
Aim in building a phylogenetic tree is to
use a knowledge of the characters of
organisms to build a tree that reflects the
relationships between them.

Organisms with many characters in
common are more likely to be related
than those with few in common.
We want to use characters that are
homologous [shared because of
common ancestry] rather than
analagous [independently evolved].
 But how is this to be done?
 Turns out that there are many
approaches the first of which is to apply
parsimony.


The basic idea of parsimony in tree
building is to build a tree that requires
the fewest evolutionary changes in its
construction.

In the following trees one species differs
from the other three. In each tree a
single evolutionary change is all that is
required to build it.

Similarly, we can [in the next slide]
analyze a situation where two non-sister
taxa (3&4) share a trait.

There are two equally likely explanations
in this case.

The same logic applies when dealing
with multiple traits (3 traits each with two
states in the next example).

Each trait is treated separately and the
most parsimonious explanation is
calculated.

When the data are pooled a total of five
changes are present on the tree.

Its turns out that the tree we just dealt
with is not the most parsimonious tree.

It is possible to build a tree that has only
three changes [it is impossible to have
fewer than three changes].
In the previous example it was easy to
see the minimum number of changes
needed to make a most parsimonious
tree.
 For larger trees this is not so simple to do.
 The Fitch algorithm can be used to figure
the minimum number of changes
necessary for a given tree.


The Fitch algorithm begins at the branch
tips of a tree and proceeds towards the
base of the tree.

A running count is kept of the number of
the character changes needed.

As we proceed down the tree each
internal node is assigned one or more
character states.

Two rules are used to assign character
states at nodes.
Rule 1. If the two daughters of a node
share no stated in common we assign to
the node all possible states for both
daughters.
 In other words the set of possible traits at
the node is the union of the sets of
possible traits for daughters 1 and 2.
 In this case we increase the tally of
character changes by one.

Rule 2. If the daughters of a node share
one or more possible states of a trait
then we assign the shared states to the
node.
 In other words we assign the intersection
of the sets of possible states for each
daughter to the node.
 In this case we do not increase the tally
of character changes.

The Fitch algorithm just tells us the
minimum number of changes needed
for a given tree.
 It does not tell us if a different tree would
have fewer.
 In order to compare different trees to
find the most parsimonious we would
have to repeat the Fitch process for all
the trees.


Another approach to building
phylogenetic trees is to use distance
methods.

In this approach pairwise distances,
(where distance is a measure of
morphological or genetic differences
between species) are calculated and
used in tree construction.

Distances can be:
› Counts of the number of character
differences between species.
› Based on morphological measurements
› In living species most commonly counts of
base pair differences in DNA sequences or
amino acid differences coded for are used
to build trees.

Because insertion/deletion mutations
occur and can shift the reading frame of
a length of DNA sometimes sequences
need to be aligned before using them to
build a phylogenetic tree.

Once distance measures have been
calculated the pairwise measures
(differences between individual pairs of
species) are arranged into a distance
matrix.

Once distance measures are tabulated
we need to figure out how to arrange
these data on a tree and decide how
long to make the branches.

For four species there is only one basic
tree shape and only three pairwise
species arrangements.

There are multiple statistical procedures
that can be used to construct trees using
distance data. The details of these are
beyond the scope of this class.

However, the aim of all of them is to find
a tree topology (or structure) in which
each pairwise distance in the tree is as
close as possible to that in the data
matrix.

One philosophical objection to trees built
using distance methods is that they don’t
explicitly incorporate underlying
evolutionary relationships.

They are similarity measures (and assume
that similarity reflects homology), but
analagous traits may sometimes be
used.

We have spent a lot of time looking at
ways of assessing how well trees are
supported by data.

However, the big challenge in building
phylogenies is in identifying potentially
useful trees from the huge number of
potential trees

It turns out that the number of potential
phylogenetic trees increases
exponentially with the number of taxa in
the tree.

The challenge for phylogenticists who
cannot search every possible tree is to
develop strategies to search only for
plausible trees.

Very computer intensive algorithms are
used to do this, but the underlying
methodologies are beyond the scope of
this class.

Phylogenetic trees are hypotheses about
the relationships between taxa.

Once a tree is constructed how much
confidence can we have that the tree
(or some part of it) is correct?

This is an issue of statistical confidence.

There are a number of techniques that
scientists have developed to measure
how well the data support a given tree.

One of the most widely used is bootstrap
resampling.

Bootstrap resampling is based on the idea
that the data set that the phylogeny is
based on is itself only one possible set of
data that the tree could have been built
with.

How sensitive is the tree’s structure to the
set of data we used? If we had used a
similar but not identical set of data would
we have produced the same tree?

To carry out a bootstrap analysis we
simply resample from our original
character matrix.

We randomly pick sets of traits with
replacement from our data set and the
new data matrix is used to build a
phylogenetic tree. That tree is then
compared to the original tree.

After repeated bootstrap resamplings
we see how often the new trees match
the original tree.

If resampled trees match the original
tree 90% of the time we say the tree has
90% bootstrap support.

For a considerable period of time before
widespread genomic analysis there was
controversy about whether the closest
relatives of the eutherian (or placental)
mammals were the marsupials or the
monotremes.

In 2001 Killian et al. sequenced a large
nuclear gene from 11 species of
placental mammal, two marsupials and
two monotremes.

Using the sequence data they
constructed a phylogeny of the
mammals that indicated the placental
and marsupial mammals were sister
groups.

To check how strongly their data
supported the monophyly of the
placental and marsupial mammals Killian
et al. carried out a bootstrap resampling
analysis of their data.

The results showed that the marsupials
and placental mammals formed a
monophyletic clade in 100% of the trees.
The bootstrap analysis thus indicated
that strongly supported for this data set
the monophyly of the placental and
marsupial mammals.
 Since Killian’s paper numerous other
studies of nuclear DNA have supported
this conclusion.
