Performance of maximum parsimony and likelihood phylogenetics when evolution is heterogeneous
All inferences in comparative biology depend on accurate estimates of evolutionary relationships. Recent phylogenetic analyses have turned away from maximum parsimony towards the probabilistic techniques of maximum likelihood and bayesian Markov chain Monte Carlo (BMCMC). These probabilistic techniq...
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| Published in | Nature (London) Vol. 431; no. 7011; pp. 980 - 984 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
London
Nature Publishing Group UK
21.10.2004
Nature Publishing Nature Publishing Group |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0028-0836 1476-4687 1476-4687 |
| DOI | 10.1038/nature02917 |
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| Abstract | All inferences in comparative biology depend on accurate estimates of evolutionary relationships. Recent phylogenetic analyses have turned away from maximum parsimony towards the probabilistic techniques of maximum likelihood and bayesian Markov chain Monte Carlo (BMCMC). These probabilistic techniques represent a parametric approach to statistical phylogenetics, because their criterion for evaluating a topology—the probability of the data, given the tree—is calculated with reference to an explicit evolutionary model from which the data are assumed to be identically distributed. Maximum parsimony can be considered nonparametric, because trees are evaluated on the basis of a general metric—the minimum number of character state changes required to generate the data on a given tree—without assuming a specific distribution
1
. The shift to parametric methods was spurred, in large part, by studies showing that although both approaches perform well most of the time
2
, maximum parsimony is strongly biased towards recovering an incorrect tree under certain combinations of branch lengths, whereas maximum likelihood is not
3
,
4
,
5
,
6
. All these evaluations simulated sequences by a largely homogeneous evolutionary process in which data are identically distributed. There is ample evidence, however, that real-world gene sequences evolve heterogeneously and are not identically distributed
7
,
8
,
9
,
10
,
11
,
12
,
13
,
14
,
15
,
16
. Here we show that maximum likelihood and BMCMC can become strongly biased and statistically inconsistent when the rates at which sequence sites evolve change non-identically over time. Maximum parsimony performs substantially better than current parametric methods over a wide range of conditions tested, including moderate heterogeneity and phylogenetic problems not normally considered difficult. |
|---|---|
| AbstractList | All inferences in comparative biology depend on accurate estimates of evolutionary relationships. Recent phylogenetic analyses have turned away from maximum parsimony towards the probabilistic techniques of maximum likelihood and bayesian Markov chain Monte Carlo (BMCMC). These probabilistic techniques represent a parametric approach to statistical phylogenetics, because their criterion for evaluating a topology--the probability of the data, given the tree--is calculated with reference to an explicit evolutionary model from which the data are assumed to be identically distributed. Maximum parsimony can be considered nonparametric, because trees are evaluated on the basis of a general metric--the minimum number of character state changes required to generate the data on a given tree--without assuming a specific distribution. The shift to parametric methods was spurred, in large part, by studies showing that although both approaches perform well most of the time, maximum parsimony is strongly biased towards recovering an incorrect tree under certain combinations of branch lengths, whereas maximum likelihood is not. All these evaluations simulated sequences by a largely homogeneous evolutionary process in which data are identically distributed. There is ample evidence, however, that real-world gene sequences evolve heterogeneously and are not identically distributed. Here we show that maximum likelihood and BMCMC can become strongly biased and statistically inconsistent when the rates at which sequence sites evolve change non-identically over time. Maximum parsimony performs substantially better than current parametric methods over a wide range of conditions tested, including moderate heterogeneity and phylogenetic problems not normally considered difficult. All inferences in comparative biology depend on accurate estimates of evolutionary relationships. Recent phylogenetic analyses have turned away from maximum parsimony towards the probabilistic techniques of maximum likelihood and bayesian Markov chain Monte Carlo (BMCMC). These probabilistic techniques represent