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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
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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)
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Linnaeus "God created, Linnaeus organized."
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Systematics
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Phylogenetic Systematics
-Relationships reflected in taxonomy vertebral column complete jaw “bony vertebrates” 4 legs amniotic egg Maxilla separated from quadratojugal by jugal
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Anatomy of a phylogenetic tree
Sister-taxa Internal branch Terminal taxa Node Common Ancestor Terminal branch Outgroup older splits younger splits
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Bifurcating vs multifurcating trees
polytomy trichotomy
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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)
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Cladistics -Builds trees by identifying monophyletic groups
-All other widely used methods are derived
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How do you identify synapomorphies?
Close Outgroups Distant Outgroups
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Amphioxus (Cephalochordate)
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Cladistics -Builds trees by identifying monophyletic groups
-All other widely used methods are derived
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Principle of Parsimony
Heuristic = educated guess; rule of thumb; common sense; a general way to approach problem solving.
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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
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4 & 5 3 & 6 bugs happy wiley daffy tweety rr + tail + appendages bugs
+ beak + feathers
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3, 4, 5, & 6. bugs happy wiley daffy tweety rr + beak + feathers
+ tail + appendages
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Phylogenetically uninformative
1, 2, 3, 4, 5, & 6. bugs happy wiley daffy tweety rr + beak + gloves + long ears + tail + appendages + feathers Autapomorphy Phylogenetically uninformative
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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
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Finding the Most Parsimonious Tree
1) Exhaustive Search 2) Branch and Bound Search 3) Heuristic Search
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Exhaustive Search with stepwise addition of taxa
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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
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3) Heuristic Search No guarantee best tree will be found
Impossible to “pass through” poorer trees to get to more parsimonious
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The Problem with Parsimony:
Molecular Phylogenetics The Problem with Parsimony: Adenine Guanine Purines Pyrimidines Thymine Cytosine Transversions Transitions
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Multiple Substitutions at single sites can lead to “Long-branch attraction”
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(Unweighted) Parsimony
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Maximum Likelihood C G A
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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
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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)
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Kimura 2-parameter model (K2P)
= transitions β = transversions General Time Reversible (GTR)
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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?
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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
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Summary
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Symmetrical Branch Lengths
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Asymmetrical Branch Lengths
Positively misleading
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Disadvantages of ML
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Bayesian Phylogenetic Inference
Similar to ML except: Model parameters: 2. Simultaneously search Pr(p|k) p
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Bayesian Phylogenetic Inference
3. Save trees Tree topology Model parameters
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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)
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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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