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Generalizing phylogenetics to infer shared evolutionary events

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Generalizing phylogenetics to
infer shared evolutionary events
Jamie R. Oaks1,2
1Department of Biological Sciences, Auburn...

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Outline
An assumption (i.e., exciting opportunity) in phylogenetics
An approach to the problem
Empirical applications
Curr...

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Current state of phylogenetics
Shared divergences Jamie Oaks – phyletica.org 3/35

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Generalizing phylogenetics to infer shared evolutionary events

  1. 1. Generalizing phylogenetics to infer shared evolutionary events Jamie R. Oaks1,2 1Department of Biological Sciences, Auburn University 2Department of Biology, University of Washington March 31, 2016 c 2007 Boris Kulikov boris-kulikov.blogspot.com Shared divergences Jamie Oaks – phyletica.org 1/35
  2. 2. Outline An assumption (i.e., exciting opportunity) in phylogenetics An approach to the problem Empirical applications Current and future directions Shared divergences Jamie Oaks – phyletica.org 2/35
  3. 3. Current state of phylogenetics Shared divergences Jamie Oaks – phyletica.org 3/35
  4. 4. Current state of phylogenetics Shared ancestry is a fundamental property of life Shared divergences Jamie Oaks – phyletica.org 3/35
  5. 5. Current state of phylogenetics Shared ancestry is a fundamental property of life Phylogenetics is rapidly progressing as the statistical foundation of comparatve biology Shared divergences Jamie Oaks – phyletica.org 3/35
  6. 6. Current state of phylogenetics Shared ancestry is a fundamental property of life Phylogenetics is rapidly progressing as the statistical foundation of comparatve biology “Big data” present exciting possibilities and computational challenges Shared divergences Jamie Oaks – phyletica.org 3/35
  7. 7. Current state of phylogenetics Shared ancestry is a fundamental property of life Phylogenetics is rapidly progressing as the statistical foundation of comparatve biology “Big data” present exciting possibilities and computational challenges Exciting opportunities to develop new ways to study biology in the light of phylogeny Shared divergences Jamie Oaks – phyletica.org 3/35
  8. 8. Current state of phylogenetics Shared divergences Jamie Oaks – phyletica.org 4/35
  9. 9. Current state of phylogenetics Assumption: All processes of diversification affect each lineage independently and only cause bifurcating divergences. Shared divergences Jamie Oaks – phyletica.org 4/35
  10. 10. Current state of phylogenetics Assumption: All processes of diversification affect each lineage independently and only cause bifurcating divergences. We know this assumption is frequently violated Shared divergences Jamie Oaks – phyletica.org 4/35
  11. 11. Violating independent divergences Shared divergences Jamie Oaks – phyletica.org 5/35
  12. 12. Violating independent divergences Shared divergences Jamie Oaks – phyletica.org 5/35
  13. 13. Violating independent divergences Shared divergences Jamie Oaks – phyletica.org 5/35
  14. 14. Violating independent divergences Shared divergences Jamie Oaks – phyletica.org 5/35
  15. 15. Violations are pervasive and interesting Biogeography Environmental changes that affect whole communities of species Shared divergences Jamie Oaks – phyletica.org 6/35
