Startseite A Variance-Components Model for Distance-Matrix Phylogenetic Reconstruction
Artikel
Lizenziert
Nicht lizenziert Erfordert eine Authentifizierung

A Variance-Components Model for Distance-Matrix Phylogenetic Reconstruction

  • Walter R Gilks , Tom M.W. Nye und Pietro Lio
Veröffentlicht/Copyright: 30. März 2011

Phylogenetic trees describe evolutionary relationships between related organisms (taxa). One approach to estimating phylogenetic trees supposes that a matrix of estimated evolutionary distances between taxa is available. Agglomerative methods have been proposed in which closely related taxon-pairs are successively combined to form ancestral taxa. Several of these computationally efficient agglomerative algorithms involve steps to reduce the variance in estimated distances. We propose an agglomerative phylogenetic method which focuses on statistical modeling of variance components in distance estimates. We consider how these variance components evolve during the agglomerative process. Our method simultaneously produces two topologically identical rooted trees, one tree having branch lengths proportional to elapsed time, and the other having branch lengths proportional to underlying evolutionary divergence. The method models two major sources of variation which have been separately discussed in the literature: noise, reflecting inaccuracies in measuring divergences, and distortion, reflecting randomness in the amounts of divergence in different parts of the tree. The methodology is based on successive hierarchical generalized least-squares regressions. It involves only means, variances and covariances of distance estimates, thereby avoiding full distributional assumptions. Exploitation of the algebraic structure of the estimation leads to an algorithm with computational complexity comparable to the leading published agglomerative methods. A parametric bootstrap procedure allows full uncertainty in the phylogenetic reconstruction to be assessed. Software implementing the methodology may be freely downloaded from StatTree.

References

Brodal, G., R. Faberberg, and P. C.N.S. (2004). Computing the quartet distance between evolutionary trees in time O(nlog2n). Algorithmica 38(2), 377–395.10.1007/s00453-003-1065-ySuche in Google Scholar

Bruno, W., N. Socci, and A. Halpern (2000). Weighted Neighbour Joining: a likelihood-based approach to distance-based phylogeny reconstruction. Molecular Biology and Evolution 17, 189–197.10.1093/oxfordjournals.molbev.a026231Suche in Google Scholar PubMed

Bulmer, M. (1991). Use of the method of generalized least squ ares in reconstructing phylogenies from sequence data. Molecular Biology and Evolution 8, 868–883.Suche in Google Scholar

Chakraborty, R. (1977). Estimation of time of divergence from phylogenetic studies. Canadian Journal of Genetics and Cytology 19, 217–223.10.1139/g77-024Suche in Google Scholar PubMed

Crowder, M. (2001). On repeated measures analysis with misspecified covariance structure. Journal of the Royal Statistical Society, Series B 63, 55–62.10.1111/1467-9868.00275Suche in Google Scholar

Desper, R. and O. Gascuel (2004). Theoretical foundation of the balanced minimum evolution method of phylogenetic inference and its relationship to weighted leastsquares tree fitting. Molecular Biology and Evolution 21(3), 587–598.10.1093/molbev/msh049Suche in Google Scholar PubMed

Felsenstein, J. (1987). Estimation of hominoid phylogeny from a DNA hybridization data set. Journal of Molecular Evolution 26, 123–131.10.1007/BF02111286Suche in Google Scholar PubMed

Felsenstein, J. (2004). Inferring Phylogenies. Massachusetts: Sinauer Associates, Inc.Suche in Google Scholar

Gascuel, O. (1997). BIONJ: an improved version of the NJ algorithm based on a simple model of sequence data. Molecular Biology and Evolution 14, 685–695.10.1093/oxfordjournals.molbev.a025808Suche in Google Scholar PubMed

Gascuel, O. (2000). Data model and classification by trees: the minimum variance reduction (MVR) method. Journal of Classification 17, 67–99.10.1007/s003570000005Suche in Google Scholar

Golub, G. and C. Van Loan (1996). Matrix Computations (3rd ed.). Baltimore: The Johns Hopkins University Press.Suche in Google Scholar

Hasegawa, M., H. Kishino, and T. Yano (1985). Dating the human-ape splitting by a molecular clock of mitochondrial DNA. Journal of Molecular Evolution 22, 160–174.10.1007/BF02101694Suche in Google Scholar PubMed

Jukes, T. and C. Cantor (1969). Evolution of protein molecules. In M.N.Munro (Ed.), Mammalian Protein Metabolism, Volume III, pp. 21–132. New York: Academic Press.10.1016/B978-1-4832-3211-9.50009-7Suche in Google Scholar

