big data and outlier loci: a cautionary tale with genome-scale phylogenetic data

Big data and outlier loci: A cautionary tale with genome-scale phylogenetic dataLyndon M. Coghill1, Vinson Doyle1, Van Wishingrad2, Robert C. Thomson2 & Jeremy M. Brown1

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Genome-scale Data Use Increasing for Phylogenetics

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Background Identifying Outlier Genes What’s driving outliers Take Home

Large datasets are desirable but…

• Process can be complicated.• Different data generation

methods, produce different results.• How this process affects the

quality of these datasets is poorly understood.


?Lab

Magic

Pipeline.canned()

An Example (Turtle Placement)Background Identifying Outlier Genes What’s driving outliers Take Home

1. Chiari et al.2. Fong et al.3. Wang et al.4. Crawford et al. 5. Lu et al.6. Shaffer et al.

All supported archosaur sister placementBackground Identifying Outlier Genes What’s driving outliers Take Home

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Bayes Factors as branch specific support

• Alternative measure of support for topological relationships.• Ratio of marginal

likelihoods between two hypotheses.

𝑩𝒂𝒚𝒆𝒔 𝑭𝒂𝒄𝒕𝒐𝒓 = 𝑷 𝑫𝒂𝒕𝒂 𝑯𝒚𝒑𝒐𝒕𝒉𝒆𝒔𝒊𝒔𝟏)𝑷 𝑫𝒂𝒕𝒂 𝑯𝒚𝒑𝒐𝒕𝒉𝒆𝒔𝒊𝒔𝟐)


H1: Bi-‐partition is present H2: Bi-‐partition is absent:

• Calculated 2 marginal likelihoods to

examine turtle placement.

• 1: Constrained turtle placement to a

single position in the tree.

• 2. Considered all other hypothesized

positions for turtles.

Bayes Factors (Turtle Placement)

Archosaur S

ister Placement

All O

ther Placements



Bayes Factors Support for Turtle Placement

Chiari

Craw

ford

Fong

Shaffer

LuWang

Bayes Factors Support for Turtle PlacementBackground Identifying Outlier Genes What’s driving outliers Take Home

Low number of genes with strong support

Chiari

Craw

ford

Fong

Shaffer

LuWang


What genes support croc sister placement

• Comparison of posterior probabilities

to 2ln(BF) values for croc and turtle

monophyly.

• 248 genes from Chiari dataset.



monophyly.







to 2ln(BF) values for croc sister

placement.


• Examine most extreme outlier genes supporting croc sister placement.• ~ 1% of genes were outliers

with strong support.•What is their effect on

inference…?

15 / 1113 genes

2 / 248 genes


Testing the effect of outliers

Wang Dataset

Chiari Dataset

All Genes Top 1% of BF outlier genes removed


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Effect of outlier genes on topology

Brown et al. Sys. Bio. In Review.

• Paralogy• Systematic Error

What’s driving the outliers? A A B B

Duplication Event


Evidence of Paralogy• BLAST genes against closest

genome.• Pull hits > 70% (~ 2 – 3)• Hits non-contiguous.• Concatenate hits.• Infer new tree..

+

Original Sequence

Hit 1 Hit 2 Hit 3

Hit Contig


Evidence of Paralogy


• Paralogy• Systematic Error• Model Fit

Coming AttractionsSystematic Error

Random Error


Bayesian Posterior Prediction

I. Drawing trees and parameters from posterior distribution


I

IIBayesian Posterior Prediction


II. Use that data to simulate new data sets


I

II IIIBayesian Posterior Prediction



III.Summarize each dataset using a test statistic


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II III

IV

Bayesian Posterior Prediction



III.Summarize each dataset using a test statistic

IV.Compare empirical test statistic value to simulated distribution


I

Take Home• Support can be misleading when using genomic-scale data.• Standard support values hide a lot of variation in underlying data. • Some loci have outlying extreme support values.• Caution: • Outlier loci included in joint analyses can have huge influence.• Small differences in analytical choices can have huge influence on results.

• Using Bayes Factors as a measure of support can help identify some of this hidden variation.


Acknowledgements

Brown LabGuifang Zhou

Genevieve MountDavid Morris

DEB-1355071DEB-1354506DBI-1356796

big data and outlier loci: a cautionary tale with genome-scale phylogenetic data

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