Lecture 17: Phylogenetics and Phylogeography October 22, 2012.
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Transcript of Lecture 17: Phylogenetics and Phylogeography October 22, 2012.
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Lecture 17: Phylogenetics and Phylogeography
October 22, 2012
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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!
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Last Time
Using FST to estimate migration
Direct estimates of migration: parentage analysis
Introduction to phylogenetic analysis
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Today
Phylogeography
Limitations of phylogenetic analysis
Coalescence introduction
Influence of demography on coalescence time
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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!
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Phenetics (distance) vs Cladistics (character state based)
Lowe, Harris, and Ashton 2004
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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
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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
)!3(2
)!52(3
n
nU
nN nnN Unn
nR )32(
)!2(2
)!32(2
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7
98
9
9
99
5
8
10
11
11Felsenstein 2004
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
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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
Lowe, Harris, and Ashton 2004
Felsenstein 2004
E AC B
D F
60
60 60
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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
MigrationsPopulation subdivisionsBottlenecks/Founder Effects
Can provide insights on current relationships among populations
Connectedness of populationsEffects of landscape features on gene flow
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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
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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
Avise 2004
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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
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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
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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
ACB
Gene Tree
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a b c
Concordant Gene Treeb is closer to a than to c
Failure to coalesce within species lineages drives divergence of relationships between gene and species trees
Gene Trees vs Species Trees
a b c
Divergent Gene Tree:b is closer to c than to a
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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
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present
Time
Individual alleles
9 generations in the history of a population of 14 gene copies
Slide courtesy of Yoav Gilad
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How to model this process?
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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
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Alternative: Start at the end and work your way back
present
Most recent common ancestor (MRCA)
Time
Individual allelesSlide courtesy of Yoav Gilad
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present
Most recent common ancestor (MRCA)
The genealogy of a sample of 5 gene copies
Time
individualsSlide courtesy of Yoav Gilad
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present
Most recent common ancestor (MRCA)
The genealogy of a sample of 5 gene copies
Time
Individual allelesSlide courtesy of Yoav Gilad
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Examples of coalescent trees for a sample of 6
Time
Individual allelesSlide courtesy of Yoav Gilad
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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
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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
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Probability of Coalescence
For any two lineages, function of population size
eecoalescenc N
P2
1
Also a function of number of lineages
eecoalescenc N
kkP
2
1
2
)1(
where k is number of lineages
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Probability of Coalescence
Probability declines over time
Lineages decrease in number
Can be estimated based on negative exponential
where k is number of lineages
eN
kkt
ecoalescenc eP 2
1
2
)1(
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Time to Coalescence Affected by Population History
Bottleneck
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Time to Coalescence Affected by Population History
Population Growth
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Time to Coalescence Affected by Population Structure
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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)