A Linear-Time Algorithm for Gaussian and Non-Gaussian Trait Evolution Models
We developed a linear-time algorithm applicable to a large class of trait evolution models, for efficient likelihood calculations and parameter inference on very large trees. Our algorithm solves the traditional computational burden associated with two key terms, namely the determinant of the phylog...
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Published in | Systematic biology Vol. 63; no. 3; pp. 397 - 408 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
England
Oxford University Press
01.05.2014
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Subjects | |
Online Access | Get full text |
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Abstract | We developed a linear-time algorithm applicable to a large class of trait evolution models, for efficient likelihood calculations and parameter inference on very large trees. Our algorithm solves the traditional computational burden associated with two key terms, namely the determinant of the phylogenetic covariance matrix V and quadratic products involving the inverse of V. Applications include Gaussian models such as Brownian motion-derived models like Pagel's lambda, kappa, delta, and the early-burst model; Ornstein-Uhlenbeck models to account for natural selection with possibly varying selection parameters along the tree; as well as non-Gaussian models such as phylogenetic logistic regression, phylogenetic Poisson regression, and phylogenetic generalized linear mixed models. Outside of phylogenetic regression, our algorithm also applies to phylogenetic principal component analysis, phylogenetic discriminant analysis or phylogenetic prediction. The computational gain opens up new avenues for complex models or extensive resampling procedures on very large trees. We identify the class of models that our algorithm can handle as all models whose covariance matrix has a 3-point structure. We further show that this structure uniquely identifies a rooted tree whose branch lengths parametrize the trait covariance matrix, which acts as a similarity matrix. The new algorithm is implemented in the R package phylolm, including functions for phylogenetic linear regression and phylogenetic logistic regression. |
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AbstractList | We developed a linear-time algorithm applicable to a large class of trait evolution models, for efficient likelihood calculations and parameter inference on very large trees. Our algorithm solves the traditional computational burden associated with two key terms, namely the determinant of the phylogenetic covariance matrix V and quadratic products involving the inverse of V. Applications include Gaussian models such as Brownian motion-derived models like Pagel's lambda, kappa, delta, and the early-burst model; Ornstein-Uhlenbeck models to account for natural selection with possibly varying selection parameters along the tree; as well as non-Gaussian models such as phylogenetic logistic regression, phylogenetic Poisson regression, and phylogenetic generalized linear mixed models. Outside of phylogenetic regression, our algorithm also applies to phylogenetic principal component analysis, phylogenetic discriminant analysis or phylogenetic prediction. The computational gain opens up new avenues for complex models or extensive resampling procedures on very large trees. We identify the class of models that our algorithm can handle as all models whose covariance matrix has a 3-point structure. We further show that this structure uniquely identifies a rooted tree whose branch lengths parametrize the trait covariance matrix, which acts as a similarity matrix. The new algorithm is implemented in the R package phylolm, including functions for phylogenetic linear regression and phylogenetic logistic regression. Abstract We developed a linear-time algorithm applicable to a large class of trait evolution models, for efficient likelihood calculations and parameter