DNA-based scores are getting better at predicting
intelligence, risks for common diseases, and more.
February 21, 2018
When Amit Khera explains
how he predicts disease, the young cardiologist’s hands touch the air,
arranging imaginary columns of people: 30,000 who have suffered heart attacks
here, 100,000 healthy controls there.
There’s never been data available on as many people’s genes as
there is today. And that wealth of information is allowing researchers to guess
at any person’s chance of getting common diseases like diabetes, arthritis,
clogged arteries, and depression.
Doctors already test for rare, deadly mutations in individual
genes. Think of the BRCA breast cancer gene. Or the one-letter
mutation that causes sickle-cell anemia. But such one-to-one connections
between a mutation and a disease—“the gene for X”—aren’t seen in most common
ailments. Instead, these have complex causes, which until recently have
remained elusive.
Here’s the breakthrough: a new way to
guess your chance of serious disease from your DNA. Any drawbacks? You bet. The
technology could lead to a society where people get genetic grades at birth.
The day I visited him, Khera was constructing what is called a
polygenic score—“poly” because his calculations involve thousands of genes, not
just one. This particular score predicted a person’s chance of developing
atrial fibrillation, or irregular heartbeat. It’s a common disorder but is
often diagnosed only after someone has been rushed to the ER with a stroke.
Khera pointed to his screen. There, seven-digit numbers, each
representing an anonymous DNA donor, appeared alongside their scores. The
outliers had a risk four times the average.
Khera, who works in the laboratory of heart doctor and gene
hunter Sekar Kathiresan at the Broad Institute, in Cambridge, Massachusetts,
says the new scores can now identify as much risk for disease as the rare
genetic flaws that have preoccupied physicians until now.
“Where I see this going
is that at a young age you’ll basically get a report card,” says Khera. “And it
will say for these 10 diseases, here’s your score. You are in the 90th
percentile for heart disease, 50th for breast cancer, and the lowest 10 percent
for diabetes.”
Such comprehensive report cards aren’t being given out yet, but
the science to create them is here. Delving into giant databases like the UK Biobank, which collects the DNA and holds the medical
records of some 500,000 Britons, geneticists are peering into the lives of more
people and extracting correlations between their genomes and their diseases,
personalities, even habits. The latest gene hunt, for the causes of insomnia,
involved a record 1,310,010 people.
The sheer quantity of material is what allows scientists like
Khera to see how complex patterns of genetic variants are tied to many diseases
and traits. Such patterns were hidden in earlier, more limited studies, but now
the search for ever smaller signals in ever bigger data is paying off. Give
Khera the simplest readout of your genome—the kind created with a $100
DNA-reading chip the size of a theater ticket—and he can add up your
vulnerabilities and strengths just as one would a tally in a ledger.
Such predictions, at first hit-or-miss, are becoming more
accurate. One test described last year can guess a person’s height to within
four centimeters, on the basis of 20,000 distinct DNA letters in a genome. As
the prediction technology improves, a flood of tests is expected to reach the
market. Doctors in California are testing an iPhone app that,
if you upload your genetic data, foretells your risk of coronary artery
disease. A commercial test launched in September, by Myriad Genetics, estimates the breast cancer chances of any
woman of European background, not only the few who have inherited broken
versions of the BRCA gene. Sharon Briggs, a
senior scientist at Helix, which operates an online store for DNA tests, says most of these products will
use risk scores within three years.
“It’s not that the scores are new,” says Briggs. “It’s that
they’re getting much better. There’s more data.”
Tiny Influences
When they launched the first modern gene searches a decade ago,
following the completion of the Human Genome Project, medical researchers still
hoped that a few major genetic culprits would explain common diseases like
diabetes. “I expect there are about 12 genes involved [in diabetes], and that
all of them will be discovered in the next two years,” Francis Collins, now the
head of the US National Institutes of Health and one of the leading players in
sequencing the human genome, confidently declared in
2006.
If that had turned out to be true, the small list of genes would
have given drug designers clear, tangible targets. That would easily have
justified the whole enterprise, financed with hundreds of millions of US tax
dollars. In the case of a few diseases, like macular degeneration, the searches
paid off. Mostly, though, geneticists drew in empty nets. By 2009, Collins and others
had begun to talk glumly about “the missing heritability.”
Where were the disease-causing genes? Everywhere, it turns out.
And by 2014, genetic studies were finally big enough to prove it. As the number
of people with diabetes who enrolled in the gene search studies rose from 661 to 10,128 to 81,412,
the “hits” began rolling in. Instead of 12 genes, we now know, type 2 diabetes
is influenced by at least 400 locations in our DNA, and probably many more—each
with only a tiny, hard-to-detect effect.
To scientists seeking the ultimate cause of common diseases,
that’s a huge disappointment. If the causes of diabetes, depression, or
schizophrenia are sprinkled around the genome like so much powdered sugar, it
means we’re far from understanding or curing them. “No one wanted that to be
the answer,” says Mark Daly, a geneticist at the Broad Institute. “But it is
what it is.”
While the scattershot nature of inheritance may make disease
hard to comprehend, though, the same data is making it much easier to predict.
To create their models, Khera and Kathiresan use 6.6 million positions in a
person’s genome. Each position is a single DNA letter. It could be A for you
and G for me. From big genetic studies, Khera can now look up how much more
likely a person with a G in that position is to have a heart attack. Maybe it
raises the chances by 0.1 percent. That’s a negligible amount. Maybe a G in
another position reduces the risk by 0.2 percent. But if you add up all the
tiny genetic influences, the effect can become substantial.