  16. 16. Violations are pervasive and interesting Biogeography Environmental changes that affect whole communities of species Gene family evolution Chromosomal duplications Shared divergences Jamie Oaks – phyletica.org 6/35
  17. 17. Violations are pervasive and interesting Biogeography Environmental changes that affect whole communities of species Gene family evolution Chromosomal duplications Epidemiology Disease spread via co-infected individuals Transmission at social gatherings Shared divergences Jamie Oaks – phyletica.org 6/35
  18. 18. Violations are pervasive and interesting Biogeography Environmental changes that affect whole communities of species Gene family evolution Chromosomal duplications Epidemiology Disease spread via co-infected individuals Transmission at social gatherings Endosymbiont evolution (e.g., parasites, microbiome) Speciation of the host Co-colonization of new host species Shared divergences Jamie Oaks – phyletica.org 6/35
  19. 19. Why account for shared divergences? Shared divergences Jamie Oaks – phyletica.org 7/35
  20. 20. Why account for shared divergences? 1. Improve inference Shared divergences Jamie Oaks – phyletica.org 7/35
  21. 21. Solution: Accommodate shared divergence models Advantage: More data to estimate shared parameters True history τ1τ2τ3 Problem: Current methods only consider general model Consequence: Unnecessary parameters introduce error Current tree model τ1 τ2 τ3τ4 τ5 τ6 τ7 τ8 Shared divergences Jamie Oaks – phyletica.org 8/35
  22. 22. Solution: Accommodate shared divergence models Advantage: More data to estimate shared parameters True history τ1τ2τ3 Problem: Current methods only consider general model Consequence: Unnecessary parameters introduce error Current tree model τ1 τ2 τ3τ4 τ5 τ6 τ7 τ8 Shared divergences Jamie Oaks – phyletica.org 8/35
  23. 23. Solution: Accommodate shared divergence models Advantage: More data to estimate shared parameters True history τ1τ2τ3 Problem: Current methods only consider general model Consequence: Unnecessary parameters introduce error Current tree model τ1 τ2 τ3τ4 τ5 τ6 τ7 τ8 Shared divergences Jamie Oaks – phyletica.org 8/35
  24. 24. Solution: Accommodate shared divergence models Advantage: More data to estimate shared parameters True history τ1τ2τ3 Problem: Current methods only consider general model Consequence: Unnecessary parameters introduce error Current tree model τ1 τ2 τ3τ4 τ5 τ6 τ7 τ8 Shared divergences Jamie Oaks – phyletica.org 8/35
  25. 25. Solution: Accommodate shared divergence models Advantage: More data to estimate shared parameters True history τ1τ2τ3 Problem: Current methods only consider general model Consequence: Unnecessary parameters introduce error Current tree model τ1 τ2 τ3τ4 τ5 τ6 τ7 τ8 Shared divergences Jamie Oaks – phyletica.org 8/35
  26. 26. Solution: Accommodate shared divergence models Advantage: More data to estimate shared parameters True history τ1τ2τ3 Problem: Current methods only consider general model Consequence: Unnecessary parameters introduce error Current tree model τ1 τ2 τ3τ4 τ5 τ6 τ7 τ8 Shared divergences Jamie Oaks – phyletica.org 8/35
  27. 27. Why account for shared divergences? 1. Improve inference Shared divergences Jamie Oaks – phyletica.org 9/35
  28. 28. Why account for shared divergences? 1. Improve inference 2. Provide a framework for studying processes of co-diversification Shared divergences Jamie Oaks – phyletica.org 9/35
