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Lecture 2: Principles of Phylogenetics

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1 Lecture 2: Principles of Phylogenetics
Origins of Classification -Organization of variation 2) Modern Systematics -Taxonomy and phylogenetics 3) Cladistics -Shared derived characters -Outgroup -Parsimony 4) Maximum Likelihood and Bayesian Inference

2 Origins of Biological Classification
Aristotle BC “An effort to show the relationships of living things as a scala naturae”1 Scala Naturae — From Charles Bonnet's Œuvresd'histoire naturelle et de philosophie, 1781 1C. Singer, A Short History of Biology (1931)

3 Linnaeus "God created, Linnaeus organized."

4

5 Systematics

6 Phylogenetic Systematics
-Relationships reflected in taxonomy vertebral column complete jaw “bony vertebrates” 4 legs amniotic egg Maxilla separated from quadratojugal by jugal

7 Anatomy of a phylogenetic tree
Sister-taxa Internal branch Terminal taxa Node Common Ancestor Terminal branch Outgroup older splits younger splits

8 Bifurcating vs multifurcating trees
polytomy trichotomy

9 A German entomologist, Willi Hennig developed the field of “Phylogenetic Systematics” which provides a framework for reconstructing phylogenies and using them to study evolutionary history Hennig (1950)

10 Cladistics -Builds trees by identifying monophyletic groups
-All other widely used methods are derived

11 How do you identify synapomorphies?
Close Outgroups Distant Outgroups

12 Amphioxus (Cephalochordate)

13 Cladistics -Builds trees by identifying monophyletic groups
-All other widely used methods are derived

14 Principle of Parsimony
Heuristic = educated guess; rule of thumb; common sense; a general way to approach problem solving.

15 1) use only derived character states 2) minimize evolutionary change
outgroup taxon character wiley rr bugs daffy tweety happy 1) Gloves: 2) Long ears 3) Beak: 4) Tail: 5) Appendages: 6) Feathers: 7) Thumb: Make a tree: 1) use only derived character states 2) minimize evolutionary change

16 4 & 5 3 & 6 bugs happy wiley daffy tweety rr + tail + appendages bugs
+ beak + feathers

17 3, 4, 5, & 6. bugs happy wiley daffy tweety rr + beak + feathers
+ tail + appendages

18 Phylogenetically uninformative
1, 2, 3, 4, 5, & 6. bugs happy wiley daffy tweety rr + beak + gloves + long ears + tail + appendages + feathers Autapomorphy Phylogenetically uninformative

19 1, 2, 3, 4, 5, 6, & 7 tweety daffy bugs rr wiley happy + thumb
+ gloves + long ears + feathers + beak + tail + appendages bugs happy wiley daffy tweety rr + beak + gloves + long ears + tail + appendages + feathers + thumb - thumb

20 Finding the Most Parsimonious Tree
1) Exhaustive Search 2) Branch and Bound Search 3) Heuristic Search

21 Exhaustive Search with stepwise addition of taxa

22 Exhaustive Searches Rarely Used
N = The number of bifurcating unrooted trees: (2n-5)! 2n-3(n-3)! Where n = the number of terminal taxa For 6 taxa trees For 20 taxa x 1020 trees

23 3) Heuristic Search No guarantee best tree will be found
Impossible to “pass through” poorer trees to get to more parsimonious

24 The Problem with Parsimony:
Molecular Phylogenetics The Problem with Parsimony: Adenine Guanine Purines Pyrimidines Thymine Cytosine Transversions Transitions

25 Multiple Substitutions at single sites can lead to “Long-branch attraction”

26 (Unweighted) Parsimony

27 Maximum Likelihood C G A

28 1) Start with one tree 2) Sum probs across all ancestral reconstructions 4) Repeat for all trees (in a heuristic search) 3) Sum probs across each site

29 But…we don’t know: Simplest Model: Jukes-Cantor (JC)
G T 4 bases 6 different types of substitutions Simplest Model: Jukes-Cantor (JC) All 6 substitutions - equal probability (α)

30 Kimura 2-parameter model (K2P)
= transitions β = transversions General Time Reversible (GTR)

31 Where do the parameters values come from?
G A T C ts tv Wait…we’re using a tree to infer the model parameters that we will then use to find…the best tree?

32 Maximum Likelihood Operationally
1. Select a model of sequence evolution; infer parameter values 2. With fixed parameter values, search tree space heuristically, with branch swapping 3. Select the topology that yields the greatest likelihood for the

33 Summary

34 Symmetrical Branch Lengths

35 Asymmetrical Branch Lengths
Positively misleading

36 Disadvantages of ML

37 Bayesian Phylogenetic Inference
Similar to ML except: Model parameters: 2. Simultaneously search Pr(p|k) p

38 Bayesian Phylogenetic Inference
3. Save trees Tree topology Model parameters

39 Bayesian Phylogenetic Inference
Searching for trees and parameters Markov-Chain Monte Carlo Search Start: random tree, model parameter values. Calculate likelihood (L). Slightly change the tree and/or parameter values; re-calculate L. Accept or reject new tree/parameter values based on L scores. Better L scores (fewer changes) are always accepted, lower or equal scores accepted with some probability (“hill-climbing” algorithm = Metropolis sampling)

40 Advantages of Bayesian Inference
1) Simultaneous exploration of parameter space and trees 2) Support for clades: evaluated across a large set of likely trees 3) MCMC: Faster Reed et al. (2002) ML heuristic search: 93 days Nearly identical topologies MCMC search: 9 days


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