Keele, B., E. Giorgi, J. Salazar-Gonzalez, J. Decker, K. Pham, M. Salazar, et al. (2008). Identification and characterization of transmitted and early founder virus envelopes in primary HIV-1 infection. Proc. Natl. Acad. Sci. USA. 105, 75527557.10.1073/pnas.0802203105Suche in Google Scholar

Kimura, M. (1980). A simple model for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences. Journal of Molecular Evolution 16, 111–120.10.1007/BF01731581Suche in Google Scholar

Kishino, H., J. Thorne, and W. Bruno (2001). Performance of a divergence time estimation method under a probabilistic model of rate evolution. Molecular Biology and Evolution 18, 352–361.10.1093/oxfordjournals.molbev.a003811Suche in Google Scholar

Lanave, C., G. Preparata, C. Saccone, and G. Serio (1984). A new method for calculating evolutionary substitution rates. Journal of Molecular Evolution 20, 86–93.10.1007/BF02101990Suche in Google Scholar

Mardia, K., J. Kent, and J. Bibby (1979). Multivariate Analysis. New York: Academic Press.Suche in Google Scholar

Nei, M., J. Stephens, and N. Saitou (1985). Methods for computing the standard errors of branching points in an evolutionary tree and their applications to molecular data from human and apes. Molecular Biology and Evolution 2, 66–85.Suche in Google Scholar

Rambaut, A. and N. Grassly (1997). Seq-gen: an application for the Monte Carlo simulation od DNA sequence evolution along phylogenetic trees. Algorithmica 13(3), 235–238.Suche in Google Scholar

Robinson, D. and L. Foulds (1981). Comparison of phylogenetic trees. Mathematical Bioscience 53, 131–147.10.1016/0025-5564(81)90043-2Suche in Google Scholar

Saitou, N. and M. Nei (1987). The neighbour-joining method: A new method for reconstructing phylogenetic trees. Molecular Biology and Evolution 4, 406–425.Suche in Google Scholar

Salazar-Gonzalez, J., M. Salazar, B. Keele, G. Learn, E. Giorgi, H. Li, et al. (2009). Genetic identity, biological phenotype, and evolutionary pathways of transmitted/founder viruses in acute and early HIV-1 infection. J. Experimental Medecine 206(6), 1273–1289.10.1084/jem.20090378Suche in Google Scholar PubMed PubMed Central

Studier, J. and K. Keppler (1988). A note on the neighbor-joining method of Saitou and Nei. Molecular Biology and Evolution 5, 729–731.Suche in Google Scholar

Susko, E. (2003). Confidence regions and hypothesis tests using generalized least squares. Molecular Biology and Evolution 20, 862–868.10.1093/molbev/msg093Suche in Google Scholar PubMed

Thorne, J., H. Kishino, and I. Painter (1998). Estimating the rate of evolution of the rate of molecular evolution. Molecular Biology and Evolution 15, 1647–1657.10.1093/oxfordjournals.molbev.a025892Suche in Google Scholar PubMed

Wang, L.-S. and T. Warnow (2005). Distance-based genome rearrangement phylogeny. In O. Gascuel (Ed.), Mathematics of Evolution & Phylogeny, Chapter 13, pp. 353–383. Oxford University Press.Suche in Google Scholar

Yang, Z. (1993). Maximum-likelihood estimation of phylogeny from DNA sequences when substitution rates differ over sites. Molecular Biology and Evolution 10, 1396–1401.Suche in Google Scholar

Zwickl, D. and D. Hillis (2002). Increased taxon sampling greatly reduces phylogenetic error. Systematic Biology 51(4), 588–598.10.1080/10635150290102339Suche in Google Scholar PubMed