  29. 29. Violations are pervasive and interesting Biogeography Environmental changes that affect whole communities of species Gene family evolution Chromosomal duplications Epidemiology Disease spread via co-infected individuals Transmission at social gatherings Endosymbiont evolution (e.g., parasites, microbiome) Speciation of the host Co-colonization of new host species Shared divergences Jamie Oaks – phyletica.org 10/35
  30. 30. Outline An assumption (i.e., exciting opportunity) in phylogenetics An approach to the problem Empirical applications Current and future directions Shared divergences Jamie Oaks – phyletica.org 11/35
  31. 31. Divergence model choice τ1 Shared divergences Jamie Oaks – phyletica.org 12/35
  32. 32. Divergence model choice τ1 Shared divergences Jamie Oaks – phyletica.org 12/35
  33. 33. Divergence model choice τ1 Shared divergences Jamie Oaks – phyletica.org 12/35
  34. 34. Divergence model choice τ2 τ1 Shared divergences Jamie Oaks – phyletica.org 12/35
  35. 35. Divergence model choice τ1τ2 Shared divergences Jamie Oaks – phyletica.org 12/35
  36. 36. Divergence model choice τ1τ2 Shared divergences Jamie Oaks – phyletica.org 12/35
  37. 37. Divergence model choice τ3 τ1τ2 Shared divergences Jamie Oaks – phyletica.org 12/35
  38. 38. Inferring co-diversification m1 m2 m3 m4 m5 τ1 τ2 τ1 τ1τ2 τ1τ2 τ3 τ1τ2 J. R. Oaks et al. (2013). Evolution 67: 991–1010, J. R. Oaks (2014). BMC Evolutionary Biology 14: 150 Shared divergences Jamie Oaks – phyletica.org 13/35
  39. 39. Inferring co-diversification m1 m2 m3 m4 m5 τ1 τ2 τ1 τ1τ2 τ1τ2 τ3 τ1τ2 We want to infer m and T given DNA sequence alignments X J. R. Oaks et al. (2013). Evolution 67: 991–1010, J. R. Oaks (2014). BMC Evolutionary Biology 14: 150 Shared divergences Jamie Oaks – phyletica.org 13/35
  40. 40. Inferring co-diversification p(m1 | X) p(m2 | X) p(m3 | X) p(m4 | X) p(m5 | X) τ1 τ2 τ1 τ1τ2 τ1τ2 τ3 τ1τ2 We want to infer m and T given DNA sequence alignments X J. R. Oaks et al. (2013). Evolution 67: 991–1010, J. R. Oaks (2014). BMC Evolutionary Biology 14: 150 Shared divergences Jamie Oaks – phyletica.org 13/35
  41. 41. Inferring co-diversification p(m1 | X) p(m2 | X) p(m3 | X) p(m4 | X) p(m5 | X) τ1 τ2 τ1 τ1τ2 τ1τ2 τ3 τ1τ2 We want to infer m and T given DNA sequence alignments X p(mi | X) ∝ p(X | mi )p(mi ) J. R. Oaks et al. (2013). Evolution 67: 991–1010, J. R. Oaks (2014). BMC Evolutionary Biology 14: 150 Shared divergences Jamie Oaks – phyletica.org 13/35
  42. 42. Inferring co-diversification p(m1 | X) p(m2 | X) p(m3 | X) p(m4 | X) p(m5 | X) τ1 τ2 τ1 τ1τ2 τ1τ2 τ3 τ1τ2 We want to infer m and T given DNA sequence alignments X p(mi | X) ∝ p(X | mi )p(mi ) p(X | mi ) = θ p(X | θ, mi )p(θ | mi )dθ J. R. Oaks et al. (2013). Evolution 67: 991–1010, J. R. Oaks (2014). BMC Evolutionary Biology 14: 150 Shared divergences Jamie Oaks – phyletica.org 13/35
  43. 43. Inferring co-diversification p(m1 | X) p(m2 | X) p(m3 | X) p(m4 | X) p(m5 | X) τ1 τ2 τ1 τ1τ2 τ1τ2 τ3 τ1τ2 We want to infer m and T given DNA sequence alignments X p(mi | X) ∝ p(X | mi )p(mi ) p(X | mi ) = θ p(X | θ, mi )p(θ | mi )dθ Divergence times Gene trees Substitution parameters Demographic parameters J. R. Oaks et al. (2013). Evolution 67: 991–1010, J. R. Oaks (2014). BMC Evolutionary Biology 14: 150 Shared divergences Jamie Oaks – phyletica.org 13/35
