Abstract
BackgroundCADM is a statistical test used to estimate the level of Congruence Among Distance Matrices. It has been shown in previous studies to have a correct rate of type I error and good power when applied to dissimilarity matrices and to ultrametric distance matrices. Contrary to most other tests of incongruence used in phylogenetic analysis, the null hypothesis of the CADM test assumes complete incongruence of the phylogenetic trees instead of congruence. In this study, we performed computer simulations to assess the type I error rate and power of the test. It was applied to additive distance matrices representing phylogenies and to genetic distance matrices obtained from nucleotide sequences of different lengths that were simulated on randomly generated trees of varying sizes, and under different evolutionary conditions.ResultsOur results showed that the test has an accurate type I error rate and good power. As expected, power increased with the number of objects (i.e., taxa), the number of partially or completely congruent matrices and the level of congruence among distance matrices.ConclusionsBased on our results, we suggest that CADM is an excellent candidate to test for congruence and, when present, to estimate its level in phylogenomic studies where numerous genes are analysed simultaneously.
Highlights
congruence among distance matrices (CADM) is a statistical test used to estimate the level of Congruence Among Distance Matrices
Type I error rates were investigated for a posteriori CADM test, where matrices included in a set under comparisons are permuted one at a time
(5) If needed, path-length distances calculated on phylogenetic trees can be used, which provide an interesting method to test for congruence among different trees in a supertree approach
Summary
CADM is a statistical test used to estimate the level of Congruence Among Distance Matrices. It has been shown in previous studies to have a correct rate of type I error and good power when applied to dissimilarity matrices and to ultrametric distance matrices. Characterstate data or distance matrices may be used, and several different types of data may be available to estimate the phylogeny of a particular group [1]. An intermediate approach, referred to as the conditional data combination, consists in testing a priori the level of congruence of different data sets. The remaining incongruent data sets are analysed separately [13,19,33,34,35]
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