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SPECIES TREE INFERENCE FROM GENOMIC SEQUENCES USING THE LOG-DET DISTANCE
Elizabeth S Allman1, Colby Long2, John A Rhodes1
1Department of Mathematics and Statistics, University of Alaska Fairbanks, Fairbanks, AK.
Abstract:
The log-det distance between two aligned DNA sequences was introduced as a tool for statistically consistent inference of a gene tree under simple non-mixture models of sequence evolution. Here we prove that the log-det distance, coupled with a distance-based tree construction method, also permits consistent inference of species trees under mixture models appropriate to aligned genomic-scale sequences data. Data may include sites from many genetic loci, which evolved on different gene trees due to incomplete lineage sorting on an ultrametric species tree, with different time-reversible substitution processes. The simplicity and speed of distance-based inference suggests log-det based methods should serve as benchmarks for judging more elaborate and computationally-intensive species trees inference methods.
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