August 03, 2004
Using Statistics to Decipher Secrets of Natural Mutation
A new mathematical approach for analyzing the complex, subtle
patterns of natural mutation in DNA is likely to help biologists
understand how mutation contributes to evolutionary change in
mammals.
The researchers, Howard Hughes Medical Institute investigator Philip
Green and his student Dick Hwang, published a report describing the
first applications of their new analytical approach in the August 3,
2004, online early edition of the Proceedings of the National
Academy of Sciences. Both Hwang and Green are at the University of
Washington in Seattle.
“Understanding naturally occurring mutations has been of great interest because mutations are major drivers of evolution.”
Philip Green
“Understanding naturally occurring mutations has been of great
interest because mutations are major drivers of evolution,” said
Green. “However, it's surprising how little is still known about
their causes.”
Previous studies have revealed a number of biases in the rates of
different types of mutational change. These arise in part from the
innate biochemical characteristics of the four DNA nucleotide bases -
adenine, guanine, cytosine and thymine - that affect their
vulnerability to modification and the accuracy with which they are
replicated when cells divide. Particular nucleotide sequences, for
example, cytosine-guanine (CpG) dinucleotides, form
“hotspots” - regions that are particularly vulnerable to
alterations that convert one nucleotide to another, causing
mutations.
To understand these biases, Hwang and Green sought to develop a
flexible approach to analyze the process of “neutral DNA
evolution” in regions of the genome thought to lack genes and
other functionally important sequences. “If you want to get an
unvarnished picture of the mutation process itself, uncorrupted by
natural selection, you want to look at neutrally evolving DNA,”
said Green. “Mutations in DNA that is not functional should
better represent the complete spectrum of naturally occurring
mutations. Mutations are of course also occurring in the genes and
those are of interest because they can create new phenotypes and cause
variation among traits. Some of those mutations are advantageous and
consequently quickly spread through the species, while others are
deleterious and are weeded out. So genes and other features don't
evolve at neutral rates.”
“Apart from their intrinsic interest, we think understanding
the underlying mutation patterns better will also help us in finding
the functionally important features in the genome. Basically, it's a
signal-to-noise issue, where the naturally occurring mutations are the
`noise' and the functional parts of the genome are the `signal.' The
better we understand the noise, the better job we can do of
understanding the signals.”
To begin to understand the patterns of DNA changes that result from
neutral mutation, Hwang and Green developed a new version of a powerful
statistical technique that they call “Bayesian Markov chain Monte
Carlo sequence analysis.” Basically, the technique enables them
to feed in sequence information from genomes of different organisms and
discern patterns that can distinguish models of mutational
mechanisms.
According to Green, the statistical approach offers an effective way
to analyze models that are very difficult or impossible to solve
analytically. “Until recently,” he said, “the state
of the art in the molecular evolution field was to use models that
people knew were gross over-simplifications, but had the merit that you
could solve them analytically. Without doing too much computation, you
could make estimates of mutation rates of various sorts. However, the
cost of that simplified approach was a model that is
unrealistic.”
In particular, he said, the standard model treated all positions in
the sequence as evolving independently of each other, rather than
taking into account context effects, in which the identity of
neighboring nucleotides influences the nature and rate of
mutations.
“While a few other investigators have been working on how to
take into account context effects, I think we are doing it in a more
rigorous, more complete way,” said Green. Without such a rigorous
approach, he said, models of evolution could give erroneous results
regarding the effects of mutation.
“I think the more realistic you can make the model, the less
likely you are to be led astray by drawing conclusions that really had
more to do with the deficiencies of your model than with the underlying
reality,” said Green.
Hwang and Green tested their analytical approach by using it to
compare the sequences of corresponding genome segments from 19
mammalian species, including human, horse, lemur, rat, rabbit, hedgehog
and armadillo. Such comparisons among species across the mammalian
evolutionary tree can provide insight into how mutational patterns have
changed over evolutionary time.
They focused their analysis on a 1.7 million base-pair DNA segment
known as the “greater cystic fibrosis transmembrane conductance
regulator region,” which was sequenced in the 19 mammals by Eric
Green and his colleagues at the National Human Genome Research
Institute. To concentrate on the neutrally evolving DNA, Hwang and Phil
Green excluded the genes from those segments and compared what was
left.
According to Green, the comparison of context-dependent mutation in
the segments across the species revealed that the CpG mutations, unlike
other mutation types, accumulated in a regular clock-like fashion. The
analysis also distinguished other sources of naturally occurring
mutations and their variation due to biological and biochemical
influences, and appears to offer some insight into factors such as
generation time and population size that have varied in mammalian
evolution.
Green concluded that by contributing to a better understanding of
naturally occurring mutations, the technique would help in
understanding both how genetic disease arises and how evolution has
occurred.
A next step, he said, will be to extend the analysis to sites on the
genome that are not evolving neutrally. This should help identify
genomic regions that were not previously recognized to be of functional
importance, said Green. Also, he said, such analyses could offer
considerable insight into how patterns of natural selection have varied
across different species in the course of evolution.
“A more complex model of the neutral process should start to
pay for itself in exploring these phenomena, because you're frequently
looking for relatively subtle effects,” said Green.
|
