Using Statistics to Decipher Secrets of DNA Natural Mutation

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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, 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 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.

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