inference on very large trees. Our algorithm solves the traditional computational burden associated with two key terms, namely the determinant of the phylogenetic covariance matrix V and quadratic products involving the inverse of V. Applications include Gaussian models such as Brownian motion-derived models like Pagel's lambda, kappa, delta, and the early-burst model; Ornstein-Uhlenbeck models to account for natural selection with possibly varying selection parameters along the tree; as well as non-Gaussian models such as phylogenetic logistic regression, phylogenetic Poisson regression, and phylogenetic generalized linear mixed models. Outside of phylogenetic regression, our algorithm also applies to phylogenetic principal component analysis, phylogenetic discriminant analysis or phylogenetic prediction. The computational gain opens up new avenues for complex models or extensive resampling procedures on very large trees. We identify the class of models that our algorithm can handle as all models whose covariance matrix has a 3-point structure. We further show that this structure uniquely identifies a rooted tree whose branch lengths parametrize the trait covariance matrix, which acts as a similarity matrix. The new algorithm is implemented in the R package phylolm, including functions for phylogenetic linear regression and phylogenetic logistic regression. We developed a linear-time algorithm applicable to a large class of trait evolution models, for efficient likelihood calculations and parameter inference on very large trees. Our algorithm solves the traditional computational burden associated with two key terms, namely the determinant of the phylogenetic covariance matrix V and quadratic products involving the inverse of V. Applications include Gaussian models such as Brownian motion-derived models like Pagel's lambda, kappa, delta, and the early-burst model; Ornstein-Uhlenbeck models to account for natural selection with possibly varying selection parameters along the tree; as well as non-Gaussian models such as phylogenetic logistic regression, phylogenetic Poisson regression, and phylogenetic generalized linear mixed models. Outside of phylogenetic regression, our algorithm also applies to phylogenetic principal component analysis, phylogenetic discriminant analysis or phylogenetic prediction. The computational gain opens up new avenues for complex models or extensive resampling procedures on very large trees. We identify the class of models that our algorithm can handle as all models whose covariance matrix has a 3-point structure. We further show that this structure uniquely identifies a rooted tree whose branch lengths parametrize the trait covariance matrix, which acts as a similarity matrix. The new algorithm is implemented in the R package phylolm, including functions for phylogenetic linear regression and phylogenetic logistic regression. [PUBLICATION ABSTRACT] |
Author | si Tung Ho, Lam Ané, Cécile |
Author_xml | – sequence: 1 givenname: Lam surname: si Tung Ho fullname: si Tung Ho, Lam – sequence: 2 givenname: Cécile surname: Ané fullname: Ané, Cécile |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/24500037$$D View this record in MEDLINE/PubMed |
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Cites_doi | 10.1016/j.jtbi.2012.08.005 10.3389/fpls.2011.00034 10.1126/science.1163197 10.1038/nature10532 10.1093/sysbio/syp074 10.1093/sysbio/42.3.265 10.1137/1.9781611971217 10.1137/1031049 10.1111/j.1420-9101.2009.01915.x 10.1038/nature05634 10.1006/jtbi.2002.3066 10.1080/01621459.1962.10480665 10.1098/rspb.2006.3488 10.1111/j.0014-3820.2006.tb01171.x 10.1111/j.2517-6161.1977.tb01600.x 10.1038/nature11631 10.1080/10635150701313830 10.1098/rstb.1989.0106 10.1214/13-AOS1105 10.1038/nature10516 10.1111/j.1365-2699.2012.02769.x 10.1086/652466 10.1111/j.1558-5646.1991.tb04375.x 10.1111/j.1558-5646.2011.01574.x 10.1111/j.0014-3820.2003.tb00285.x 10.1086/303327 10.1111/j.1558-5646.2011.01435.x 10.1111/j.1558-5646.2011.01271.x 10.1111/j.1558-5646.2012.01619.x 10.1094/PHYTO-02-11-0060 10.1111/j.1558-5646.2012.01645.x 10.1890/10-1264.1 10.1086/286013 10.1111/j.1558-5646.1997.tb01457.x 10.1111/j.1463-6409.1997.tb00423.x 10.1086/587525 10.1111/j.2041-210X.2012.00220.x 10.1086/426002 10.1111/j.1558-5646.2011.01401.x 10.1093/sysbio/syr122 10.1093/oso/9780198509424.001.0001 10.1111/j.1558-5646.2009.00804.x 10.1038/44766 10.18637/jss.v033.i02 10.1371/journal.pbio.0040373 10.1002/9781119115151 10.1111/j.2041-210X.2012.00234.x |
