Transcript Lecture 7-POSTED-BISC441-2012

What is phylogenetic analysis and why
should we perform it?
Phylogenetic analysis has two major components:
(1) Phylogeny inference or “tree building”
the inference of the branching orders, and
ultimately the evolutionary relationships,
between “taxa” (entities such as genes,
populations, species, etc.)
(2) Analyzing change in traits (phenotypes, genes)
using phylogenies as analytical frameworks
for rigorous understanding of the evolution of
various traits or conditions of interest
Germline and somatic evolution included!
Uses of Phylogenetics in the Study of
Health & Disease
(1) Evolutionary history of humans, between and within
species
(2) Analysis of evolution of phenotypic and genetic traits in
humans, especially human-specific traits - evolved when,
where, why, how
(3) Evolution of parasites and pathogens, in relation to their
hosts (us)
(4) Evolution of cancer cell lineages, and somatic evolution
more generally.
(5) Study of adaptation in humans and other taxa
What you will learn in this lecture
(1) About phylogenies, terminology, what they are,
how they work, ‘tree thinking’
(2) How to infer phylogenies
(3) How we can use phylogenies to answer questions
related to human adaptation, health and disease
Common Phylogenetic Tree Terminology
Terminal Nodes
Branches or
Lineages
A
B
C
D
Ancestral Node
or ROOT of
the Tree
Internal Nodes or
Divergence Points
(represent hypothetical
ancestors of the taxa)
E
Represent the
TAXA (genes,
populations,
species, etc.)
used to infer
the phylogeny
Phylogenetic trees diagram the evolutionary
relationships between the taxa
Taxon B
Taxon C
Taxon A
Taxon D
No meaning to the
spacing between the
taxa, or to the order in
which they appear from
top to bottom.
Taxon E
This dimension either can have no scale (for ‘cladograms’),
can be proportional to genetic distance or amount of change
(for ‘phylograms’ or ‘additive trees’), or can be proportional
to time (for ‘ultrametric trees’ or true evolutionary trees).
((A,(B,C)),(D,E)) = The above phylogeny as nested parentheses
These say that B and C are more closely related to each other than either is to A,
and that A, B, and C form a clade that is a sister group to the clade composed of
D and E. If the tree has a time scale, then D and E are the most closely related.
Three types of trees
Cladogram
Phylogram
6
Taxon B
Taxon C
Taxon A
Taxon D
no
meaning
1
1
3
1
5
Ultrametric tree
Taxon B
Taxon B
Taxon C
Taxon C
Taxon A
Taxon A
Taxon D
Taxon D
genetic
change
time
All show the same evolutionary relationships, or branching orders, between the taxa.
A major goal of phylogeny inference is to resolve the
branching orders of lineages in evolutionary trees:
Completely unresolved
or "star" phylogeny
Partially resolved
phylogeny
A
A
A
B
C
E
C
E
C
D
B
B
E
D
D
Polytomy or multifurcation
Fully resolved,
bifurcating phylogeny
A bifurcation
RESOLUTION AND SUPPORT for nodes
There are three possible unrooted trees
for four taxa (A, B, C, D)
Tree 1
Tree 2
Tree 3
A
C
A
B
A
B
B
D
C
D
D
C
Phylogenetic tree building (or inference) methods are aimed at
discovering which of the possible unrooted trees is "correct".
We would like this to be the “true” biological tree — that is, one
that accurately represents the evolutionary history of the taxa.
However, we must settle for discovering the computationally
correct or optimal tree for the phylogenetic method of choice.
The number of unrooted trees increases in a greater
than exponential manner with number of taxa
A
# Taxa ( N)
B
C
A
B
C
A
C
B
D
D
E
A
B
C
F
D
E
3
4
5
6
7
8
9
10
.
.
.
.
30
# Unrooted trees
1
3
15
105
945
10,935
135,135
2,027,025
.
.
.
.