  44. 44. Inferring co-diversification p(m1 | X) p(m2 | X) p(m3 | X) p(m4 | X) p(m5 | X) τ1 τ2 τ1 τ1τ2 τ1τ2 τ3 τ1τ2 Challenges: J. R. Oaks et al. (2013). Evolution 67: 991–1010, J. R. Oaks (2014). BMC Evolutionary Biology 14: 150 Shared divergences Jamie Oaks – phyletica.org 13/35
  45. 45. Inferring co-diversification p(m1 | X) p(m2 | X) p(m3 | X) p(m4 | X) p(m5 | X) τ1 τ2 τ1 τ1τ2 τ1τ2 τ3 τ1τ2 Challenges: 1. Cannot solve all the integrals analytically J. R. Oaks et al. (2013). Evolution 67: 991–1010, J. R. Oaks (2014). BMC Evolutionary Biology 14: 150 Shared divergences Jamie Oaks – phyletica.org 13/35
  46. 46. Inferring co-diversification p(m1 | X) p(m2 | X) p(m3 | X) p(m4 | X) p(m5 | X) τ1 τ2 τ1 τ1τ2 τ1τ2 τ3 τ1τ2 Challenges: 1. Cannot solve all the integrals analytically 2. Likelihood is tractable, but “cumbersome” (or is it?. . . ) J. R. Oaks et al. (2013). Evolution 67: 991–1010, J. R. Oaks (2014). BMC Evolutionary Biology 14: 150 Shared divergences Jamie Oaks – phyletica.org 13/35
  47. 47. Inferring co-diversification p(m1 | X) p(m2 | X) p(m3 | X) p(m4 | X) p(m5 | X) τ1 τ2 τ1 τ1τ2 τ1τ2 τ3 τ1τ2 Challenges: 1. Cannot solve all the integrals analytically 2. Likelihood is tractable, but “cumbersome” (or is it?. . . ) Numerical approximation via approximate-likelihood Bayesian computation (ABC) J. R. Oaks et al. (2013). Evolution 67: 991–1010, J. R. Oaks (2014). BMC Evolutionary Biology 14: 150 Shared divergences Jamie Oaks – phyletica.org 13/35
  48. 48. Inferring co-diversification p(m1 | X) p(m2 | X) p(m3 | X) p(m4 | X) p(m5 | X) τ1 τ2 τ1 τ1τ2 τ1τ2 τ3 τ1τ2 Challenges: 1. Cannot solve all the integrals analytically 2. Likelihood is tractable, but “cumbersome” (or is it?. . . ) Numerical approximation via approximate-likelihood Bayesian computation (ABC) 3. Sampling over all possible models J. R. Oaks et al. (2013). Evolution 67: 991–1010, J. R. Oaks (2014). BMC Evolutionary Biology 14: 150 Shared divergences Jamie Oaks – phyletica.org 13/35
  49. 49. Inferring co-diversification p(m1 | X) p(m2 | X) p(m3 | X) p(m4 | X) p(m5 | X) τ1 τ2 τ1 τ1τ2 τ1τ2 τ3 τ1τ2 Challenges: 1. Cannot solve all the integrals analytically 2. Likelihood is tractable, but “cumbersome” (or is it?. . . ) Numerical approximation via approximate-likelihood Bayesian computation (ABC) 3. Sampling over all possible models 5 taxa = 52 models J. R. Oaks et al. (2013). Evolution 67: 991–1010, J. R. Oaks (2014). BMC Evolutionary Biology 14: 150 Shared divergences Jamie Oaks – phyletica.org 13/35
  50. 50. Inferring co-diversification p(m1 | X) p(m2 | X) p(m3 | X) p(m4 | X) p(m5 | X) τ1 τ2 τ1 τ1τ2 τ1τ2 τ3 τ1τ2 Challenges: 1. Cannot solve all the integrals analytically 2. Likelihood is tractable, but “cumbersome” (or is it?. . . ) Numerical approximation via approximate-likelihood Bayesian computation (ABC) 3. Sampling over all possible models 5 taxa = 52 models 10 taxa = 115,975 models J. R. Oaks et al. (2013). Evolution 67: 991–1010, J. R. Oaks (2014). BMC Evolutionary Biology 14: 150 Shared divergences Jamie Oaks – phyletica.org 13/35
  51. 51. Inferring co-diversification p(m1 | X) p(m2 | X) p(m3 | X) p(m4 | X) p(m5 | X) τ1 τ2 τ1 τ1τ2 τ1τ2 τ3 τ1τ2 Challenges: 1. Cannot solve all the integrals analytically 2. Likelihood is tractable, but “cumbersome” (or is it?. . . ) Numerical approximation via approximate-likelihood Bayesian computation (ABC) 3. Sampling over all possible models 5 taxa = 52 models 10 taxa = 115,975 models 20 taxa = 51,724,158,235,372 models!! J. R. Oaks et al. (2013). Evolution 67: 991–1010, J. R. Oaks (2014). BMC Evolutionary Biology 14: 150 Shared divergences Jamie Oaks – phyletica.org 13/35
