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Correlating traits with phylogenies

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    1. Correlating traits with phylogenies Using BaTS

    2. Key point: students should understand Phylogenies as hypotheses about ancestry Discrete nature of character traits Possible traits, e.g. geographic, tissue-based, molecular characterization, etc. Associations must be significant given potential for Type I error (especially by eye)Key point: students should understand Phylogenies as hypotheses about ancestry Discrete nature of character traits Possible traits, e.g. geographic, tissue-based, molecular characterization, etc. Associations must be significant given potential for Type I error (especially by eye)

    3. Phylogeny and trait values A phylogeny describes a hypothesis about the evolutionary relationship between individuals sampled from a population Key point: students should understand Phylogenies as hypotheses about ancestryKey point: students should understand Phylogenies as hypotheses about ancestry

    4. Phylogeny and trait values Discrete character traits of interest can be mapped onto the phylogeny Key point: students should understand Discrete nature of character traits Possible traits, e.g. geographic, tissue-based, molecular characterization, etc.Key point: students should understand Discrete nature of character traits Possible traits, e.g. geographic, tissue-based, molecular characterization, etc.

    5. Phylogeny and trait values A significant association between a particular trait value and its distribution on a phylogeny indicates a potential causative relationship Key point: students should understand Associations must be significant given potential for Type I error (especially by eyeKey point: students should understand Associations must be significant given potential for Type I error (especially by eye

    6. Phylogeny and trait values Often, the phylogeny-trait relationship does not appear unequivocal by eye: an analytical framework may be needed. Key point: students should understand Associations must be significant given potential for Type I error (especially by eye Key point: review Key point: null hypothesisKey point: students should understand Associations must be significant given potential for Type I error (especially by eye Key point: review Key point: null hypothesis

    7. 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 Key point: null hypothesis Key point: null hypothesis

    8. 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): 11343-11352. **Parker, Rambaut & Pybus (2008) MEEGID 8(3):239-246. For illustration only no interpretation at this pointFor illustration only no interpretation at this point

    9. Available methods Non-phylogenetic: ANOVA Ignores shared ancestry Phylogenetic: Single tree mapping Slatkin-Maddison & AI BaTS Overview of available methods, including BaTS.Overview of available methods, including BaTS.

    10. 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 Discuss: What is involved? Have the students encountered any of these methods before? What are the pros and cons?Discuss: What is involved? Have the students encountered any of these methods before? What are the pros and cons?

    11. 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 Discuss: What is involved? Have the students encountered any of these methods before? What are the pros and cons? Discuss: What is involved? Have the students encountered any of these methods before? What are the pros and cons?

    12. 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 Discuss: What is involved? Have the students encountered any of these methods before? What are the pros and cons? Key point: Students should understand: BaTS calculates proper null distributions to calculate significance. BaTS statistics have been tested and Type I error found to be correct, as well as powerful. Because BaTS is a Bayesian MCMC approach, it corrects for phylogenetic uncertainty. That is, all different phylogenies are included in the posterior set in proportion to their support in the data bootstrapping techniques only test a single phylogeny. Discuss: What is involved? Have the students encountered any of these methods before? What are the pros and cons? Key point: Students should understand: BaTS calculates proper null distributions to calculate significance. BaTS statistics have been tested and Type I error found to be correct, as well as powerful. Because BaTS is a Bayesian MCMC approach, it corrects for phylogenetic uncertainty. That is, all different phylogenies are included in the posterior set in proportion to their support in the data bootstrapping techniques only test a single phylogeny.

    13. 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 Key point: exact working and philosophy not important at this stage. See Parker et al. (2008) for details. Key point: Students should understand: A Bayesian MCMC analysis is a prerequisite step BaTS uses the posterior distribution of phylogenies as input Null (expected) and observed distributions are compared. Observed values are less important than significance. Key point: exact working and philosophy not important at this stage. See Parker et al. (2008) for details. Key point: Students should understand: A Bayesian MCMC analysis is a prerequisite step BaTS uses the posterior distribution of phylogenies as input Null (expected) and observed distributions are compared. Observed values are less important than significance.