a parametric approach to statistical phylogenetics, because their criterion for evaluating a topology--the probability of the data, given the tree--is calculated with reference to an explicit evolutionary model from which the data are assumed to be identically distributed. Maximum parsimony can be considered nonparametric, because trees are evaluated on the basis of a general metric--the minimum number of character state changes required to generate the data on a given tree--without assuming a specific distribution. The shift to parametric methods was spurred, in large part, by studies showing that although both approaches perform well most of the time, maximum parsimony is strongly biased towards recovering an incorrect tree under certain combinations of branch lengths, whereas maximum likelihood is not. All these evaluations simulated sequences by a largely homogeneous evolutionary process in which data are identically distributed. There is ample evidence, however, that real-world gene sequences evolve heterogeneously and are not identically distributed. Here we show that maximum likelihood and BMCMC can become strongly biased and statistically inconsistent when the rates at which sequence sites evolve change non-identically over time. Maximum parsimony performs substantially better than current parametric methods over a wide range of conditions tested, including moderate heterogeneity and phylogenetic problems not normally considered difficult.All inferences in comparative biology depend on accurate estimates of evolutionary relationships. Recent phylogenetic analyses have turned away from maximum parsimony towards the probabilistic techniques of maximum likelihood and bayesian Markov chain Monte Carlo (BMCMC). These probabilistic techniques represent a parametric approach to statistical phylogenetics, because their criterion for evaluating a topology--the probability of the data, given the tree--is calculated with reference to an explicit evolutionary model from which the data are assumed to be identically distributed. Maximum parsimony can be considered nonparametric, because trees are evaluated on the basis of a general metric--the minimum number of character state changes required to generate the data on a given tree--without assuming a specific distribution. The shift to parametric methods was spurred, in large part, by studies showing that although both approaches perform well most of the time, maximum parsimony is strongly biased towards recovering an incorrect tree under certain combinations of branch lengths, whereas maximum likelihood is not. All these evaluations simulated sequences by a largely homogeneous evolutionary process in which data are identically distributed. There is ample evidence, however, that real-world gene sequences evolve heterogeneously and are not identically distributed. Here we show that maximum likelihood and BMCMC can become strongly biased and statistically inconsistent when the rates at which sequence sites evolve change non-identically over time. Maximum parsimony performs substantially better than current parametric methods over a wide range of conditions tested, including moderate heterogeneity and phylogenetic problems not normally considered difficult. All inferences in comparative biology depend on accurate estimates of evolutionary relationships. Recent phylogenetic analyses have turned away from maximum parsimony towards the probabilistic techniques of maximum likelihood and bayesian Markov chain Monte Carlo (BMCMC). These probabilistic techniques represent a parametric approach to statistical phylogenetics, because their criterion for evaluating a topology--the probability of the data, given the tree--is calculated with reference to an explicit evolutionary model from which the data are assumed to be identically distributed. Maximum parsimony can be considered nonparametric, because trees are evaluated on the basis of a general metric--the minimum number of character state changes required to generate the data on a given tree--without assuming a specific distribution. The shift to parametric methods was spurred, in large part, by studies showing that although both approaches perform well most of the time, maximum parsimony is strongly biased towards recovering an incorrect tree under certain combinations of branch lengths, whereas maximum likelihood is not. All these evaluations simulated sequences by a largely homogeneous evolutionary process in which data are identically distributed. There is ample evidence, however, that real-world gene sequences evolve heterogeneously and are not identically distributed. Here we show that maximum likelihood and BMCMC can become strongly biased and statistically inconsistent when the rates at which sequence sites evolve change