  52. 52. Inferring co-diversification p(m1 | X) p(m2 | X) p(m3 | X) p(m4 | X) p(m5 | X) τ1 τ2 τ1 τ1τ2 τ1τ2 τ3 τ1τ2 Challenges: 1. Cannot solve all the integrals analytically 2. Likelihood is tractable, but “cumbersome” (or is it?. . . ) Numerical approximation via approximate-likelihood Bayesian computation (ABC) 3. Sampling over all possible models 5 taxa = 52 models 10 taxa = 115,975 models 20 taxa = 51,724,158,235,372 models!! A “diffuse” Dirichlet process prior (DPP) J. R. Oaks et al. (2013). Evolution 67: 991–1010, J. R. Oaks (2014). BMC Evolutionary Biology 14: 150 Shared divergences Jamie Oaks – phyletica.org 13/35
  53. 53. “Easy” as ABC A A A G G G C C C C C C G G G G G G A A A A A T A A A A A A T T C C C C G G G G G G T T T T T T G G G G G G C C C T T T T T T C C C C C C C C C G G G G G G C C T T T T A A A A A A C C C C C C G G G G G G T T T T T T A A A G G G C C C C C C C C C C C C A A A T T T G G G G G G T T T T C C A A A A A A C C C C C C C C C T T T G G G G G G G G G G G G T T T T T T S1 S2 S3 Shared divergences Jamie Oaks – phyletica.org 14/35
  54. 54. “Easy” as ABC A A A G G G C C C C C C G G G G G G A A A A A T A A A A A A T T C C C C G G G G G G T T T T T T G G G G G G C C C T T T T T T C C C C C C C C C G G G G G G C C T T T T A A A A A A C C C C C C G G G G G G T T T T T T A A A G G G C C C C C C C C C C C C A A A T T T G G G G G G T T T T C C A A A A A A C C C C C C C C C T T T G G G G G G G G G G G G T T T T T T S1 S2 S3 Shared divergences Jamie Oaks – phyletica.org 14/35
  55. 55. “Easy” as ABC A A A G G G C C C C C C G G G G G G A A A A A T A A A A A A T T C C C C G G G G G G T T T T T T G G G G G G C C C T T T T T T C C C C C C C C C G G G G G G C C T T T T A A A A A A C C C C C C G G G G G G T T T T T T A A A G G G C C C C C C C C C C C C A A A T T T G G G G G G T T T T C C A A A A A A C C C C C C C C C T T T G G G G G G G G G G G G T T T T T T S1 S2 S3 Shared divergences Jamie Oaks – phyletica.org 14/35
  56. 56. “Easy” as ABC 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 S1 S2 S3 Shared divergences Jamie Oaks – phyletica.org 15/35
  57. 57. “Easy” as ABC 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 S1 S2 S3 Shared divergences Jamie Oaks – phyletica.org 15/35
  58. 58. “Easy” as ABC 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 S1 S2 S3 Shared divergences Jamie Oaks – phyletica.org 15/35
  59. 59. “Easy” as ABC 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 S1 S2 S3 Shared divergences Jamie Oaks – phyletica.org 15/35
  60. 60. “Easy” as ABC 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 S1 S2 S3 Shared divergences Jamie Oaks – phyletica.org 15/35
  61. 61. “Easy” as ABC 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 S1 S2 S3 Shared divergences Jamie Oaks – phyletica.org 15/35
  62. 62. “Easy” as ABC 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 S1 S2 S3 Shared divergences Jamie Oaks – phyletica.org 15/35
  63. 63. “Easy” as ABC 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 S1 S2 S3 Shared divergences Jamie Oaks – phyletica.org 15/35
  64. 64. “Easy” as ABC 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 S1 S2 S3 Shared divergences Jamie Oaks – phyletica.org 15/35
  65. 65. α 1 1 α α 2 1 Shared divergences Jamie Oaks – phyletica.org 17/35
  66. 66. α 1 1 α α 2 1 Shared divergences Jamie Oaks – phyletica.org 17/35
