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The researchers gathered
data from large-scale genome-wide association studies to identify genetic
variants associated with each disease, and combined information from all of the
variants into a single "polygenic risk test." Credit: Lauren Solomon
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A research team reports a new kind of genome
analysis that could identify large fractions of the population who have a much
higher risk of developing serious common diseases, including coronary artery
disease, breast cancer, or type 2 diabetes. These tests, which use information
from millions of places in the genome to ascertain risk for five diseases, can
flag greater likelihood of developing the potentially fatal conditions well
before any symptoms appear.
A research team at the Broad Institute of MIT and
Harvard, Massachusetts General Hospital (MGH), and Harvard Medical School
reports a new kind of genome analysis that could identify large fractions of
the population who have a much higher risk of developing serious common
diseases, including coronary artery disease, breast cancer, or type 2 diabetes.
These tests, which use information from millions
of places in the genome to ascertain risk for five diseases, can flag greater
likelihood of developing the potentially fatal conditions well before any
symptoms appear. While the study was conducted with data from the UK, it
suggests that up to 25 million people in the US may be at more than triple the
normal risk for coronary artery disease, and millions more may be at similar
elevated risk for the other conditions, based on genetic variation alone. The
genomic information could allow physicians to focus particular attention on
these individuals, perhaps enabling early interventions to prevent disease.
The research raises important questions about how
this method, called polygenic risk scoring, should be further developed and
used in the medical system. In addition, the authors note that the genetic
tests are largely based on information from individuals of European descent,
and the results underscore the need for larger studies of other ethnic groups
to ensure equity. The study appears in Nature
Genetics.
"We've known for long time that there are
people out there at high risk for disease based just on their overall genetic
variation," said senior author Sekar Kathiresan, an institute member and
director of the Cardiovascular Disease Initiative at the Broad Institute, as
well as director of the Centre for Genomic Medicine at MGH and a professor of
medicine at Harvard Medical School. "Now, we're able to measure that risk
using genomic data in a meaningful way. From a public health perspective, we
need to identify these higher-risk segments of the population so we can provide
appropriate care."
Kathiresan led the work with first authors Amit
V. Khera, a cardiologist at MGH and junior faculty member in Kathiresan's lab,
and Mark Chaffin, a computational biologist also in Kathiresan's lab.
To develop the algorithms for scoring disease
risk, the researchers first gathered data from large-scale genome-wide
association studies to identify genetic variants associated with coronary
artery disease, atrial fibrillation, type 2 diabetes, inflammatory bowel
disease, or breast cancer. For each disease, they applied a computational
algorithm to combine information from all of the variants — most of which individually
have an extremely small impact on risk — into a single number, or polygenic risk score. This number could
be used to predict a person's chances of getting these diseases based on his or
her genome.
The team tested and validated the polygenic risk
score algorithms on data from over 400,000 individuals in the UK Biobank, an
extensive database of genomic data and medical information from participants of
British ancestry.
Importantly, according to Khera, the people with
high polygenic risk scores for coronary artery disease did not necessarily
exhibit other warning signs of disease risk (such as hypertension or high
cholesterol).
"These individuals, who are at several times
the normal risk for having a heart attack just because of the additive effects
of many variations, are mostly flying under the radar," he explained.
"If they came into my clinical practice, I wouldn't be able to pick them
out as high risk with our standard metrics. There's a real need to identify
these cases so we can target screening and treatments more effectively, and
this approach gives us a potential way forward."
Researchers Predict Risk
For Common Deadly Diseases From Millions Of Genetic Variants
Here's how the score worked for coronary artery
disease: The algorithm pored over more than 6.6 million locations in the genome
to estimate a person's risk of developing the deadly disease, which is the most
common type of heart disease and a leading cause of death for adults in the
United States. Of the individuals in the UK Biobank dataset, 8 percent were
more than three times as likely to develop the disease compared to everyone
else, based on their genetic variation. In absolute terms, only 0.8 percent of
individuals with the very lowest polygenic risk scores had coronary artery
disease, as compared to 11 percent for the people with the top scores.
For breast cancer, a leading cause of
malignancy-related death in women, the polygenic predictor found that 1.5
percent of the UK Biobank population had more than triple the risk for having
the disease when compared to everyone else. Those with the very highest
polygenic risk scores had five times the risk — meaning, in absolute terms, that 19% of people
with the top scores had breast cancer, versus about 4% of the remaining
individuals. The researchers applied a similar approach to polygenic risk
scoring for type 2 diabetes, atrial fibrillation, and inflammatory bowel
disease.
To develop polygenic risk scoring tests for other
common diseases, the team notes that additional research will be necessary to
collect genome-wide association data and validate the scores with reference
biobanks. In addition, the current polygenic risk calculations are largely
derived from genetic studies done in people of European ancestry — so more studies are needed
to optimize the algorithms for other ethnic groups.
Nevertheless, the researchers propose that it is
time for the biomedical community to consider including this approach in
clinical care. To do this, a number of factors need to be considered, such as:
whether the disease has a genetic component; if the disease is prevalent enough
in the general population to make screening worth incorporating into routine
clinical care; and if knowing the genetic risk for a disease would be useful in
guiding care to offset this inherited risk.
Originally published on SCIENCE DAILY