Å3.58 x 10
36
(2N - 5)!! = # unrooted trees for N taxa
Inferring evolutionary relationships between
the taxa requires rooting the tree:
B
To root a tree mentally,
imagine that the tree is
made of string. Grab the
string at the root and
tug on it until the ends of
the string (the taxa) fall
opposite the root:
Root
D
Unrooted tree
A
A
Note that in this rooted tree, taxon A is
no more closely related to taxon B than
it is to C or D.
C
B
C
D
Rooted tree
Root
TIME
Now, try it again with the root at another position:
B
C
Root
Unrooted tree
D
A
A
B
C
D
Rooted tree
TIME
Root
Note that in this rooted tree, taxon A is most
closely related to taxon B, and together they
are equally distantly related to taxa C and D.
An unrooted, four-taxon tree theoretically can be rooted in five
different places to produce five different rooted trees
A
The unrooted tree 1:
4
1
B
Rooted tree 1a
2
Rooted tree 1b
C
5
D
3
Rooted tree 1c
Rooted tree 1d
Rooted tree 1e
B
A
A
C
D
A
B
B
D
C
C
C
C
A
A
D
D
D
B
B
These trees show five different evolutionary relationships among the taxa!
All of these rearrangements show the same evolutionary
relationships between the taxa
Rooted tree 1a
B
A
C
D
A
C
A
D
D
C
B
B
C
D
D
C
A
A
B
B
B
B
C
D
D
A
C
A
Main way to root trees:
By outgroup:
Uses taxa (the “outgroup”) that are
known to fall outside of the group of
interest (the “ingroup”). Requires
some prior knowledge about the
relationships among the taxa.
outgroup
Molecular phylogenetic tree building methods:
Are mathematical and/or statistical methods for inferring the divergence
order of taxa, as well as the lengths of the branches that connect them.
There are many phylogenetic methods available today, each having
strengths and weaknesses. Most can be classified as follows:
COMPUTATIONAL METHOD
Characters
Distances
DATA TYPE
Optimality criterion
Clustering algorithm
PARSIMONY
MAXIMUM LIKELIHOOD
MINIMUM EVOLUTION
UPGMA
LEAST SQUARES
NEIGHBOR-JOINING
Types of data used in phylogenetic inference:
Character-based methods: Use the aligned characters, such as DNA
or protein sequences, directly during tree inference.
Taxa
Species
Species
Species
Species
Species
ETC
A
B
C
D
E
Characters
ATCGCTAGTCCTATAGTGCA
ATCGCTAGTCCTATATTGCA
TTCGCTAGACCTGTGGTCCA
TTGACCAGACCTGTGGTCCG
TTGACCAGTTCTGTGGTCCG ETC
Similarity vs. Evolutionary Relationship:
Similarity and relationship are not the same thing, even though
evolutionary relationship is inferred from certain types of similarity.
Similar: having likeness or resemblance (an observation)
Related: genetically connected (an historical fact)
Two taxa can be most similar without being most closely-related:
6
1
1
3
1
5
Taxon C
Taxon A
Taxon D
Taxon B (eg HUMANS!)
C is more similar in sequence
to A (d = 3) than to B (d = 7),
but C and B are most closely
related (that is, C and B shared
a common ancestor more recently
than either did with A).
Main computational approach:
Optimality approaches:
Use either character or distance data.
First define an optimality criterion (minimum branch lengths, fewest
number of events, highest likelihood), and then use a specific algorithm
for finding trees with the best value for the objective function. Can
identify many equally optimal trees, if such exist.
Warning: Finding an optimal tree is not necessarily the same as finding
the "true” tree. Random data will give you an ‘optimal’ (best ) tree!
Parsimony methods:
Optimality criterion: The ‘most-parsimonious’ tree is the one that
requires the fewest number of evolutionary events (e.g., nucleotide
substitutions, amino acid replacements) to explain the sequences.
Advantages:
• Are simple, intuitive, and logical (many possible by ‘pencil-and-paper’).
