1. Lecture 17: Phylogenetics and Phylogeography
October 22, 2012

2. Announcements Exam Next Wednesday (Oct 31) Review on Monday
Bring questions
Covers material from genetic drift (Sept 28) through Coalescence (Friday)
I will be gone Monday, Oct 29 (after office hours) through Oct 31
Bring questions on Monday!

3. Last Time Using FST to estimate migration
Direct estimates of migration: parentage analysis
Introduction to phylogenetic analysis

4. Today Phylogeography Limitations of phylogenetic analysis
Coalescence introduction
Influence of demography on coalescence time

5. Read the Lab 9 Introduction!
UPGMA Method
Use all pairwise comparisons to make dendrogram
UPGMA:Unweighted Pairwise Groups Method using Arithmetic Means
Hierarchically link most closely related individuals
Read the Lab 9 Introduction!

6. Phenetics (distance) vs Cladistics (character state based)
Lowe, Harris, and Ashton 2004

7. Parsimony Methods
Based on underlying genealogical relationships among alleles
Occam’s Razor: simplest scenario is the most likely
Useful for depicting evolutionary relationships among taxa or populations
Choose tree that requires smallest number of steps (mutations) to produce observed relationships

8. Choosing Phylogenetic Trees
MANY possible trees can be built for a given set of taxa
Very computationally intensive to choose among these
Lowe, Harris, and Ashton 2004

9. Choosing Phylogenetic Trees
Many algorithms exist for searching tree space
Local optima are problem: need to traverse valleys to get to other peaks
Heuristic search: cut trees up systematically and reassemble
Branch and bound: search for optimal path through tree space 7 9 8 5 10 11
Felsenstein 2004

10. Choosing Phylogenetic Trees
If multiple trees equally likely, select majority rule or consensus
Strict consensus is most conservative approach
Bootstrap data matrix (sample with replacement) to determine robustness of nodes
Felsenstein 2004
Lowe, Harris, and Ashton 2004 E A C B D F 60

11. Phylogeography
The study of evolutionary relationships among individuals based on phylogenetic analysis of DNA sequences in geographic context
Can be used to infer evolutionary history of populations
Migrations
Population subdivisions
Bottlenecks/Founder Effects
Can provide insights on current relationships among populations
Connectedness of populations
Effects of landscape features on gene flow

12. Phylogeography
Topology of tree provides clues about evolutionary and ecological history of a set of populations
Dispersal creates poor correspondence between geography and tree topology
Vicariance (division of populations preventing gene flow among subpopulations) results in neat mapping of geography onto haplotypes

13. Example: Pocket gophers (Geomys pinetis)
Avise 2004
Fossorial rodent that inhabits 3-state area in the U.S.
RFLP for mtDNA of 87 individuals revealed 23 haplotypes
Parsimony network reveals geographic relationships among haplotypes
Haplotypes generally confined to single populations
Major east-west split in distribution revealed

14. Problems with using Phylogenetics for Inferring Evolution
It’s a black box: starting from end point, reconstructing past based on assumed evolutionary model
Homologs versus paralogs
Hybridization
Differential evolutionary rates
Assumes coalescence

15. Gene Orthology
Phylogenetics requires unambiguous identification of orthologous genes
Paralogous genes are duplicated copies that do not share a common evolutionary history
Difficult to determine orthology relationships
Lowe, Harris, Ashton 2004

16. Gene Trees vs Species Trees
Genes (or loci) evolve at different rates
Why?
Topology derived by a single gene may not match topology based on whole genome, or morphological traits A C B
Gene Tree

17. Gene Trees vs Species Treesa b c
Concordant Gene Tree
b is closer to a than to c
Gene Trees vs Species Trees
Failure to coalesce within species lineages drives divergence of relationships between gene and species trees a b c
Divergent Gene Tree:
b is closer to c than to a

18. Coalescence Retrospective tracing of ancestry of individual alleles
Allows explicit simulation of sequence evolution
Incorporation of factors that cause deviation from neutrality: selection, drift, and gene flow

19. 9 generations in the history of a population of 14 gene copies
Slide courtesy of Yoav Gilad
Time
present
Individual alleles

20. How to model this process?

21. Modeling from Theoretical Ancestors: Forward Evolution
Can model populations in a forward direction, starting with theoretical past
Fisher-Wright model of neutral evolution
Very computationally intensive for large populations

22. Alternative: Start at the end and work your way back
Most recent common ancestor (MRCA)
Time
present
Individual alleles
Slide courtesy of Yoav Gilad

23. The genealogy of a sample of 5 gene copies
Most recent common ancestor (MRCA)
Time
present
individuals
Slide courtesy of Yoav Gilad

24. The genealogy of a sample of 5 gene copies
Most recent common ancestor (MRCA)
Time
present
Individual alleles
Slide courtesy of Yoav Gilad

25. Examples of coalescent trees for a sample of 6
Time
Individual alleles
Slide courtesy of Yoav Gilad

26. Coalescence Advantages
Don’t have to model dead ends
Only consider lineages that survive to modern day: computationally efficient
Based on actual observations
Can simulate different evolutionary scenarios to see what best fits the observed data

27. Coalescent Tree Example
Coalescence: Merging of two lineages in the Most Recent Common Ancestor (MRCA)
Waiting Time: time to coalescence for two lineages
Increases with each coalescent event

28. Probability of Coalescence
For any two lineages, function of population size
Also a function of number of lineages
where k is number of lineages

29. Probability of Coalescence
Probability declines over time
Lineages decrease in number
Can be estimated based on negative exponential
where k is number of lineages

30. Time to Coalescence Affected by Population History
Bottleneck

31. Time to Coalescence Affected by Population History
Population Growth

32. Time to Coalescence Affected by Population Structure

33. Applications of the Coalescent Approach
Framework for efficiently testing alternative models for evolution
Inferences about effective population size
Detection of population structure
Signatures of selection (coming attraction)