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dc.contributor.author
Mitov, Venelin
dc.contributor.author
Bartoszek, Krzysztof
dc.contributor.author
Stadler, Tanja
dc.date.accessioned
2019-09-02T08:15:36Z
dc.date.available
2019-09-01T02:10:37Z
dc.date.available
2019-09-02T08:15:36Z
dc.date.issued
2019-08-20
dc.identifier.issn
0027-8424
dc.identifier.issn
1091-6490
dc.identifier.other
10.1073/pnas.1813823116
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/361685
dc.identifier.doi
10.3929/ethz-b-000361685
dc.description.abstract
Phylogenetic comparative methods are widely used to understand and quantify the evolution of phenotypic traits, based on phylogenetic trees and trait measurements of extant species. Such analyses depend crucially on the underlying model. Gaussian phylogenetic models like Brownian motion and Ornstein–Uhlenbeck processes are the workhorses of modeling continuous-trait evolution. However, these models fit poorly to big trees, because they neglect the heterogeneity of the evolutionary process in different lineages of the tree. Previous works have addressed this issue by introducing shifts in the evolutionary model occurring at inferred points in the tree. However, for computational reasons, in all current implementations, these shifts are “intramodel,” meaning that they allow jumps in 1 or 2 model parameters, keeping all other parameters “global” for the entire tree. There is no biological reason to restrict a shift to a single model parameter or, even, to a single type of model. Mixed Gaussian phylogenetic models (MGPMs) incorporate the idea of jointly inferring different types of Gaussian models associated with different parts of the tree. Here, we propose an approximate maximum-likelihood method for fitting MGPMs to comparative data comprising possibly incomplete measurements for several traits from extant and extinct phylogenetically linked species. We applied the method to the largest published tree of mammal species with body- and brain-mass measurements, showing strong statistical support for an MGPM with 12 distinct evolutionary regimes. Based on this result, we state a hypothesis for the evolution of the brain–body-mass allometry over the past 160 million y.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
National Academy of Sciences
en_US
dc.rights.uri
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject
correlated quantitative traits
en_US
dc.subject
selection
en_US
dc.subject
evolutionary regimes
en_US
dc.subject
clustering
en_US
dc.subject
nonultrametric tree
en_US
dc.title
Automatic generation of evolutionary hypotheses using mixed Gaussian phylogenetic models
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
dc.date.published
2019-08-02
ethz.journal.title
Proceedings of the National Academy of Sciences of the United States of America
ethz.journal.volume
116
en_US
ethz.journal.issue
34
en_US
ethz.journal.abbreviated
Proc Natl Acad Sci U S A
ethz.pages.start
16921
en_US
ethz.pages.end
16926
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
Washington, DC
en_US
ethz.publication.status
published
en_US
ethz.date.deposited
2019-09-01T02:10:40Z
ethz.source
WOS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2019-09-02T08:15:55Z
ethz.rosetta.lastUpdated
2019-09-02T08:15:55Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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