non-identically over time. Maximum parsimony performs substantially better than current parametric methods over a wide range of conditions tested, including moderate heterogeneity and phylogenetic problems not normally considered difficult. [PUBLICATION ABSTRACT] All inferences in comparative biology depend on accurate estimates of evolutionary relationships. Recent phylogenetic analyses have turned away from maximum parsimony towards the probabilistic techniques of maximum likelihood and bayesian Markov chain Monte Carlo (BMCMC). These probabilistic techniques represent a parametric approach to statistical phylogenetics, because their criterion for evaluating a topology—the probability of the data, given the tree—is calculated with reference to an explicit evolutionary model from which the data are assumed to be identically distributed. Maximum parsimony can be considered nonparametric, because trees are evaluated on the basis of a general metric—the minimum number of character state changes required to generate the data on a given tree—without assuming a specific distribution 1 . The shift to parametric methods was spurred, in large part, by studies showing that although both approaches perform well most of the time 2 , maximum parsimony is strongly biased towards recovering an incorrect tree under certain combinations of branch lengths, whereas maximum likelihood is not 3 , 4 , 5 , 6 . All these evaluations simulated sequences by a largely homogeneous evolutionary process in which data are identically distributed. There is ample evidence, however, that real-world gene sequences evolve heterogeneously and are not identically distributed 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 . Here we show that maximum likelihood and BMCMC can become strongly biased and statistically inconsistent when the rates at which sequence sites evolve change non-identically over time. Maximum parsimony performs substantially better than current parametric methods over a wide range of conditions tested, including moderate heterogeneity and phylogenetic problems not normally considered difficult. |
| Audience | Academic |
| Author | Kolaczkowski, Bryan Thornton, Joseph W. |
| Author_xml | – sequence: 1 givenname: Bryan surname: Kolaczkowski fullname: Kolaczkowski, Bryan organization: Department of Computer and Information Science – sequence: 2 givenname: Joseph W. surname: Thornton fullname: Thornton, Joseph W. email: joet@uoregon.edu organization: Center for Ecology and Evolutionary Biology, University of Oregon |
| BackLink | http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=16206650$$DView record in Pascal Francis https://www.ncbi.nlm.nih.gov/pubmed/15496922$$D View this record in MEDLINE/PubMed |
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| CODEN | NATUAS |
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| Cites_doi | 10.1006/jmbi.1998.2601 10.1080/106351598261030 10.1093/oxfordjournals.molbev.a003973 10.1007/BF02459467 10.1016/0025-5564(95)00172-7 10.1007/BF01738880 10.1093/oxfordjournals.molbev.a026025 10.2307/2412923 10.1093/bioinformatics/14.9.817 10.1098/rspb.2002.2025 10.1093/oxfordjournals.molbev.a004128 10.1080/106351501753328848 10.1080/106351500750049860 10.1038/nature02053 10.1093/bioinformatics/17.8.754 10.1093/oxfordjournals.molbev.a040183 10.1093/oxfordjournals.molbev.a025613 10.1016/S0968-0004(01)01877-1 10.1080/106351598260987 10.1006/mpev.2001.1045 10.1080/01621459.1968.11009285 10.1126/science.8171318 10.1080/10635150117959 10.1007/s00239-002-2341-1 10.1093/molbev/msh130 |
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| References | P Lopez (BFnature02917_CR9) 2002; 19 WM Fitch (BFnature02917_CR10) 1976; 8 JP Huelsenbeck (BFnature02917_CR5) 1998; 47 DM Hillis (BFnature02917_CR19) 1998; 47 J Sullivan (BFnature02917_CR18) 2001; 50 MJ Sanderson (BFnature02917_CR1) 2000; 49 DL Swofford (BFnature02917_CR26) 1998 MK Kuhner (BFnature02917_CR4) 1994; 11 DM Hillis (BFnature02917_CR2) 1994; 264 J Felsenstein (BFnature02917_CR3) 1978; 27 PJ Lockhart (BFnature02917_CR14) 1998; 15 TS Donaldson (BFnature02917_CR17) 1968; 63 Z Yang (BFnature02917_CR27) 1997; 13 BS Gaut (BFnature02917_CR6) 1995; 12 B Misof (BFnature02917_CR15) 2002; 55 JP Huelsenbeck (BFnature02917_CR28) 2001; 17 H Philippe (BFnature02917_CR16) 2001; 26 MM Miyamoto (BFnature02917_CR8) 1995; 12 D Posada (BFnature02917_CR29) 1998; 14 DD Pollock (BFnature02917_CR11) 1999; 287 JT Chang (BFnature02917_CR20) 1996; 134 A Rokas (BFnature02917_CR21) 2003; 425 CA Russo (BFnature02917_CR22) 1996; 13 Y Inagaki (BFnature02917_CR13) 2004; 21 DL Swofford (BFnature02917_CR30) 2001; 50 GJ Naylor (BFnature02917_CR24) 1998; 47 C Tuffley (BFnature02917_CR25) 1997; 59 C Delarbre (BFnature02917_CR23) 2002; 22 T Pupko (BFnature02917_CR12) 2002; 269 JP Huelsenbeck (BFnature02917_CR7) 2002; 19 |