  67. 67. α 1 1 α α 2 1 Shared divergences Jamie Oaks – phyletica.org 17/35
  68. 68. α 1 1 α α 2 1 Shared divergences Jamie Oaks – phyletica.org 17/35
  69. 69. α α+1 α α+2 α α α+1 1 α+2 1 α α+1 1 α+2 1 α 1 α+1 α α+2 α 1 α+1 2 α+22 1 Shared divergences Jamie Oaks – phyletica.org 17/35
  70. 70. α = 0.5 α α+1 α α+2 = 0.067 α α α+1 1 α+2 = 0.133 1 α α+1 1 α+2 = 0.133 1 α 1 α+1 α α+2 = 0.133 α 1 α+1 2 α+2 = 0.5332 1 Shared divergences Jamie Oaks – phyletica.org 17/35
  71. 71. α = 10.0 α α+1 α α+2 = 0.758 α α α+1 1 α+2 = 0.076 1 α α+1 1 α+2 = 0.076 1 α 1 α+1 α α+2 = 0.076 α 1 α+1 2 α+2 = 0.0152 1 Shared divergences Jamie Oaks – phyletica.org 17/35
  72. 72. New method: dpp-msbayes Approximate-likelihood Bayesian approach to inferring models of shared divergences J. R. Oaks (2014). BMC Evolutionary Biology 14: 150 Shared divergences Jamie Oaks – phyletica.org 18/35
  73. 73. New method: dpp-msbayes Approximate-likelihood Bayesian approach to inferring models of shared divergences Flexible Dirichlet-process prior (DPP) over all possible divergence models J. R. Oaks (2014). BMC Evolutionary Biology 14: 150 Shared divergences Jamie Oaks – phyletica.org 18/35
  74. 74. New method: dpp-msbayes Approximate-likelihood Bayesian approach to inferring models of shared divergences Flexible Dirichlet-process prior (DPP) over all possible divergence models Flexible priors on parameters to avoid strongly weighted posteriors J. R. Oaks (2014). BMC Evolutionary Biology 14: 150 Shared divergences Jamie Oaks – phyletica.org 18/35
  75. 75. New method: dpp-msbayes Approximate-likelihood Bayesian approach to inferring models of shared divergences Flexible Dirichlet-process prior (DPP) over all possible divergence models Flexible priors on parameters to avoid strongly weighted posteriors Multi-processing to accommodate genomic datasets J. R. Oaks (2014). BMC Evolutionary Biology 14: 150 Shared divergences Jamie Oaks – phyletica.org 18/35
  76. 76. dpp-msbayes: Simulation-based assessment Validation: Simulate 50,000 datasets and analyze each under the same model Shared divergences Jamie Oaks – phyletica.org 19/35
  77. 77. dpp-msbayes: Simulation-based assessment Validation: Simulate 50,000 datasets and analyze each under the same model Robustness: Simulate datasets that violate model assumptions and analyze each of them Shared divergences Jamie Oaks – phyletica.org 19/35
  78. 78. dpp-msbayes: Validation results 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 Posterior probability of one divergence Trueprobabilityofonedivergence J. R. Oaks (2014). BMC Evolutionary Biology 14: 150 Shared divergences Jamie Oaks – phyletica.org 20/35
  79. 79. dpp-msbayes: Validation results 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 Posterior probability of one divergence Trueprobabilityofonedivergence 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 Posterior probability of one divergence Trueprobabilityofonedivergence J. R. Oaks (2014). BMC Evolutionary Biology 14: 150 Shared divergences Jamie Oaks – phyletica.org 20/35
  80. 80. dpp-msbayes: Performance New method for estimating shared evolutionary history shows: 1. Model-choice accuracy 2. Robustness to model violations 3. Power to detect variation in divergence times 4. It’s fast! J. R. Oaks (2014). BMC Evolutionary Biology 14: 150 Shared divergences Jamie Oaks – phyletica.org 21/35
