phylogenyGENE 3000

• why is coalescent theory important for understanding phylogenetics (species trees)?

•coalescent theory lets us test our assumptions of how DNA sequences evolve before we use them to reconstruct phylogeny

•coalescent theory explains why recently-diverged populations may not yet have synapomorphies despite already being on different evolutionary paths

• this model gives us basis for estimating time to ancestor of any two sequences, within a population or between species

• DNA characters are just like phenotypic characters

• 4 character states A,C,T,G plus information in insertion-deletion, gene copy number, etc.

• same concerns of homology and shared descent apply

human population isolated ~200kya

• “mitochondrial Eve” sets up misunderstanding

•every locus sampled now has a point in the past where all current alleles coalesce to a common ancestor

• in recently diverged species, diversity is often older than the species

understanding coalescence1. larger effective size (Ne), more diversity

2. when time between branching events shortrelative to Ne, more likely that allelic diversity is older

than branching event

Ne

isolation

isolation

"This coalescence does not mean that the population originally consisted of a single individual with that ancestral

allele. It just means that particular individual’s allele was the one that, out of all the alleles present at that time, later

became fixed in the population."

"This coalescence does not mean that the population originally consisted of a single

individual with that ancestral allele. It just means that particular individual’s allele was the one that,

out of all the alleles present at that time, later became fixed in the population."

National Geographic OCT. 2013

phylogeny inference

•2 basic approaches: algorithm vs. criterion

•“neighbor joining” shown in book is an algorithm that generates a single tree by finding shortest “distances” (proportion of differences at nucleotide sites)

•algorithm approaches do not help identify our uncertainty: one answer comes out, whether well supported or not

criterion-based phylogeny

30 tips results in 8.7 x 1036 possible treescomputer search necessary

A phylogeny is a hypothesis.

3 of >10,000possible treeswhich fits data

best?depends on the

criterion

3 of >10,000possible treeswhich fits data

best?depends on the

criterion

11 changes 11 changes

7 changes = most parsimonious of these

3

Take out paper...

•Quick. Draw a phylogeny with 7 tips.

•Without thinking add: baboon, hamster, mouse, chicken, human, cow, sheep as randomly as possible

(Not this, draw your own. With 7 tips.)

Just scoring theseCharacters in the most

parsimonious wayWhat score do you

get?

criteria used in phylogeny

• parsimony - the fewest # of changes indicates the most acceptable tree topology

• maximum likelihood - both topology (arrangement of branches) and branch lengths are iteratively searched for tree(s) that fit statistical model of molecular evolution (e.g. transitions > transversions)

• Bayesian - criterion is still maximum likelihood, search strategy is different (sums result over many similar-likelihood trees)

green fluorescent protein has evolved to be more than

green

the nucleotides are not what we are interested in. we are interested in how traitsthat affect fitness, ecology, speciation, performance, evolve along a phylogeny

why different criteria?

1.we are making our assumptions explicit for inference of the unknown

2.different scientists have different backgrounds that drive their assumptions

3.using multiple methods/criteria lets us test how safe our assumptions are

are your data sufficient?

•all of these methods will find a tree: whether algorithm or criterion-based search

•is it one you can believe is better than random? is it one you would put your name behind?

•bootstrapping, and consensus methods

bootstrapping

•text: “statistical method for estimating the strength of evidence that a particular node in a phylogeny exists”

•more general: resampling technique used to obtain estimates of ... parameters and accuracy/variance around those parameters

•observed data: is a particular subset of the data driving the result of our analysis?

mean vs. median•1,2,2,3,3,3,4,4,4,4,5,5,5,6,6,15

•mean is 4.5... median is 4. you can change 15 to 150 and mean goes up but median doesn’t change, more robust

•random resample of data with replacement (means same data entry can be used multiple times) can identify true tendency of data, help ignore ‘outliers’

higher bootstrap proportions better!

this valueis % of “pseudo”replicates thatdivide tree in

same way (represents data tendency to

support node)

consensus tree• can also ask equally-

supported trees (equally parsimonious, equal likelihood) how well they all support same nodes

• doesn’t have to involve subset of data like in bootstrap

• may summarize the stable parts of tree across 2+ trees

ba c d e ba c d e

ba c d e

support for the method

•do we believe phylogeny reconstruction works? need to test it against a known history

•(fish(salamander(bird(mouse,human)) we feel pretty strongly about

•experimental phylogenetics uses virus evolution to go one step further

experimental evolution

growing T7 phage

on E. coli plates; speed up mutation

process by adding mutagen

40generations

40generations

40generations

experimental evolution

•so phylogeny is known, and ancestral strains can be kept in freezer

•sequence part of DNA and use parsimony, likelihood, and other approaches

•consistently got the right (TRUE) answer!

•can also track “traits” on this tree, e.g. changes in growth rate and plaque size on E. coli plates (and check against actual ancestors)

# DNA # DNA mutations mutations

on this on this branchbranch

Text: “Because constructing phylogenies, and science more broadly, is often a process of evaluating evidence, scientists often test the effectiveness of the methodologies used to draw conclusions.”

well-supported phylogeny of rabies virus

lineages, coded by host bat

species

For RNA viruses, rapid viral evolution and the biological similarity of closely related host species have been proposed as key determinants of the occurrence and long-term outcome of cross-species transmission. Using a data set of hundreds of rabies viruses sampled from 23 North American bat species, we present a general framework to quantify per capita rates of cross-species transmission and reconstruct historical patterns of viral

establishment in new host species using molecular sequence data. These estimates demonstrate diminishing frequencies of both cross-species transmission and host shifts with increasing phylogenetic distance between bat species .

Evolutionary constraints on viral host range indicate that host species barriers may trump the intrinsic mutability of RNA viruses in determining the fate of emerging host-virus interactions.

analysisindicates

rate of virusjumping from

one host to another

so this study requires TWO phylogenies (virus

and bats)CST: cross-species transmission