| References_xml | – volume: 287 start-page: 187 year: 1999 ident: BFnature02917_CR11 publication-title: J. Mol. Biol. doi: 10.1006/jmbi.1998.2601 – volume: 47 start-page: 61 year: 1998 ident: BFnature02917_CR24 publication-title: Syst. Biol. doi: 10.1080/106351598261030 – volume: 47 start-page: 519 year: 1998 ident: BFnature02917_CR5 publication-title: Syst. Biol. – volume: 19 start-page: 1 year: 2002 ident: BFnature02917_CR9 publication-title: Mol. Biol. Evol. doi: 10.1093/oxfordjournals.molbev.a003973 – volume: 59 start-page: 581 year: 1997 ident: BFnature02917_CR25 publication-title: Bull. Math. Biol. doi: 10.1007/BF02459467 – volume: 13 start-page: 555 year: 1997 ident: BFnature02917_CR27 publication-title: Comput. Appl. Biosci. – volume: 134 start-page: 189 year: 1996 ident: BFnature02917_CR20 publication-title: Math. Biosci. doi: 10.1016/0025-5564(95)00172-7 – volume: 8 start-page: 13 year: 1976 ident: BFnature02917_CR10 publication-title: J. Mol. Evol. doi: 10.1007/BF01738880 – volume: 15 start-page: 1183 year: 1998 ident: BFnature02917_CR14 publication-title: Mol. Biol. Evol. doi: 10.1093/oxfordjournals.molbev.a026025 – volume: 27 start-page: 401 year: 1978 ident: BFnature02917_CR3 publication-title: Syst. Zool. doi: 10.2307/2412923 – volume: 14 start-page: 817 year: 1998 ident: BFnature02917_CR29 publication-title: Bioinformatics doi: 10.1093/bioinformatics/14.9.817 – volume: 269 start-page: 1313 year: 2002 ident: BFnature02917_CR12 publication-title: Proc. R. Soc. Lond. B doi: 10.1098/rspb.2002.2025 – volume: 19 start-page: 698 year: 2002 ident: BFnature02917_CR7 publication-title: Mol. Biol. Evol. doi: 10.1093/oxfordjournals.molbev.a004128 – volume: 50 start-page: 723 year: 2001 ident: BFnature02917_CR18 publication-title: Syst. Biol. doi: 10.1080/106351501753328848 – volume: 49 start-page: 817 year: 2000 ident: BFnature02917_CR1 publication-title: Syst. Biol. doi: 10.1080/106351500750049860 – volume: 425 start-page: 798 year: 2003 ident: BFnature02917_CR21 publication-title: Nature doi: 10.1038/nature02053 – volume-title: PAUP*: Phylogenetic Analysis Using Parsimony and Other Methods, v.4.0b10 year: 1998 ident: BFnature02917_CR26 – volume: 17 start-page: 754 year: 2001 ident: BFnature02917_CR28 publication-title: Bioinformatics doi: 10.1093/bioinformatics/17.8.754 – volume: 12 start-page: 152 year: 1995 ident: BFnature02917_CR6 publication-title: Mol. Biol. Evol. doi: 10.1093/oxfordjournals.molbev.a040183 – volume: 13 start-page: 525 year: 1996 ident: BFnature02917_CR22 publication-title: Mol. Biol. Evol. doi: 10.1093/oxfordjournals.molbev.a025613 – volume: 12 start-page: 503 year: 1995 ident: BFnature02917_CR8 publication-title: Mol. Biol. Evol. – volume: 26 start-page: 414 year: 2001 ident: BFnature02917_CR16 publication-title: Trends Biochem. Sci. doi: 10.1016/S0968-0004(01)01877-1 – volume: 47 start-page: 3 year: 1998 ident: BFnature02917_CR19 publication-title: Syst. Biol. doi: 10.1080/106351598260987 – volume: 22 start-page: 184 year: 2002 ident: BFnature02917_CR23 publication-title: Mol. Phylogenet. Evol. doi: 10.1006/mpev.2001.1045 – volume: 63 start-page: 660 year: 1968 ident: BFnature02917_CR17 publication-title: J. Am. Stat. Assoc. doi: 10.1080/01621459.1968.11009285 – volume: 264 start-page: 671 year: 1994 ident: BFnature02917_CR2 publication-title: Science doi: 10.1126/science.8171318 – volume: 50 start-page: 525 year: 2001 ident: BFnature02917_CR30 publication-title: Syst. Biol. doi: 10.1080/10635150117959 – volume: 55 start-page: 460 year: 2002 ident: BFnature02917_CR15 publication-title: J. Mol. Evol. doi: 10.1007/s00239-002-2341-1 – volume: 21 start-page: 1340 year: 2004 ident: BFnature02917_CR13 publication-title: Mol. Biol. Evol. doi: 10.1093/molbev/msh130 – volume: 11 start-page: 459 year: 1994 ident: BFnature02917_CR4 publication-title: Mol. Biol. Evol. |
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| Snippet | All inferences in comparative biology depend on accurate estimates of evolutionary relationships. Recent phylogenetic analyses have turned away from maximum... |
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| SubjectTerms | Bayes Theorem Bias Biological and medical sciences Biological evolution Biology Computer Simulation Evolution Fundamental and applied biological sciences. Psychology Genetics Genetics of eukaryotes. Biological and molecular evolution Heterogeneity Humanities and Social Sciences letter Likelihood Functions Markov Chains Monte Carlo Method multidisciplinary Phylogeny Research Design Science Science (multidisciplinary) Sensitivity and Specificity Taxonomy Topology |
| Title | Performance of maximum parsimony and likelihood phylogenetics when evolution is heterogeneous |
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