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Big Data and Outlier Loci: A Cautionary Tale with Genome-Scale Phylogenetic Data
- 1. Big data and outlier loci: A cautionary tale with genome-scale phylogenetic data Lyndon M. Coghill1,Vinson Doyle1, Van Wishingrad2,Robert C. Thomson2 & JeremyM. Brown1 1.0 1.0?
- 2. Genome-scale Data Use Increasing for Phylogenetics 0 5000 10000 15000 20000 25000 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 PublishedGenomic-ScalePhylogenies Year Background Identifying Outlier Genes What’s driving outliers Take Home
- 3. 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. Background Identifying Outlier Genes What’s driving outliers Take Home ? Lab Magic Pipeline.canned()
- 4. An Example (Turtle Placement) Background Identifying Outlier Genes What’s driving outliers Take Home
- 5. 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 placement Background Identifying Outlier Genes What’s driving outliers Take Home ?
- 6. 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 placement Background Identifying Outlier Genes What’s driving outliers Take Home ?
- 7. Bayes Factors as branch specific support • Alternative measure of support for topological relationships. • Ratio of marginal likelihoods between two hypotheses. 𝑩𝒂𝒚𝒆𝒔 𝑭 𝒂𝒄𝒕𝒐𝒓 = 𝑷 𝑫𝒂𝒕𝒂 𝑯 𝒚𝒑𝒐𝒕𝒉𝒆𝒔𝒊𝒔 𝟏) 𝑷 𝑫𝒂𝒕𝒂 𝑯 𝒚𝒑𝒐𝒕𝒉𝒆𝒔𝒊𝒔 𝟐) Background Identifying Outlier Genes What’s driving outliers Take Home H1: Bi-‐partition is present H2: Bi-‐partition is absent:
- 8. • 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 Sister PlacementAll Other Placements Background Identifying Outlier Genes What’s driving outliers Take Home
- 9. Background Identifying Outlier Genes What’s driving outliers Take Home Bayes Factors Support for Turtle Placement ChiariCrawfordFong ShafferLuWang
- 10. Bayes Factors Support for Turtle Placement Background Identifying Outlier Genes What’s driving outliers Take Home Low number of genes with strong support ChiariCrawfordFong ShafferLuWang
- 11. Background Identifying Outlier Genes What’s driving outliers Take Home What genes support croc sister placement • Comparison of posterior probabilities to 2ln(BF) values for croc and turtle monophyly. • 248 genes from Chiari dataset.
- 12. • Comparison of posterior probabilities to 2ln(BF) values for croc and turtle monophyly. • 248 genes from Chiari dataset. Background Identifying Outlier Genes What’s driving outliers Take Home What genes support croc sister placement
- 13. Background Identifying Outlier Genes What’s driving outliers Take Home What genes support croc sister placement • Comparison of posterior probabilities to 2ln(BF) values for croc and turtle monophyly. • 248 genes from Chiari dataset.
- 14. Background Identifying Outlier Genes What’s driving outliers Take Home What genes support croc sister placement • Comparison of posterior probabilities to 2ln(BF) values for croc sister placement. • 248 genes from Chiari dataset.
- 15. • 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 Background Identifying Outlier Genes What’s driving outliers Take Home Testing the effect of outliers Wang Dataset Chiari Dataset
- 16. All Genes Top 1% of BF outlier genes removed Background Identifying Outlier Genes What’s driving outliers Take Home 1.0 1.0 Effect of outlier genes on topology Brown et al. Sys. Bio. In Review.
- 17. • Paralogy • Systematic Error What’s driving the outliers? A A B B Duplication Event Background Identifying Outlier Genes What’s driving outliers Take Home
- 18. 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 Background Identifying Outlier Genes What’s driving outliers Take Home
- 19. Evidence of Paralogy Background Identifying Outlier Genes What’s driving outliers Take Home
- 20. • Paralogy • Systematic Error • Model Fit Coming Attractions Systematic Error Random Error Background Identifying Outlier Genes What’s driving outliers Take Home
- 21. Bayesian Posterior Prediction I. Drawing trees and parameters from posterior distribution Background Identifying Outlier Genes What’s driving outliers Take Home I
- 22. II Bayesian Posterior Prediction I. Drawing trees and parameters from posterior distribution II. Use that data to simulate new data sets Background Identifying Outlier Genes What’s driving outliers Take Home I
- 23. II III Bayesian Posterior Prediction I. Drawing trees and parameters from posterior distribution II. Use that data to simulate new data sets III.Summarize each dataset using a test statistic Background Identifying Outlier Genes What’s driving outliers Take Home I
- 24. II III IV Bayesian Posterior Prediction I. Drawing trees and parameters from posterior distribution II. Use that data to simulate new data sets III.Summarize each dataset using a test statistic IV.Compare empirical test statistic value to simulated distribution Background Identifying Outlier Genes What’s driving outliers Take Home I
- 25. 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. Background Identifying Outlier Genes What’s driving outliers Take Home
- 26. Acknowledgements Brown Lab Guifang Zhou Genevieve Mount David Morris DEB-1355071 DEB-1354506 DBI-1356796