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References | Felsenstein ( key 20170622051815_B13) 1973; 25 Hadfield ( key 20170622051815_B23) 2010; 33 Harmon ( key 20170622051815_B28) 2010; 64 Lawson ( key 20170622051815_B36) 1995 Revell ( key 20170622051815_B47) 2012; 66 Revell ( key 20170622051815_B45) 2009; 63 Ho ( key 20170622051815_B29) 2013; 41 Smith ( key 20170622051815_B50) 2008; 322 O'Meara ( key 20170622051815_B41) 2006; 60 Bartoszek ( key 20170622051815_B1) 2012; 314 Pagel ( key 20170622051815_B42) 1997; 26 Garland ( key 20170622051815_B19) 2000; 155 Cai ( key 20170622051815_B8) 2011; 101 Venditti ( key 20170622051815_B52) 2011; 479 Eastman ( key 20170622051815_B12) 2011; 65 Hansen ( key 20170622051815_B26) 1997; 51 Brawand ( key 20170622051815_B6) 2011; 478 Boettiger ( key 20170622051815_B5) 2012; 66 Lynch ( key 20170622051815_B38) 1991; 45 Ives ( key 20170622051815_B30) 2011; 81 Goff ( key 20170622051815_B20) 2011; 2 key 20170622051815_B34 Martins ( key 20170622051815_B39) 1997; 149 Freckleton ( key 20170622051815_B16) 2012; 3 Ives ( key 20170622051815_B32) 2007; 56 Paradis ( key 20170622051815_B44) 2002; 218 Thomas ( key 20170622051815_B51) 2006; 273 Bininda-Emonds ( key 20170622051815_B3) 2007; 446 Butler ( key 20170622051815_B7) 2004; 164 Cressie ( key 20170622051815_B10) 1993 Pagel ( key 20170622051815_B43) 1999; 401 Liang ( key 20170622051815_B37) 2007 Garland ( key 20170622051815_B18) 1993; 42 Cooper ( key 20170622051815_B9) 2010; 175 Revell ( key 20170622051815_B46) 2008; 10 Lavergne ( key 20170622051815_B35) 2013; 40 Hadfield ( key 20170622051815_B24) 2010; 23 Goldberger ( key 20170622051815_B21) 1962; 57 Beaulieu ( key 20170622051815_B2) 2012; 66 Motani ( key 20170622051815_B40) 2011; 65 Freckleton ( key 20170622051815_B17) 2006; 4 Felsenstein ( key 20170622051815_B14) 2008; 171 Semple ( key 20170622051815_B49) 2003 Ives ( key 20170622051815_B31) 2010; 59 Blomberg ( key 20170622051815_B4) 2003; 57 Hager ( key 20170622051815_B25) 1989; 31 Revell ( key 20170622051815_B48) 2012; 66 Jetz ( key 20170622051815_B33) 2012; 491 FitzJohn ( key 20170622051815_B15) 2012; 3 Dempster ( key 20170622051815_B11) 1977; 39 Hansen ( key 20170622051815_B27) 2012; 61 Grafen ( key 20170622051815_B22) 1989; 326 |
References_xml | – volume: 314 start-page: 204 year: 2012 ident: key 20170622051815_B1 article-title: A phylogenetic comparative method for studying multivariate adaptation publication-title: J. Theor. Biolog. doi: 10.1016/j.jtbi.2012.08.005 contributor: fullname: Bartoszek – volume-title: Springer Series in Statistics year: 2007 ident: key 20170622051815_B37 article-title: Linear and Generalized Linear Mixed Models and their Applications contributor: fullname: Liang – volume: 2 year: 2011 ident: key 20170622051815_B20 article-title: The iplant collaborative: Cyberinfrastructure for plant biology publication-title: Front. Plant Sci. doi: 10.3389/fpls.2011.00034 contributor: fullname: Goff – volume: 64 start-page: 2385 year: 2010 ident: key 20170622051815_B28 article-title: Early bursts of body size and shape evolution are rare in comparative data publication-title: Evolution contributor: fullname: Harmon – volume: 322 start-page: 86 year: 2008 ident: key 20170622051815_B50 article-title: Rates of molecular evolution are linked to life history in flowering plants publication-title: Science doi: 10.1126/science.1163197 contributor: fullname: Smith – volume: 478 start-page: 343 year: 2011 ident: key 20170622051815_B6 article-title: The evolution of gene expression levels