  81. 81. dpp-msbayes: Performance New method for estimating shared evolutionary history shows: 1. Model-choice accuracy 2. Robustness to model violations 3. Power to detect variation in divergence times 4. It’s fast! A new tool for biologists to leverage comparative genomic data to explore processes of co-diversification J. R. Oaks (2014). BMC Evolutionary Biology 14: 150 Shared divergences Jamie Oaks – phyletica.org 21/35
  82. 82. Outline An assumption (i.e., exciting opportunity) in phylogenetics An approach to the problem Empirical applications Current and future directions Shared divergences Jamie Oaks – phyletica.org 22/35
  83. 83. Shared divergences Jamie Oaks – phyletica.org 23/35
  84. 84. Shared divergences Jamie Oaks – phyletica.org 24/35
  85. 85. Did repeated fragmentation of islands during inter-glacial rises in sea level promote diversification? Shared divergences Jamie Oaks – phyletica.org 24/35
  86. 86. Climate-driven diversification Shared divergences Jamie Oaks – phyletica.org 25/35
  87. 87. Climate-driven diversification Shared divergences Jamie Oaks – phyletica.org 25/35
  88. 88. Climate-driven diversification Shared divergences Jamie Oaks – phyletica.org 25/35
  89. 89. Results 1 3 5 7 9 11 13 15 17 19 21 Number of divergence events 0.00 0.02 0.04 0.06 0.08 0.10 Posteriorprobability J. R. Oaks (2014). BMC Evolutionary Biology 14: 150 Shared divergences Jamie Oaks – phyletica.org 26/35
  90. 90. Results 1 3 5 7 9 11 13 15 17 19 21 Number of divergence events 0.00 0.02 0.04 0.06 0.08 0.10 Posteriorprobability 0100200300400500 Time (kya) 0 -50 -100 Sealevel(m) J. R. Oaks (2014). BMC Evolutionary Biology 14: 150 Shared divergences Jamie Oaks – phyletica.org 26/35
  91. 91. More data! Collecting genomic data from taxa co-distributed across Southeast Asian Islands and Mainland Shared divergences Jamie Oaks – phyletica.org 27/35
  92. 92. More data! Collecting genomic data from taxa co-distributed across Southeast Asian Islands and Mainland Preliminary results for 1000 loci from 5 pairs of Gekko mindorensis populations 1 2 3 4 5 Number of divergence events, j¿j -5.0 -4.0 -3.0 -2.0 -1.0 0.0 1.0 2.0 3.0 2ln(Bayesfactor) Shared divergences Jamie Oaks – phyletica.org 27/35
  93. 93. Diversification across African rainforests Did climate cycles drive diversification and community assembly across rainforest taxa? Shared divergences Jamie Oaks – phyletica.org 28/35
  94. 94. Diversification across African rainforests Did climate cycles drive diversification and community assembly across rainforest taxa? Preliminary results with 300 loci from 3 taxa 1 2 3 Number of divergence events, j¿j -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2ln(Bayesfactor) Shared divergences Jamie Oaks – phyletica.org 28/35
  95. 95. Conclusions New method for estimating shared evolutionary history Shows good “frequentist” behavior Relatively robust to model violations Shared divergences Jamie Oaks – phyletica.org 29/35
  96. 96. Conclusions New method for estimating shared evolutionary history Shows good “frequentist” behavior Relatively robust to model violations Finding support for temporally clustered divergences in multiple systems Shared divergences Jamie Oaks – phyletica.org 29/35
  97. 97. Conclusions New method for estimating shared evolutionary history Shows good “frequentist” behavior Relatively robust to model violations Finding support for temporally clustered divergences in multiple systems However, there is a lot of uncertainty! Shared divergences Jamie Oaks – phyletica.org 29/35