Published Online: 2011-3-30

©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston

Artikel in diesem Heft

  1. Invited Editorial
  2. Measurement of Evidence and Evidence of Measurement
  3. Article
  4. Fully Moderated T-statistic for Small Sample Size Gene Expression Arrays
  5. Determining Coding CpG Islands by Identifying Regions Significant for Pattern Statistics on Markov Chains
  6. Assessing Modularity Using a Random Matrix Theory Approach
  7. Choice of Summary Statistic Weights in Approximate Bayesian Computation
  8. Genetic Linkage Analysis in the Presence of Germline Mosaicism
  9. Fitting Boolean Networks from Steady State Perturbation Data
  10. Adaptive Elastic-Net Sparse Principal Component Analysis for Pathway Association Testing
  11. Bayesian Learning from Marginal Data in Bionetwork Models
  12. Unsupervised Classification for Tiling Arrays: ChIP-chip and Transcriptome
  13. Multiple Testing in Candidate Gene Situations: A Comparison of Classical, Discrete, and Resampling-Based Procedures
  14. Modeling Read Counts for CNV Detection in Exome Sequencing Data
  15. Multiscale Characterization of Signaling Network Dynamics through Features
  16. A Calibrated Multiclass Extension of AdaBoost
  17. False Discovery Rate Estimation for Stability Selection: Application to Genome-Wide Association Studies
  18. A Markov-Chain Model for the Analysis of High-Resolution Enzymatically 18O-Labeled Mass Spectra
  19. Repeated Measures Semiparametric Regression Using Targeted Maximum Likelihood Methodology with Application to Transcription Factor Activity Discovery
  20. Learning Monotonic Genotype-Phenotype Maps
  21. A Comparison of Multifactor Dimensionality Reduction and L1-Penalized Regression to Identify Gene-Gene Interactions in Genetic Association Studies
  22. Accuracy and Computational Efficiency of a Graphical Modeling Approach to Linkage Disequilibrium Estimation
  23. Learning from Past Treatments and Their Outcome Improves Prediction of In Vivo Response to Anti-HIV Therapy
  24. A Three Component Latent Class Model for Robust Semiparametric Gene Discovery
  25. Log-Linear Modelling of Protein Dipeptide Structure Reveals Interesting Patterns of Side-Chain-Backbone Interactions
  26. A Robust Statistical Method to Detect Null Alleles in Microsatellite and SNP Datasets in Both Panmictic and Inbred Populations
  27. Large Sample Approximations of Probabilities of Correct Evolutionary Tree Estimation and Biases of Maximum Likelihood Estimation
  28. Interval Estimation of Familial Correlations from Pedigrees
  29. Information Metrics in Genetic Epidemiology
  30. Linear Combination Test for Hierarchical Gene Set Analysis
  31. Exploratory Analysis of Multiple Omics Datasets Using the Adjusted RV Coefficient
  32. Application of the Lasso to Expression Quantitative Trait Loci Mapping
  33. A Variance-Components Model for Distance-Matrix Phylogenetic Reconstruction
  34. Imputation Estimators Partially Correct for Model Misspecification
  35. On the Statistical Properties of SGoF Multitesting Method
  36. Meta-Analysis of Family-Based and Case-Control Genetic Association Studies that Use the Same Cases
  37. A Non-Parametric Method for Detecting Specificity Determining Sites in Protein Sequence Alignments
  38. Performance of Matrix Representation with Parsimony for Inferring Species from Gene Trees
  39. Disequilibrium Coefficient: A Bayesian Perspective
  40. Analyzing Time-Course Microarray Data Using Functional Data Analysis - A Review
  41. The NBP Negative Binomial Model for Assessing Differential Gene Expression from RNA-Seq
  42. Inferring Gene Networks using Robust Statistical Techniques
  43. A Two-Stage Poisson Model for Testing RNA-Seq Data
  44. Quantifying the Relative Contribution of the Heterozygous Class to QTL Detection Power
  45. The Joint Null Criterion for Multiple Hypothesis Tests
  46. Multiple Imputation of Missing Phenotype Data for QTL Mapping
  47. Sparse Canonical Covariance Analysis for High-throughput Data
  48. Comparison of Clinical Subgroup aCGH Profiles through Pseudolikelihood Ratio Tests
  49. Random Forests for Genetic Association Studies
  50. Deviance Information Criteria for Model Selection in Approximate Bayesian Computation
  51. High-Dimensional Regression and Variable Selection Using CAR Scores
  52. Surveying the Manifold Divergence of an Entire Protein Class for Statistical Clues to Underlying Biochemical Mechanisms
  53. Smoothing Gene Expression Data with Network Information Improves Consistency of Regulated Genes
  54. Entropy Based Genetic Association Tests and Gene-Gene Interaction Tests
  55. Weighted Lasso with Data Integration
  56. MA-SNP -- A New Genotype Calling Method for Oligonucleotide SNP Arrays Modeling the Batch Effect with a Normal Mixture Model
  57. A Modified Maximum Contrast Method for Unequal Sample Sizes in Pharmacogenomic Studies
Heruntergeladen am 5.11.2025 von https://www.degruyterbrill.com/document/doi/10.2202/1544-6115.1574/html?lang=de
Button zum nach oben scrollen