in mammalian organs publication-title: Nature doi: 10.1038/nature10532 contributor: fullname: Brawand – ident: key 20170622051815_B34 – volume: 59 start-page: 9 year: 2010 ident: key 20170622051815_B31 article-title: Phylogenetic logistic regression for binary dependent variables publication-title: Syst. Biol. doi: 10.1093/sysbio/syp074 contributor: fullname: Ives – volume: 42 start-page: 265 year: 1993 ident: key 20170622051815_B18 article-title: Phylogenetic analysis of covariance by computer simulation publication-title: Syst. Biol. doi: 10.1093/sysbio/42.3.265 contributor: fullname: Garland – volume-title: Classics in Applied Mathematics Society for Industrial and Applied Mathematics year: 1995 ident: key 20170622051815_B36 article-title: Solving Least Squares Problems doi: 10.1137/1.9781611971217 contributor: fullname: Lawson – volume: 31 start-page: 221 year: 1989 ident: key 20170622051815_B25 article-title: Updating the inverse of a matrix publication-title: SIAM Rev. doi: 10.1137/1031049 contributor: fullname: Hager – volume: 23 start-page: 494 year: 2010 ident: key 20170622051815_B24 article-title: General quantitative genetic methods for comparative biology: phylogenies, taxonomies and multi-trait models for continuous and categorical characters publication-title: J. Evol. Biol. doi: 10.1111/j.1420-9101.2009.01915.x contributor: fullname: Hadfield – volume: 446 start-page: 507 year: 2007 ident: key 20170622051815_B3 article-title: The delayed rise of present-day mammals publication-title: Nature doi: 10.1038/nature05634 contributor: fullname: Bininda-Emonds – volume: 218 start-page: 175 year: 2002 ident: key 20170622051815_B44 article-title: Analysis of comparative data using generalized estimating equations publication-title: J. Theor. Biol. doi: 10.1006/jtbi.2002.3066 contributor: fullname: Paradis – volume: 57 start-page: 369 year: 1962 ident: key 20170622051815_B21 article-title: Best linear unbiased prediction in the generalized linear regression model publication-title: J. Am. Stat. Assoc. doi: 10.1080/01621459.1962.10480665 contributor: fullname: Goldberger – volume: 273 start-page: 1619 year: 2006 ident: key 20170622051815_B51 article-title: Comparative analyses of the influence of developmental mode on phenotypic diversification rates in shorebirds publication-title: Proc. R. Soc. B: Biol. Sci. doi: 10.1098/rspb.2006.3488 contributor: fullname: Thomas – volume: 60 start-page: 922 year: 2006 ident: key 20170622051815_B41 article-title: Testing for different rates of continuous trait evolution using likelihood publication-title: Evolution doi: 10.1111/j.0014-3820.2006.tb01171.x contributor: fullname: O'Meara – volume: 39 start-page: 1 year: 1977 ident: key 20170622051815_B11 article-title: Maximum likelihood from incomplete data via the EM algorithm publication-title: J. R. Stat. Soc. doi: 10.1111/j.2517-6161.1977.tb01600.x contributor: fullname: Dempster – volume: 25 start-page: 471 year: 1973 ident: key 20170622051815_B13 article-title: Maximum-likelihood estimation of evolutionary trees from continuous characters publication-title: Am. J. Hum. Genet. contributor: fullname: Felsenstein – volume: 491 start-page: 444 year: 2012 ident: key 20170622051815_B33 article-title: The global diversity of birds in space and time publication-title: Nature doi: 10.1038/nature11631 contributor: fullname: Jetz – volume: 56 start-page: 252 year: 2007 ident: key 20170622051815_B32 article-title: Within-species variation and measurement error in phylogenetic comparative methods publication-title: Syst. Biol. doi: 10.1080/10635150701313830 contributor: fullname: Ives – volume: 326 start-page: 119 year: 1989 ident: key 20170622051815_B22 article-title: The phylogenetic regression publication-title: Phil. Trans. R. Soc. London. Series B, Biol. Sci. doi: 10.1098/rstb.1989.0106 contributor: fullname: Grafen – volume: 41 start-page: 957 year: 2013 ident: key 20170622051815_B29 article-title: Asymptotic theory with hierarchical autocorrelation: Ornstein-uhlenbeck