  Because biological processes can make different loci have different
evolutionary histories, species tree estimation requires multiple loci from
across the genome. While many processes can result in discord between gene
trees and species trees, incomplete lineage sorting (ILS), modeled by the
multi-species coalescent, is considered to be a dominant cause for gene tree
heterogeneity. Coalescent-based methods have been developed to estimate species
trees, many of which operate by combining estimated gene trees, and so are
called summary methods. Because summary methods are generally fast, they have
become very popular techniques for estimating species trees from multiple loci.
However, recent studies have established that summary methods can have reduced
accuracy in the presence of gene tree estimation error, and also that many
biological datasets have substantial gene tree estimation error, so that
summary methods may not be highly accurate on biologically realistic
conditions. Mirarab et al. (Science 2014) presented the statistical binning
technique to improve gene tree estimation in multi-locus analyses, and showed
that it improved the accuracy of MP-EST, one of the most popular
coalescent-based summary methods. Statistical binning, which uses a simple
statistical test for combinability and then uses the larger sets of genes to
re-calculate gene trees, has good empirical performance, but using statistical
binning within a phylogenomics pipeline does not have the desirable property of
being statistically consistent. We show that weighting the recalculated gene
trees by the bin sizes makes statistical binning statistically consistent under
the multispecies coalescent, and maintains the good empirical performance.
Thus, "weighted statistical binning" enables highly accurate genome-scale
species tree estimation, and is also statistical consistent under the
multi-species coalescent model.
