Measuring phylogenetic signal between categorical traits and phylogenies
Abstract Motivation Determining whether a trait and phylogeny share some degree of phylogenetic signal is a flagship goal in evolutionary biology. Signatures of phylogenetic signal can assist the resolution of a broad range of evolutionary questions regarding the tempo and mode of phenotypic evolution. However, despite the considerable number of strategies to measure it, few and limited approaches exist for categorical traits. Here, we used the concept of Shannon entropy and propose the δ statistic for evaluating the degree of phylogenetic signal between a phylogeny and categorical traits. Results We validated δ as a measure of phylogenetic signal: the higher the δ-value the higher the degree of phylogenetic signal between a given tree and a trait. Based on simulated data we proposed a threshold-based classification test to pinpoint cases of phylogenetic signal. The assessment of the test’s specificity and sensitivity suggested that the δ approach should only be applied to 20 or more species. We have further tested the performance of δ in scenarios of branch length and topology uncertainty, unbiased and biased trait evolution and trait saturation. Our results showed that δ may be applied in a wide range of phylogenetic contexts. Finally, we investigated our method in 14 360 mammalian gene trees and found that olfactory receptor genes are significantly associated with the mammalian activity patterns, a result that is congruent with expectations and experiments from the literature. Our application shows that δ can successfully detect molecular signatures of phenotypic evolution. We conclude that δ represents a useful measure of phylogenetic signal since many phenotypes can only be measured in categories. Availability and implementation https://github.com/mrborges23/delta_statistic. Supplementary information Supplementary data are available at Bioinformatics online.
- Sponsoring Organization:
- USDOE Office of Nuclear Energy (NE), Nuclear Fuel Cycle and Supply Chain; USDOE Office of Nuclear Energy (NE), Fuel Cycle Technologies (NE-5)
- Grant/Contract Number:
- RB: SFRH/BD/79850/2011; SFRH/BD/71041/2010; CG: SFRH/BD/71041/2010; PTDC/AAG-GLO/6887/2014; POCI-01-0124-FEDER-016845
- OSTI ID:
- 1524121
- Alternate ID(s):
- OSTI ID: 1480148
- Journal Information:
- Bioinformatics, Journal Name: Bioinformatics Vol. 35 Journal Issue: 11; ISSN 1367-4803
- Publisher:
- Oxford University PressCopyright Statement
- Country of Publication:
- United Kingdom
- Language:
- English
Web of Science
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