tree models publication-title: Annals Stat. doi: 10.1214/13-AOS1105 contributor: fullname: Ho – volume: 479 start-page: 393 year: 2011 ident: key 20170622051815_B52 article-title: Multiple routes to mammalian diversity publication-title: Nature doi: 10.1038/nature10516 contributor: fullname: Venditti – volume: 40 start-page: 24 year: 2013 ident: key 20170622051815_B35 article-title: In and out of africa: how did the strait of gibraltar affect plant species migration and local diversification? publication-title: J. Biogeography doi: 10.1111/j.1365-2699.2012.02769.x contributor: fullname: Lavergne – volume: 175 start-page: 727 year: 2010 ident: key 20170622051815_B9 article-title: Body size evolution in mammals: Complexity in tempo and mode publication-title: Am. Nat. doi: 10.1086/652466 contributor: fullname: Cooper – volume: 45 start-page: 1065 year: 1991 ident: key 20170622051815_B38 article-title: Methods for the analysis of comparative data in evolutionary biology publication-title: Evolution doi: 10.1111/j.1558-5646.1991.tb04375.x contributor: fullname: Lynch – volume: 66 start-page: 2240 year: 2012 ident: key 20170622051815_B5 article-title: Is your phylogeny informative? measuring the power of comparative methods publication-title: Evolution doi: 10.1111/j.1558-5646.2011.01574.x contributor: fullname: Boettiger – volume: 57 start-page: 717 year: 2003 ident: key 20170622051815_B4 article-title: Testing for phylogenetic signal in comparative data: behavioral traits are more labile publication-title: Evolution doi: 10.1111/j.0014-3820.2003.tb00285.x contributor: fullname: Blomberg – volume: 155 start-page: 346 year: 2000 ident: key 20170622051815_B19 article-title: Using the past to predict the present: Confidence intervals for regression equations in phylogenetic comparative methods publication-title: Am. Nat. doi: 10.1086/303327 contributor: fullname: Garland – volume: 66 start-page: 135 year: 2012 ident: key 20170622051815_B47 article-title: A new phylogenetic method for identifying exceptional phenotypic diversification publication-title: Evolution doi: 10.1111/j.1558-5646.2011.01435.x contributor: fullname: Revell – volume: 65 start-page: 2245 year: 2011 ident: key 20170622051815_B40 article-title: Phylogenetic versus functional signals in the evolution of form-function relationships in terrestrial vision publication-title: Evolution doi: 10.1111/j.1558-5646.2011.01271.x contributor: fullname: Motani – volume: 66 start-page: 2369 year: 2012 ident: key 20170622051815_B2 article-title: Modeling stabilizing selection: Expanding the Ornstein-Ühlenbeck model of adaptive evolution publication-title: Evolution doi: 10.1111/j.1558-5646.2012.01619.x contributor: fullname: Beaulieu – volume: 101 start-page: 1074 year: 2011 ident: key 20170622051815_B8 article-title: A test of taxonomic and biogeographic predictivity: Resistance to potato virus Y in wild relatives of the cultivated potato publication-title: Phytopathology doi: 10.1094/PHYTO-02-11-0060 contributor: fullname: Cai – volume: 66 start-page: 2697 year: 2012 ident: key 20170622051815_B48 article-title: A new Bayesian method for fitting evolutionary models to comparative data with intraspecific variation publication-title: Evolution doi: 10.1111/j.1558-5646.2012.01645.x contributor: fullname: Revell – volume: 81 start-page: 511 year: 2011 ident: key 20170622051815_B30 article-title: Generalized linear mixed models for phylogenetic analyses of community structure publication-title: Ecol. Monographs doi: 10.1890/10-1264.1 contributor: fullname: Ives – volume: 149 start-page: 646 year: 1997 ident: key 20170622051815_B39 article-title: Phylogenies and the comparative method: A general approach to incorporating phylogenetic information into the analysis of interspecific data publication-title: Am. Nat. doi: 10.1086/286013 contributor: fullname: Martins – volume: 51 start-page: 1341 year: 1997 ident: key 20170622051815_B26 article-title: Stabilizing selection and the comparative analysis of adaptation