Lecture 17: Phylogenetics and Phylogeography

October 22, 2012

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!

Last Time

Using FST to estimate migration

Direct estimates of migration: parentage analysis

Introduction to phylogenetic analysis

Today

Phylogeography

Limitations of phylogenetic analysis

Coalescence introduction

Influence of demography on coalescence time

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!

Phenetics (distance) vs Cladistics (character state based)

Lowe, Harris, and Ashton 2004

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

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

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nN nnN Unn

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7

98

9

9

99

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8

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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

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

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

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

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

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

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

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

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

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

present

Time

Individual alleles

9 generations in the history of a population of 14 gene copies

Slide courtesy of Yoav Gilad

How to model this process?

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

Alternative: Start at the end and work your way back

present

Most recent common ancestor (MRCA)

Time

Individual allelesSlide courtesy of Yoav Gilad

present

Most recent common ancestor (MRCA)

The genealogy of a sample of 5 gene copies

Time

individualsSlide courtesy of Yoav Gilad

present

Most recent common ancestor (MRCA)

The genealogy of a sample of 5 gene copies

Time

Individual allelesSlide courtesy of Yoav Gilad

Examples of coalescent trees for a sample of 6

Time

Individual allelesSlide courtesy of Yoav Gilad

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

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

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

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(

Time to Coalescence Affected by Population History

Bottleneck

Time to Coalescence Affected by Population History

Population Growth

Time to Coalescence Affected by Population Structure

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)