Transcript Correlating traits with phylogenies
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) CNS tissues * : Dataset of HIV sequences sampled from
post mortem
Analysis by Slatkin-Maddison (1989) method, reanalyzed in BaTS ** .
Compartmentalization by tissue type: circulating viral populations defined by location in the body: Statistic AI PS Frontal lobe Occipital lobe Meninges Lymph nodes Temporal lobe Spinal cord * Salemi
et al.
(2005) J. Virol 79 (17): 11343-11352.
** Parker, Rambaut & Pybus (2008) MEEGID 8 (3):239 246.
p-value (BaTS)
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<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 (i)
Sequence alignment: CLUSTAL, BioEdit, SE-Al Bayesian MCMC analysis: MRBAYES, BEAST Taxa marked-up with traits
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)
The analysis
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 30 trees were detected in the input file analysing... 30 trees, with 7 states (housekeeping and debugging messages) analysing observed (using obs state data) 30 29 30 29 30 29 Output: statstics, one per line, tabulated 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 ‘MC…’ statistics are reported in the
order in which they occur
in the input file
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 association increased phylogeny-trait 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
for more.
is
required .
See java.sun.com
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
use –Xmx can slow the analysis, 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
Author contact:
Joe Parker Department of Zoology Oxford University, UK OX1 3PS [email protected]
http://evolve.zoo.ox.ac.uk