publication-title: Evolution doi: 10.1111/j.1558-5646.1997.tb01457.x contributor: fullname: Hansen – volume: 26 start-page: 331 year: 1997 ident: key 20170622051815_B42 article-title: Inferring evolutionary processes from phylogenies publication-title: Zoologica Scripta doi: 10.1111/j.1463-6409.1997.tb00423.x contributor: fullname: Pagel – volume: 171 start-page: 713 year: 2008 ident: key 20170622051815_B14 article-title: Comparative methods with sampling error and withinspecies variation: Contrasts revisited and revised publication-title: Am. Nat. doi: 10.1086/587525 contributor: fullname: Felsenstein – volume: 3 start-page: 940 year: 2012 ident: key 20170622051815_B16 article-title: Fast likelihood calculations for comparative analyses publication-title: Methods Ecol. Evol. doi: 10.1111/j.2041-210X.2012.00220.x contributor: fullname: Freckleton – volume: 164 start-page: 683 year: 2004 ident: key 20170622051815_B7 article-title: Phylogenetic comparative analysis: A modeling approach for adaptive evolution publication-title: Am. Nat. doi: 10.1086/426002 contributor: fullname: Butler – volume: 65 start-page: 3578 year: 2011 ident: key 20170622051815_B12 article-title: A novel comparative method for identifying shifts in the rate of character evolution on trees publication-title: Evolution doi: 10.1111/j.1558-5646.2011.01401.x contributor: fullname: Eastman – volume: 61 start-page: 413 year: 2012 ident: key 20170622051815_B27 article-title: Interpreting the evolutionary regression: The interplay between observational and biological errors in phylogenetic comparative studies publication-title: Syst. Biol. doi: 10.1093/sysbio/syr122 contributor: fullname: Hansen – volume-title: Oxford Lecture Series in Mathematics and its Applications year: 2003 ident: key 20170622051815_B49 article-title: Phylogenetics doi: 10.1093/oso/9780198509424.001.0001 contributor: fullname: Semple – volume: 63 start-page: 3258 year: 2009 ident: key 20170622051815_B45 article-title: Size-correction and principal components for interspecific comparative studies publication-title: Evolution doi: 10.1111/j.1558-5646.2009.00804.x contributor: fullname: Revell – volume: 401 start-page: 877 year: 1999 ident: key 20170622051815_B43 article-title: Inferring the historical patterns of biological evolution publication-title: Nature doi: 10.1038/44766 contributor: fullname: Pagel – volume: 33 start-page: 1 year: 2010 ident: key 20170622051815_B23 article-title: MCMC methods for multi-response generalized linear mixed models: the MCMCglmm R package publication-title: J. Stat. Software doi: 10.18637/jss.v033.i02 contributor: fullname: Hadfield – volume: 10 start-page: 311 year: 2008 ident: key 20170622051815_B46 article-title: Testing quantitative genetic hypotheses about the evolutionary rate matrix for continuous characters publication-title: Evol. Ecol. Res. contributor: fullname: Revell – volume: 4 start-page: e373 year: 2006 ident: key 20170622051815_B17 article-title: Detecting non-brownian trait evolution in adaptive radiations publication-title: PLoS Biol. doi: 10.1371/journal.pbio.0040373 contributor: fullname: Freckleton – volume-title: Statistics for Spatial Data year: 1993 ident: key 20170622051815_B10 doi: 10.1002/9781119115151 contributor: fullname: Cressie – volume: 3 start-page: 1084 year: 2012 ident: key 20170622051815_B15 article-title: Diversitree: comparative phylogenetic analyses of diversification in R publication-title: Methods Ecol. Evol. doi: 10.1111/j.2041-210X.2012.00234.x contributor: fullname: FitzJohn |
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SubjectTerms | Algorithms Biological Evolution Classification - methods Computer Simulation Covariance Covariance matrices Evolution Modeling Normal distribution Parametric models Phenotypic traits Phylogenetics Plant roots Regression analysis Software - standards Systematic biology Taxa |
Title | A Linear-Time Algorithm for Gaussian and Non-Gaussian Trait Evolution Models |
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