Phylogeny reconstruction

1. Phylogeny reconstruction • How do we reconstruct the tree of life? • Outline: • Terminology • Methods • distance • parsimony • maximum likelihood • bootstrapping • Problems • homoplasy • hybridisation Dr. Sean Graham, UBC.

2. Phylogenetic reconstruction

3. Phylogenetic reconstruction • Rooted trees

4. Phylogenetic reconstruction • Rooted trees Outgroup:

5. Phylogenetic reconstruction Introduction

6. Understanding Trees Birds Amphibians Crocodiles Birds Mammals Lizards Snakes Turtles Crocodiles Snakes Lizards Turtles Mammals Amphibians

7. Do these phylogenies agree? Figure 14.17

8. Branch lengths A B C D A B C D 1 nt change

9. A B C D E A B C D E A B C D E Understanding Trees Trees can be used to describe taxonomic groups Monophyletic • Paraphyletic Polyphyletic

10. Amniotes Amphibians Crocodiles Mammals Snakes Lizards Turtles Birds Amnion What is the relationship between taxonomic names and phylogenetic groups?

11. Reptiles Crocodiles Snakes Lizards Turtles Birds Cold Blooded What is the relationship between taxonomic names and phylogenetic groups?

12. Amphibians Crocodiles Rodents Lizards Snakes Turtles Birds Bats Wings What is the relationship between taxonomic names and phylogenetic groups?

13. Polyphyletic example: Amentiferae

14. Polyphyletic example: Amentiferae Oaks Walnuts Willows Evolution of catkins Ancestor with separate flowers

15. Vertebrate Phylogeny Are these groups monophyletic, paraphyletic or polyphyletic? fish? tetrapods? (= four limbed) amphibians? mammals? ectotherms (= warm blooded)?

16. Constructing Trees Methods: distance (UPGMA, Neighbor joining) parsimony maximum likelihood (Bayesian)

17. Distance Methods (phenetics)

18. Distance methods rely on clustering algorithms (e.g. UPGMA) D B A Example 1: morphology Trait 2 C Distance matrix Trait 1

19. UPGMA D B A Example 1: morphology Trait 2 C Distance matrix Trait 1 A B

20. UPGMA D B A Example 1: morphology Trait 2 C Distance matrix Trait 1 A B C D

21. Distance methods with sequence data A: ATTGCAATCGG B: ATTACGATCGG C: GTTACAACCGG D: CTCGTAGTCGA Distance matrix A B

22. Distance methods with sequence data New Distance matrix: take averages A B

23. Distance methods with sequence data A B C A B C D

24. Distance methods with sequence data A B C A B C D

25. Assumptions of distance methods

26. Strengths and weaknesses of distance methods

27. II. Parsimony Methods (Cladistics) Hennig (German entomologist) wrote in 1966 Translated into English in 1976: very influential

28. Applying parsimony • Consider four taxa (1-4) and four characters (A-D) • Ancestral state: abcd Trait Taxon

29. Applying parsimony • Consider four taxa (1-4) and four characters (A-D) • Ancestral state: abcd Unique changes Convergences or reversals • 1 2 3 4 • a’bcd a’b’c’d’ a’b’c’d a’b’cd Trait b d’ c’ Taxon b’ a’ 5 steps abcd

30. Applying parsimony • Consider four taxa (1-4) and four characters (A-D) • Ancestral state: abcd Unique changes Convergences or reversals • 1 4 3 2 • a’bcd a’b’cd a’b’c’d a’b’c’d’ Trait d’ c’ Taxon b’ a’ 4 steps abcd

31. Strengths and weaknesses of parsimony Strengths Weaknesses .

32. Parsimony practice Position Taxon 1234567 K AGTACCG L AAGACTA M AACCTTA N AAAGTTA Which unrooted tree is most parsimonious? N L L L K M 2 2 K 2 M K N N M Plot each change on each tree. Positions 1 and 2 are done. Which positions help to determine relationships?

33. Inferring the direction of evolution ACGCTAGCTAGG Mouse Where did the mutation occur, and what was the change? Orangutan ACGCTAGCTAGG ACGCTAGCTAGG Gorilla ACGCTAGCTAGG Human ACGCTAGCTACG Bonobo ACGCTAGCTACG Chimp

34. III. Maximum likelihood (and Bayesian)

35. Transitions Transversions A G T C Maximum likelihood: a starting sketch • Probabilities • transition: 0.2 transversion: 0.1 no change 0.7 Find the tree with the highest probability

36. Transitions Transversions A G T C Maximum likelihood: a starting sketch • Probabilities • transition: 0.2 transversion: 0.1 no change 0.7 P = (.7)(.1)(.2)(.7)(.7) Find the tree with the highest probability

37. Transitions Transversions A G T C Maximum likelihood: a starting sketch • Probabilities • transition: 0.2 transversion: 0.1 no change 0.7 P = (.7)(.1)(.2)(.7)(.7) P = (.7)(.1)(.7)(.7)(.7) P = (.1)(.2)(.7)(.7)(.2) Find the tree with the highest probability

38. Assessment of Maximum Likelihood (also Bayesian) • Strengths • Weaknesses

39. Characters to use in phylogeny • Morphology • DNA sequence

40. Challenges of using DNA data Alignment can be very challenging! Taxon 1 AATGCGC Taxon 2 AATCGCT Taxon 1 AATGCGC Taxon 2

41. Informative sequences evolve at moderate rates • Too slow? • not enough variation • Taxon 1 AATGCGC • Taxon 2 AATGCGC • Taxon 3 AATGCGC Polytomy

42. Example of insufficient evidence: metazoan phylogeny Metazoans Fungi

43. Challenges: sunflower phylogeny • Recent radiation (200,000 years) • Many species, much hybridization • Need more rapidly evolving markers!! = 15 spp! = 12 spp!

44. Informative sequences evolve at moderate rates • Too fast? • homoplasy likely • “saturation” – only 4 possible states for DNA • Taxon 1 ATTCTGA • Taxon 2 GTAGTGG • Taxon 3 CGTGCTG Polytomy

45. Saturation • Imagine changing one nucleotide every hour to a random nucleotide • Split the ancestral population in 2. ACTTGCT ACCTGAA AGCGGAA ACCAGAA ACGTGCT ACGAGCT GCGATCC GAGCTCC AGCCTCC 8 hours 12 hours One hour Four hours Red indicates multiple mutations at a site 24 hours?

46. Saturation: mammalian mitochondrial DNA

47. Forces of evolution and phylogeny reconstruction How does each force affect the ability to reconstruct phylogeny? mutation? drift? selection? non-random mating? migration?

48. Phylogeny case study I: whales Are whales ungulates (hoofed mammals)? Figure 14.4

49. Whales: DNA sequence data Hillis, D. A. 1999. How reliable is this tree? Bootstrapping.

50. How consistent are the data? • Take the dataset (5 taxa, 10 characters) • Create a new data set by sampling characters at random, with replacement