Dataset Information

Exploring Evolutionary Relationships Across the Genome Using Topology Weighting.

ABSTRACT: We introduce the concept of topology weighting, a method for quantifying relationships between taxa that are not necessarily monophyletic, and visualizing how these relationships change across the genome. A given set of taxa can be related in a limited number of ways, but if each taxon is represented by multiple sequences, the number of possible topologies becomes very large. Topology weighting reduces this complexity by quantifying the contribution of each taxon topology to the full tree. We describe our method for topology weighting by iterative sampling of subtrees (Twisst), and test it on both simulated and real genomic data. Overall, we show that this is an informative and versatile approach, suitable for exploring relationships in almost any genomic dataset. Scripts to implement the method described are available at http://github.com/simonhmartin/twisst.

SUBMITTER: Martin SH

PROVIDER: S-EPMC5419486 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Exploring Evolutionary Relationships Across the Genome Using Topology Weighting.

Martin Simon H SH Van Belleghem Steven M SM Van Belleghem Steven M SM

Genetics 20170324 1

We introduce the concept of topology weighting, a method for quantifying relationships between taxa that are not necessarily monophyletic, and visualizing how these relationships change across the genome. A given set of taxa can be related in a limited number of ways, but if each taxon is represented by multiple sequences, the number of possible topologies becomes very large. Topology weighting reduces this complexity by quantifying the contribution of each taxon topology to the full tree. We de ...[more]

PMID: 28341652

Similar Datasets

Project description:Using genetic data to resolve the evolutionary relationships of species is of major interest in evolutionary and systematic biology. However, reconstructing the sequence of speciation events, the so-called species tree, in closely related and potentially hybridizing species is very challenging. Processes such as incomplete lineage sorting and interspecific gene flow result in local gene genealogies that differ in their topology from the species tree, and analyses of few loci with a single sequence per species are likely to produce conflicting or even misleading results. To study these phenomena on a full phylogenomic scale, we use whole-genome sequence data from 200 individuals of four black-and-white flycatcher species with so far unresolved phylogenetic relationships to infer gene tree topologies and visualize genome-wide patterns of gene tree incongruence. Using phylogenetic analysis in nonoverlapping 10-kb windows, we show that gene tree topologies are extremely diverse and change on a very small physical scale. Moreover, we find strong evidence for gene flow among flycatcher species, with distinct patterns of reduced introgression on the Z chromosome. To resolve species relationships on the background of widespread gene tree incongruence, we used four complementary coalescent-based methods for species tree reconstruction, including complex modeling approaches that incorporate post-divergence gene flow among species. This allowed us to infer the most likely species tree with high confidence. Based on this finding, we show that regions of reduced effective population size, which have been suggested as particularly useful for species tree inference, can produce positively misleading species tree topologies. Our findings disclose the pitfalls of using loci potentially under selection as phylogenetic markers and highlight the potential of modeling approaches to disentangle species relationships in systems with large effective population sizes and post-divergence gene flow.

Dataset Information

Exploring Evolutionary Relationships Across the Genome Using Topology Weighting.

Publications

Exploring Evolutionary Relationships Across the Genome Using Topology Weighting.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure