|
By David F. Salisbury
June 27, 2001
Now
that the human genome has been mapped, one of the biggest challenges
facing human geneticists is identifying groups of genes that collectively
conspire to make some individuals particularly susceptible to a
number of common diseases, including breast cancer, cardiovascular
disease and depression.
Disentangling genetic predisposition
from environmental factors is a highly complex process. So far geneticists
have been able to do so only for a limited number of cases like
cystic fibrosis that are caused by mutations in a single gene. Where
more than two genes are involved, however, traditional methods of
analysis have floundered because it has proven impractical to acquire
genetic information from the large number of subjects required.
Now, however, a group of researchers
at the Vanderbilt University Medical Center and the Vanderbilt-Ingram
Cancer Center report that they have developed an alternative statistical
approach to this problem. The technique, called Multifactor
Dimensionality Reduction, can identify multiple gene interactions
using data from a reasonable number of patients. Writing in the
July issue of the American
Journal of Human Genetics, the researchers report that they
have used this technique successfully to identify four DNA sequence
variations in three genes that work together to heighten a womans
risk of breast cancer.
"For some time we have known that
a person's susceptibility to a number of common, complex diseases
is not determined by a single gene, but by a number of genes working
together," says Jason H. Moore, assistant professor of molecular
physiology and biophysics, who led the research effort. "But,
to the best of our knowledge, this is the first time that such a
multiple-gene interaction has been identified."
Co-author and Professor of Pathology
Fritz Parl, who has been studying the relation between estrogen
and breast cancer for a number of years, predicts that this new
approach will be widely used to study multiple-gene risk factors.
When such an analysis is expanded to take non-genetic risk factors
into account, it should significantly improve a doctors ability
to determine the risk that certain treatments, like hormone replacement
therapy, represent for individual patients, he says.
Most genes harbor common DNA sequence
variations called polymorphisms and rare DNA sequence variations
called mutations. Some mutations single-handedly increase an individuals
susceptibility to specific diseases. Such "genetic" diseases
are recognizable because they are heritable and so cluster in certain
families. An example is hereditary breast cancer, which accounts
for less than 10 percent of all breast cancer cases. It is highly
associated with the action of mutations in one of two genes.
On the other hand, most common diseases
do not exhibit a clear pattern of heritability. So geneticists argue
that the observed variations in susceptibility must be caused by
the interactions among multiple polymorphisms. In such cases, individual
polymorphisms are harmless, but when they occur in a specific combination
they significantly enhance a person's risk. In some cases, the increased
susceptibility may be due to the collective action of a few polymorphisms,
but in others they may arise from the subtle interactions among
hundreds of gene-variants.
Take the case of the polymorphisms
that the Vanderbilt group has linked with the sporadic breast cancers
that occur in women with no family history of the disease and account
for more than 90 percent of all breast cancer cases.
The researchers began by looking at
five genes involved in estrogen metabolism. They chose this particular
set of genes because there is considerable evidence that estrogens
influence breast cancer risk and recent studies have shown that
the enzymes that break down estrogen in the body produce metabolites
that can cause cancer.
Moore, graduate student Marylyn Ritchie
and programmer Lance Hahn analyzed 10 functional polymorphisms that
alter the levels of the suspect estrogen metabolites. When they
looked at the polymorphisms individually, he and his colleagues
found no indication of increased cancer risk. It was only when they
looked at different combinations that they found that women with
four specific polymorphisms were significantly more likely to develop
breast cancer than those with only three, two or one of these gene
variants.
They found this complex association
by applying their technique to the genetic information that Parl
had compiled on 200 women with breast cancer and an age-matched
group of 200 female patients without the illness. First, they constructed
a series of tables that compare pairs of polymorphisms. Because
a person can inherit a given polymorphism from her father, mother
or both parents, each table has nine cells. In each cell, they calculate
the number of subjects with breast cancer and the number of control
subjects without the illness who have the indicated pair of polymorphisms.
If the number with cancer is higher, then the cell is listed as
high risk; if the number without cancer is higher, then it is considered
low risk. If none of the subjects have a particular combination
of the two polymorphisms, the cell is labeled as empty.
The researchers constructed these tables
for all possible pairs of polymorphisms. Then they constructed similar
tables for all combinations of three, four, five all the way up
to nine polymorphisms. By examining the pattern of high and low
risks in these tables, the geneticists determined that only one
combination, four polymorphisms in three genes, increased an individual's
cancer risk.
Moore's approach isn't limited to analyzing
interactions among three or four genes. Using machine learning algorithms
and a new multi-processor supercomputer on campus, he estimates
that they will be able to search for similar interactions among
as many as 20 genes selected from a list of thousands of candidates.
Other members of the research team
are technician Nady Roodi and analyst L. Renee Bailey who now works
for AstraZeneca in Wilmington, DE.
The research was funded by the National
Institutes of Health, the Vanderbilt-Ingram Cancer Center and the
Vanderbilt University Medical Center. With the help of Vanderbilt's
Office of Technology Transfer Moore has decided to make a Multifactor-Dimensionality
Reduction software package freely available to academic users. More
information is available online at http://phg.mc.vanderbilt.edu/Software/MDR.
The paper is available online at
http://www.journals.uchicago.edu/AJHG/journal/issues/v69n1/012797/brief/012797.abstract.html .
| 
