Lecture 17 phylogenetics and phylogeography
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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

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Presentation Transcript

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
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!



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


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


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


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


Phylogeography
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


Phylogeography1
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

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


Problems with using phylogenetics for inferring evolution
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

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


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


Coalescence
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


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

Time

present

Individual alleles

Slide courtesy of Yoav Gilad



Modeling from Theoretical Ancestors: Forward Evolution copies

  • 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 copies

Most recent common ancestor (MRCA)

Time

present

Individual alleles

Slide courtesy of Yoav Gilad


The genealogy of a sample of 5 gene copies copies

Most recent common ancestor (MRCA)

Time

present

individuals

Slide courtesy of Yoav Gilad


The genealogy of a sample of 5 gene copies copies

Most recent common ancestor (MRCA)

Time

present

Individual alleles

Slide courtesy of Yoav Gilad


Examples of coalescent trees for a sample of 6
Examples of coalescent trees for a sample of 6 copies

Time

Individual alleles

Slide courtesy of Yoav Gilad


Coalescence advantages
Coalescence Advantages copies

  • 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
Coalescent Tree Example copies

  • 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
Probability of Coalescence copies

  • For any two lineages, function of population size

  • Also a function of number of lineages

where k is number of lineages


Probability of coalescence1
Probability of Coalescence copies

  • Probability declines over time

    • Lineages decrease in number

  • Can be estimated based on negative exponential

where k is number of lineages





Applications of the coalescent approach
Applications of the Coalescent Approach copies

  • Framework for efficiently testing alternative models for evolution

  • Inferences about effective population size

  • Detection of population structure

  • Signatures of selection (coming attraction)


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