  98. 98. Outline An assumption (i.e., exciting opportunity) in phylogenetics An approach to the problem Empirical applications Current and future directions Shared divergences Jamie Oaks – phyletica.org 30/35
  99. 99. Current work: More power Ecoevolity: Estimating evolutionary coevality 1 D. Bryant et al. (2012). Molecular Biology And Evolution 29: 1917–1932 Shared divergences Jamie Oaks – phyletica.org 31/35
  100. 100. Current work: More power Ecoevolity: Estimating evolutionary coevality Full-likelihood Bayesian implementation Uses all the information in the data Applicable to deeper timescales 1 D. Bryant et al. (2012). Molecular Biology And Evolution 29: 1917–1932 Shared divergences Jamie Oaks – phyletica.org 31/35
  101. 101. Current work: More power Ecoevolity: Estimating evolutionary coevality Full-likelihood Bayesian implementation Uses all the information in the data Applicable to deeper timescales Analytically integrate over gene trees 1 Very efficient numerical approximation of posterior Applicable to NGS datasets 1 D. Bryant et al. (2012). Molecular Biology And Evolution 29: 1917–1932 Shared divergences Jamie Oaks – phyletica.org 31/35
  102. 102. Next step: A general framework Develop a framework for inferring shared divergences across phylogenies τ1τ2 Shared divergences Jamie Oaks – phyletica.org 32/35
  103. 103. Next step: A general framework Develop a framework for inferring shared divergences across phylogenies τ1τ2 Shared divergences Jamie Oaks – phyletica.org 32/35
  104. 104. Next step: A general framework Develop a framework for inferring shared divergences across phylogenies Generalize Bayesian phylogenetics to incorporate shared divergences τ1τ2 Shared divergences Jamie Oaks – phyletica.org 32/35
  105. 105. Next step: A general framework Develop a framework for inferring shared divergences across phylogenies Generalize Bayesian phylogenetics to incorporate shared divergences Sample models numerically via reversible-jump Markov chain Monte Carlo τ1τ2 Shared divergences Jamie Oaks – phyletica.org 32/35
  106. 106. Next step: A general framework Develop a framework for inferring shared divergences across phylogenies Generalize Bayesian phylogenetics to incorporate shared divergences Sample models numerically via reversible-jump Markov chain Monte Carlo Benefits: Improve phylogenetic inference Framework for studying processes of co-diversification τ1τ2 Shared divergences Jamie Oaks – phyletica.org 32/35
  107. 107. Everything is on GitHub. . . Software: Ecoevolity: https://github.com/phyletica/ecoevolity PyMsBayes: https://joaks1.github.io/PyMsBayes dpp-msbayes: https://github.com/joaks1/dpp-msbayes ABACUS: Approximate BAyesian C UtilitieS. https://github.com/joaks1/abacus Open-Science Notebook: msbayes-experiments: https://github.com/joaks1/msbayes-experiments Shared divergences Jamie Oaks – phyletica.org 33/35
  108. 108. Acknowledgments Ideas and feedback: Leach´e Lab Minin Lab Holder Lab Brown Lab/KU Herpetology Computation: Funding: Photo credits: Rafe Brown, Cam Siler, Jesse Grismer, & Jake Esselstyn FMNH Philippine Mammal Website: D.S. Balete, M.R.M. Duya, & J. Holden PhyloPic! Shared divergences Jamie Oaks – phyletica.org 34/35
  109. 109. Questions? joaks@auburn.edu c 2007 Boris Kulikov boris-kulikov.blogspot.com Shared divergences Jamie Oaks – phyletica.org 35/35

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