Simon H. Martin | bioRxiv Science

bioRxiv2016

Exploring evolutionary relationships across the genome using topology weighting

We introduce the concept of topology weighting, a method for quantifying relationships between taxa that are not necessarily monophyletic, and visualising 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 sub-trees (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.\n\nScripts to implement the method described are available at github.com/simonhmartin/twisst.

Evolutionary Biology↗

bioRxiv2013

Evaluating the use of ABBA-BABA statistics to locate introgressed loci

Several methods have been proposed to test for introgression across genomes. One method tests for a genome-wide excess of shared derived alleles between taxa using Pattersons D statistic, but does not establish which loci show such an excess or whether the excess is due to introgression or ancestral population structure. Several recent studies have extended the use of D by applying the statistic to small genomic regions, rather than genome-wide. Here, we use simulations and whole genome data from Heliconius butterflies to investigate the behavior of D in small genomic regions. We find that D is unreliable in this situation as it gives inflated values when effective population size is low, causing D outliers to cluster in genomic regions of reduced diversity. As an alternative, we propose a related statistic [Formula] a modified version of a statistic originally developed to estimate the genome-wide fraction of admixture. [Formula] is not subject to the same biases as D, and is better at identifying introgressed loci. Finally, we show that both D and [Formula] outliers tend to cluster in regions of low absolute divergence (dXY), which can confound a recently proposed test for differentiating introgression from shared ancestral variation at individual loci.

Evolutionary Biology↗