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</script>pmid: 29701821
Abstract The goal of phylogenetic comparative methods (PCMs) is to study the distribution of quantitative traits among related species. The observed traits are often seen as the result of a Brownian Motion (BM) along the branches of a phylogenetic tree. Reticulation events such as hybridization, gene flow or horizontal gene transfer, can substantially affect a species’ traits, but are not modeled by a tree. Phylogenetic networks have been designed to represent reticulate evolution. As they become available for downstream analyses, new models of trait evolution are needed, applicable to networks. We develop here an efficient recursive algorithm to compute the phylogenetic variance matrix of a trait on a network, in only one preorder traversal of the network. We then extend the standard PCM tools to this new framework, including phylogenetic regression with covariates (or phylogenetic ANOVA), ancestral trait reconstruction, and Pagel’s $\lambda$ test of phylogenetic signal. The trait of a hybrid is sometimes outside of the range of its two parents, for instance because of hybrid vigor or hybrid depression. These two phenomena are rather commonly observed in present-day hybrids. Transgressive evolution can be modeled as a shift in the trait value following a reticulation point. We develop a general framework to handle such shifts and take advantage of the phylogenetic regression view of the problem to design statistical tests for ancestral transgressive evolution in the evolutionary history of a group of species. We study the power of these tests in several scenarios and show that recent events have indeed the strongest impact on the trait distribution of present-day taxa. We apply those methods to a data set of Xiphophorus fishes, to confirm and complete previous analysis in this group. All the methods developed here are available in the Julia package PhyloNetworks.
[SDE] Environmental Sciences, Gene Flow, Gene Transfer, Horizontal, [SDV]Life Sciences [q-bio], 590, [MATH] Mathematics [math], [INFO] Computer Science [cs], Evolution, Molecular, Cyprinodontiformes, phylogenetic networks, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, Animals, [INFO]Computer Science [cs], [SDV.BV] Life Sciences [q-bio]/Vegetal Biology, [MATH]Mathematics [math], transgressive evolution, Phylogeny, Models, Genetic, phylonetworks, [SDV] Life Sciences [q-bio], Phenotype, phylogenetic comparative methods, [SDE]Environmental Sciences, Hybridization, Genetic, Heterosis, Algorithms
[SDE] Environmental Sciences, Gene Flow, Gene Transfer, Horizontal, [SDV]Life Sciences [q-bio], 590, [MATH] Mathematics [math], [INFO] Computer Science [cs], Evolution, Molecular, Cyprinodontiformes, phylogenetic networks, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, Animals, [INFO]Computer Science [cs], [SDV.BV] Life Sciences [q-bio]/Vegetal Biology, [MATH]Mathematics [math], transgressive evolution, Phylogeny, Models, Genetic, phylonetworks, [SDV] Life Sciences [q-bio], Phenotype, phylogenetic comparative methods, [SDE]Environmental Sciences, Hybridization, Genetic, Heterosis, Algorithms
| citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 72 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 1% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
