Lecture 17: Phylogenetics and Phylogeography

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

9. Choosing Phylogenetic Trees 9 8 9 10 9 11 9 8 7 11 9 5 Felsenstein 2004 • 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

10. Choosing Phylogenetic Trees E A F Felsenstein 2004 D C B 60 60 60 Lowe, Harris, and Ashton 2004 • 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

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. Avise 2004 Example: Pocket gophers (Geomys pinetis) • 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 Gene Tree B C A • 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

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

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 Time present Individual alleles Slide courtesy of Yoav Gilad

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)