.7) Ma. These dates were similar to those found using penalized likelihood on other subsets of the data, but slightly younger than several of the Bayesian estimates.

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