Linking great apes genome evolution across time scales using polymorphism-aware phylogenetic models

Nicola De Maio, Christian Schlötterer, Carolin Kosiol

Research output: Contribution to journalArticlepeer-review

37 Citations (Scopus)


The genomes of related species contain valuable information on the history of the considered taxa. Great apes in particular exhibit variation of evolutionary patterns along their genomes. However, the great ape data also bring new challenges, such as the presence of incomplete lineage sorting and ancestral shared polymorphisms. Previous methods for genome-scale analysis are restricted to very few individuals or cannot disentangle the contribution of mutation rates and fixation biases. This represents a limitation both for the understanding of these forces as well as for the detection of regions affected by selection. Here, we present a new model designed to estimate mutation rates and fixation biases from genetic variation within and between species. We relax the assumption of instantaneous substitutions, modeling substitutions as mutational events followed by a gradual fixation. Hence, we straightforwardly account for shared ancestral polymorphisms and incomplete lineage sorting. We analyze genome-wide synonymous site alignments of human, chimpanzee, and two orangutan species. From each taxon, we include data from several individuals. We estimate mutation rates and GC-biased gene conversion intensity. We find that both mutation rates and biased gene conversion vary with GC content. We also find lineage-specific differences, with weaker fixation biases in orangutan species, suggesting a reduced historical effective population size. Finally, our results are consistent with directional selection acting on coding sequences in relation to exonic splicing enhancers.

Original languageEnglish
Pages (from-to)2249-62
Number of pages14
JournalMolecular Biology and Evolution
Issue number10
Early online date1 Aug 2013
Publication statusPublished - Oct 2013


  • Phylogenetics-population genetics model
  • Mutation rates
  • Biased gene conversion
  • Rate heterogeneity
  • Coding sequence evolution
  • Primates evolution


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