• Can be used on molecular and non-molecular (e.g., morphological) data.
• Can be used for character (can infer the exact substitutions) and rate analysis.
• Can be used to infer the sequences of the extinct (hypothetical) ancestors.
Disadvantages:
• Not explicitly statistical
• Can be fooled by high levels of parallel evolution
Use parsimony to infer the optimal (best) tree
Character-based methods: Use the aligned characters, such as DNA
or protein sequences, directly during tree inference.
Taxa
Species
Species
Species
Species
Species
A
B
C
D
E
OUTGROUP
ATCG
ATCG
TTCG
TTGA
TTGA
Characters
CTAGACCTATAGTGCA
CTAGACCTATATTGCA
CTAGACCTGTGGTCCA
CCAGACCTGTGGTCCG
CCAGTTGTGTGGTCCG
TTAC
CCATTTGTGTCCTCCG
Infer maximum parsimony tree using first four characters
Quality of trees (how likely it is that they reflect the one True
Tree) can be evaluated in various ways (random data will give you a
low-quality ‘best’ tree)
We can Statistically Compare alternative trees,
corresponding to specific biological hypotheses
of the history of some set of lineages
Time scales on trees:
molecular clocks
% genetic divergence
Why such different
profiles? Variation in
mutation rate?
100%
Fibrinopeptides
75%
Hemoglobin
50%
25%
Variation in selection.
Genes coding for some
molecules under very
strong stabilizing selection.
Cytochrome c
Histone IV
300
600
900
1200
Time since divergence (Myr)
1500
Dates for calibrating molecular clocks can come from geology,
fossils, or historical data
From known ages
of islands, for two genes
Calibrating using fossil data
chimps
6 substitutions
humans
whales
60 substitutions
hippos
56 mya
Calibrating from known dates of the ages of samples:
for very fast-evolving
taxa such as HIV
Uses of Phylogenetics in the Study of
Health & Disease
(1) Evolutionary history of humans, between and
within species
(2) Analysis of evolution of phenotypic and genetic
traits in humans, especially human-specific
traits - evolved when, where, why, how
(3) Taxonomy and evolution of parasites and
pathogens, and evolution in relation to their
hosts
(4) Evolution of cancer cell lineages, and somatic
evolution more generally.
(5) Study of adaptation in humans and other taxa,
via analysis of divergence and convergence
EMERGING VIRUSES - THE GREATEST KNOWN HEALTH THREAT TO HUMANITY
VIRUS - what IS it?
Sequence it’s DNA and relate
sequence to known viruses
Evolution of SIV and HIV viruses:
multiple transfers to humans, from
chimps and from green monkeys
SARS (severe acute respiratory syndrome)
what causes it and where did it come from?
HIV phylogeny
within humans in
different regions:
Haiti as stepping
stone to
North America
HIV evolves very
rapidly WITHIN hosts,
as a result of interactions
with the immune system
Can do phylogenetics:
-Pathogens within individuals,
-Pathogens between
Individuals (eg in different
or same regions)
How originate?
From other species?
How spread?
How does resistance to
Antibiotics evolve in pathogens,
& resistance to chemotherapeutic
agents evolve in cancer?
Cancer evolves
genetically
in the body during
carcinogenesis,
allowing the inference
of ‘oncogenetic trees’
Cytogenetic data:
Gains and losses of
Chromosomal regions
During evolution of cancers;
Lose tumor suppressor
gene copies, gain
Oncogene copies
Involves losses of
heterozygosity
and losses of imprinting
Cancer
Evolutionary
Phylogenomics
Compare
primary cancer
with metastatic
tumors
What you learned in this lecture
(1) About phylogenies, terminology, what they are,
how they work, ‘tree thinking’
(2) How to infer and evaluate phylogenies
(3) How to use phylogenies to answer questions
related to human adaptation, health
and disease (viruses, cancer, etc)
(4) How to THINK in terms of evolutionary trees
(historical patterns of evolution), within and between species