    14. 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

    15. Workflow: Preparation (i) Sequence alignment: CLUSTAL, BioEdit, SE-Al Bayesian MCMC analysis: MRBAYES, BEAST Taxa marked-up with traits Students may have their own preferred third-party software: not relevant to BaTS how the analysis proceeds up to the bMCMC stage.Students may have their own preferred third-party software: not relevant to BaTS how the analysis proceeds up to the bMCMC stage.

    16. Workflow: Preparation (ii) Taxa marked-up with traits: Typical NEXUS format: Illustration of typical #NEXUS format. Check students are familiar and comfortable with the basic #NEXUS DOM / data structure before moving on.Illustration of typical #NEXUS format. Check students are familiar and comfortable with the basic #NEXUS DOM / data structure before moving on.

    17. Workflow: Preparation (iii) Taxa marked-up with traits: The BaTS markup. Load dataset example.trees to inspect. Students should: Be competent and confident in: opening a #nexus file in a text editor, editing by hand for BaTS, and saving as a new BaTS input file. Understand how BaTS input parser differs. Pay attention to whitespace, capitalization and trait labelling Bear in mind that mis-typed input traits will be parsed as another discrete state. The BaTS markup. Load dataset example.trees to inspect. Students should: Be competent and confident in: opening a #nexus file in a text editor, editing by hand for BaTS, and saving as a new BaTS input file. Understand how BaTS input parser differs. Pay attention to whitespace, capitalization and trait labelling Bear in mind that mis-typed input traits will be parsed as another discrete state.

    18. Workflow: BaTS analysis To use BaTS from the command-line, type: java jar BaTS_beta_build2.jar [single|batch] <treefile_name> <reps> <states> Where: single or batch asks BaTS to analyse either a single input file, or a whole directory (batch analysis) <treefile_name> is the name and full location of the treefile or directory to be analysed, <reps> is the number (an integer > 1, typically 100 at least) of state randomizations to perform to yield a null distribution, and <states> is the number of different states seen. Run example.treesRun example.trees

    19. C:\joeWork\apps\BaTS\BaTS_beta_build2\BaTS_beta_build2>java -jar BaTS_beta_build 2.jar single example.trees 100 7 Performing single analysis. File: example.trees Null replicates: 100 Maximum number of discrete character states: 7 analysing... 30 trees, with 7 states analysing observed (using obs state data) 30 29 30 29 30 29 30 29 Statistic observed mean lower 95% CI upper 95% CU null mean lower 95% CI upper 95% CI significance AI 1.5555052757263184 1.1128820180892944 2.160351037979126 12.03488540649414 11.475320040039 12.6391201928711 0.0 PS 18.5 17.0 20.0 80.7713394165039 77.86666870117188 83.56666564941406 0.0 MC (state 0) 12.633333206176758 9.0 16.0 1.7496669292449951 1.399999976158142 2.1666667461395264 0.009999990463256836 MC (state 1) 19.0 19.0 19.0 1.7480005025863647 1.33333337306976 32 2.0999999046325684 0.009999990463256836 MC (state 2) 12.666666984558105 12.0 13.0 1.77991247559 1.33333697632 2.200000047683716 0.009999990463256836 MC (state 3) 8.566666603088379 3.0 11.0 1.66733866943 1.2333333492279053 2.133333444595337 0.009999990463256836 MC (state 4) 11.0 11.0 11.0 1.5526663064956665 1.16666662693023 68 2.0999999046325684 0.009999990463256836 MC (state 5) 3.433333396911621 2.0 6.0 1.4840000867843628 1.100000023841858 2.0333333015441895 0.009999990463256836 MC (state 6) 5.066666603088379 5.0 6.0 1.2973339557647705 1.0333333015441895 1.600000023841858 0.009999990463256836 done Done. The analysis

    20. 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

    21. 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

    22. 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: http://edocs.bea.com/wls/docs70/perform/JVMTuning.html