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Detecting Genomic Adaptive Convergence: Insights, Results, and Perspectives

This presentation by Joe Parker from Queen Mary University of London discusses the complex landscape of adaptive molecular convergence in genomics. It explores methods for detecting adaptive convergence across diverse datasets, highlighting challenges related to sampling and interpretation. Key topics include the definition of molecular convergence, empirical data on echolocating mammals, and the nuances of detecting adaptive homoplasies. The discussion emphasizes future directions, methodological advancements, and the pervasive role of convergence in evolutionary biology.

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Detecting Genomic Adaptive Convergence: Insights, Results, and Perspectives

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  1. Convergence for everyone?Detecting disparate signals of genomic adaptive convergence in several different datasets:Initial results, lessons & perspectives 16th September 2014 Joe Parker, Queen Mary University London

  2. Adaptive molecular convergence • Definition • Methods to detect • Datasets and results • Sampling issues • Interpretational issues • Future

  3. Defining molecular convergence • Surprisingly hard • It isn’t: • Divergence (adaptive or neutral) • Conservation or purifying selection • Retention of ancestral states with secondary changes in outgroups • ‘Neutral’ homoplasy • It ought to be: • ‘Adaptive’ homoplasies • ‘Excess’ homoplasies

  4. Observations • ‘Adaptive convergence’ predicated on assumption adaptive sites ‘count’ more - conditions for detection? • Can parallel changes with dN/dS ~ 1 be ‘convergent’ • ‘Excess’ - a problematic definition without a good null model • Odds-ratio based? • Empirical CDF? • Eyeball…

  5. Methods • Species phylogeny and inputs • Selection detection • Site-based methods • Tree-based methods

  6. Site-based methods • Look at tips • Sample balance?

  7. Site-based methods • Look at tips • Reconstruct ancestral changes

  8. Site-based methods • Look at tips • Reconstruct ancestral changes • Pairwise P(conv) ~ P(div) changes

  9. Site-based methods • Look at tips • Reconstruct ancestral changes • Pairwise P(conv) ~ P(div) changes • BEB posterior probabilities

  10. Tree-based methods • de novo tree search • Inference error • Signal : noise • Multiple phylogenies

  11. Tree-based methods • ∆SSLS (likelhood comparison) • Which hypotheses? • Multiple simultaneous comparisons? • Models insensitive • How extreme

  12. Tree-based methods • Unrestricted / ennumerated phylogeny fitting

  13. Trees and sites methods • dN/dS and ∆SSLS correlation

  14. Trees and sites methods • Random control tree correction

  15. Genomic approaches • Pool information across sites • Are loci comparable • Error rates? • Orthology, paralogy

  16. Sampling • Sampling balance • Within locus • Between loci • Gene selection / ascertainment bias • Networks • Tree selection • a priori phylogenies

  17. Convergence in echolocating mammals • 22 mammals, 2,326 loci • Convergence signals across genome

  18. Convergence in echolocating bats • The • The

  19. Convergence in mole-rats • The • The

  20. Interpretational issues • Relative measure • Strength of evidence • Null model? Sampling? Which phylogenies

  21. Interpretation • Notional convergence detected genomically, or not at all • Selection, incongruence • Which trees?

  22. Which Trees? • Choice of hypothesis, subtly different from usual practice • If we accept tree space distance important… • … Hypotheses are parameters • Ennumerate over trees?

  23. Future directions • Null model • Alternative (convergent) model • Tree space distance

  24. Conclusion • Strong evidence molecular convergence, or something like our best definition of it, is a pervasive force • Very early work; contrast with e.g. early dN/dS tests

  25. Acknowlegements • Colleagues • Institutions • Funding

  26. Further information • Reading • Li, Liu, Castoe, Parker • Resources • SVN, site, j.d.parker@qmul.ac.uk

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