Presentation on theme: "Correlating traits with phylogenies Using BaTS. Phylogeny and trait values A phylogeny describes a hypothesis about the evolutionary relationship between."— Presentation transcript:
Correlating traits with phylogenies Using BaTS
Phylogeny and trait values A phylogeny describes a hypothesis about the evolutionary relationship between individuals sampled from a population Discrete character traits of interest can be mapped onto the phylogeny A significant association between a particular trait value and its distribution on a phylogeny indicates a potential causative relationship
Phylogeny and trait values A phylogeny describes a hypothesis about the evolutionary relationship between individuals sampled from a population
Phylogeny and trait values Discrete character traits of interest can be mapped onto the phylogeny
Phylogeny and trait values A significant association between a particular trait value and its distribution on a phylogeny indicates a potential causative relationship
Phylogeny and trait values Often, the phylogeny-trait relationship does not appear unequivocal by eye: an analytical framework may be needed. (clear association) (no association) ????
Phylogeny and trait values The null hypothesis The null hypothesis under test is one of random phylogeny-trait association; that is, that “No single tip bearing a given character trait is any more likely to share that trait with adjoining taxa than we would expect due to chance”
An example Salemi et al (2005) * : Dataset of HIV sequences sampled from CNS tissues post mortem Analysis by Slatkin-Maddison (1989) method, reanalyzed in BaTS **. Compartmentalization by tissue type: circulating viral populations defined by location in the body: * Salemi et al. (2005) J. Virol 79(17): ** Parker, Rambaut & Pybus (2008) MEEGID 8(3): Statistic p-value (BaTS) AI<0.01 PS<0.01 Frontal lobe<0.01 Occipital lobe<0.01 Meninges<0.01 Lymph nodes<0.01 Temporal lobe<0.01 Spinal cord<0.01
Available methods Non-phylogenetic: ANOVA Ignores shared ancestry Phylogenetic: Single tree mapping Slatkin-Maddison & AI BaTS
Methods: Single-tree mapping Method: Map traits onto a tree Look for correlation Pros: Fast Simple Cons: No indication of significance Statistically weak (high Type II error) Conditional on a single topology
Methods: Slatkin-Maddison & AI Method: Map traits onto a tree by parsimony & count migration events (Slatkin-Maddison) or measure ‘association index’ within clades recursively (AI) Compare observed value with a null (expected) value obtained by bootstrapping Pros: Still reasonably fast Indication of significance Cons: Still conditional on a single topology
Methods: BaTS Method: See below(!) Pros: Indication of significance Statistically powerful and Type I error is correct Accounts for phylogenetic uncertainty Cons: Requires Bayesian MCMC sequence analysis Slower
BaTS: under the bonnet Use a posterior distribution of phylogenies from Bayesian MCMC analysis Calculates migrations, AI and a variety of other measures of association Both observed and expected (null) values’ posterior distributions sampled Significance obtained by comparing observed vs. expected
BaTS: analysis workflow Preparation: Sequence alignment Bayesian MCMC phylogeny reconstruction (BEAST, MrBAYES) to obtain posterior distribution of trees (PST) Taxa in PST marked up with discrete traits BaTS analysis Interpretation
Workflow: Preparation (ii) Taxa marked-up with traits: Typical NEXUS format:
Workflow: Preparation (iii) Taxa marked-up with traits: begin states; a) Declare ‘states’ block b) Assign a trait to each taxon in the order that they appear in the original #NEXUS file c) Close the ‘states’ block. d) Omit ‘translate’ and ‘taxa’ blocks.
Workflow: BaTS analysis To use BaTS from the command-line, type: java –jar BaTS_beta_build2.jar [single|batch] Where: single or batch asks BaTS to analyse either a single input file, or a whole directory (batch analysis) is the name and full location of the treefile or directory to be analysed, is the number (an integer > 1, typically 100 at least) of state randomizations to perform to yield a null distribution, and is the number of different states seen.
C:\joeWork\apps\BaTS\BaTS_beta_build2\BaTS_beta_build2>java -jar BaTS_beta_build 2.jar single example.trees Performing single analysis. File: example.trees Null replicates: 100 Maximum number of discrete character states: 7 analysing trees, with 7 states analysing observed (using obs state data) Statistic observed mean lower 95% CI upper 95% CU null mean lower 95% CI upper 95% CI significance AI PS MC (state 0) MC (state 1) MC (state 2) MC (state 3) MC (state 4) MC (state 5) MC (state 6) done Done. The analysis 30 trees were detected in the input file Output: statstics, one per line, tabulated The ‘MC…’ statistics are reported in the order in which they occur in the input file (housekeeping and debugging messages)
Workflow: Interpretation The null hypothesis The null hypothesis under test is one of random phylogeny-trait association; that is, that “No single tip bearing a given character trait is any more likely to share that trait with adjoining taxa than we would expect due to chance”
Workflow: Interpretation The statistics: Larger values increased phylogeny-trait association Significance indicated by p-value In addition, observed posterior values are informative for some statistics: PS: indicates migration events between trait values MC( trait value ): indicates number of taxon in largest clade monophyletic for that trait value
FAQs / common pitfalls Java 1.5 or higher is required. See java.sun.com for more. Large datasets can be slow, so down-sample input tree files (uniformly, not randomly) where necessary, or to check BaTS input files are marked-up correctly. A RAM (memory) shortage can slow the analysis, use –Xmx switch to allocate virtual RAM* Check input file mark-up carefully if in doubt. *See more:
Author contact: Joe Parker Department of Zoology Oxford University, UK OX1 3PS