When they built a predictor for coronary heart disease, for
instance, Kathiresan’s team discovered that the people it predicted to have the
very highest risk, the top 2.5 percent, had four times the average chance of
developing clogged arteries. That’s about equal to the risk of clogged arteries
caused by familial hypercholesterolemia, a condition marked by sky-high
cholesterol levels and caused by a single critical gene. If doctors worry about
that—which they do—why not also pay attention to the high end of genomic risk
scores?
“That’s the thing that convinced me,” says Kathiresan. And the
number of people whose genome predictions raise a red alert will also be much
larger. Familial cholesterolemia affects only about one in 250 people. Genome
scores would identify about eight times as many people at high risk for heart
disease, he believes.
What he’s not yet sure about, Kathiresan says, is how to get the
new risk information into people’s hands. He has considered launching an app or
selling the statistical model to a diagnostics company. “Everyone wants to get
their score. Everyone is asking where is the product for heart disease,” he
says. “I tell them, we are working on it.”
Heart disease is, in some ways, a best-case scenario for using
risk scores. That’s because you can change your real-life risk—say, by going on
a diet or taking a cholesterol-lowering statin pill. What’s more, probabilities
are already a big part of heart medicine. Khera, who dons a white coat once a
week to treat patients at Massachusetts General Hospital, uses a combination of
a person’s age, weight, cholesterol levels, and habits like smoking to guess
the chance of a heart attack in the next 10 years. Now genetic scores could be
added to those models, making them more accurate.
What’s powerful about DNA predictions is that they are
measurable at any time of life, unlike most risk factors. “If you line up a
bunch of 18-year-olds, none of them have high cholesterol, none of them have
diabetes. It’s a zero in all the columns, and you can’t stratify them by who is
most at risk,” says Khera.
“But with a $100 test we can get stratification at least as good
as when someone is 50, and for a lot of diseases.”
Dangerous knowledge
Drug companies have started to notice. Last year Anders Dale, a
brain researcher at the University of California, San Diego, announced his
intention to market a risk calculator for Alzheimer’s disease. It will guess
whether a person will develop the disease and, if so, at what age.
The service won’t
launch until this summer, but Dale says drug companies immediately got in
touch. Now he is helping three of them test the DNA of people in clinical
trials for Alzheimer’s drugs (he declined to name them). Despite the billions
spent developing such drugs, every one tried so far has flopped. The problem is
that when no one knows who will get the disease, it’s difficult to know whether
a preventive drug is working. If companies could test the drugs only on people
with a high risk of Alzheimer’s, it would be much easier. It’s possible future
drugs will be labeled “Recommended for those with polygenic scores 90 and
above.”
Dale is working with commercial partners to put his Alzheimer’s
predictor online and charge as little as $99 to anyone who wants to use
it. More than ten million people already have their DNA data because
they signed up for 23andMe or Ancestry.com to research their family trees. Dale
says they will be able to upload the data with a click and receive a report. I
asked him why people would want to know ahead of time about a disease that’s
currently untreatable. “They may want to make plans,” he said.
DNA-based IQ tests are likely to
become available in coming years. Critics fear “some truly dreadful social
policies” could result.
Other doctors believe risk scores will give people the push they
need to think harder about their well-being. “I love the idea of polygenic risk
scores because the future is health, not medicine,” says Steven Tucker, a
physician who practices in Singapore. He likes his patients to use wearable
devices and trackers, and risk scores could be combined with those. Someone at
high risk for atrial fibrillation, for instance, might wear a smart watch with
a heart monitor built into it. “My patients want to manage the future,” says
Tucker. “If you can define it more accurately, there is a better chance you can
do something about it.”
Even so, the value of the new genomic future-gazing is hotly
disputed. That’s because the scores are not individual certainties; they are
merely rough probabilities derived from large populations. Of people given high
scores by Khera’s atrial fibrillation predictor, for example, only a small minority,
7 percent, would actually develop the condition by age 55.
This uncertainty matters because if people are given risk
scores, they’ll base decisions on them. Last fall, Myriad Genetics became the
first large diagnostics company to introduce a polygenic risk test to the US
market. Called riskScore, it measures 81 variants to estimate a woman’s
chance of breast cancer. Women with a high score might undergo extra
mammograms; those at low risk might skip them. What no one yet knows is whether
those decisions will lead to fewer cancer deaths. Finding out will require
expensive long-term studies that Myriad, which is selling the test, hasn’t yet
done.
One physician who finds all this troubling is Patrick Sullivan
at the University of North Carolina, Chapel Hill, where he leads the
Psychiatric Genomics Consortium. The group has DNA data from more than 900,000
people with confirmed mental illness, including more than 60,000 with
schizophrenia. This disease is known to be highly influenced by genes. If one
identical twin develops schizophrenia, for example, there is a 50 percent
chance the other one will too.
But Sullivan says it would be reckless to tell apparently
healthy people whether their DNA score for schizophrenia is high or low. Just
think of those twins, he says: they have the same DNA and the same score, yet
it is even odds a schizophrenia prediction would be wrong. Giving such a flawed
forecast to someone “is a terrible idea,” says Sullivan. “What you want it to
do is distinguish who has it and who doesn’t, and we aren’t there yet.”
A DNA IQ test
In addition to predicting disease, geneticists can build models
to predict any human trait that can be measured, including behaviors. Is this
person destined for a life of crime and recidivism? Will that one be neurotic,
depressed, or smarter than average?
The scoring technology, scientists say, will soon shed
uncomfortable light on such questions. In January, two leading psychologists
argued that direct-to-consumer DNA IQ tests will soon become “routinely
available” and will predict children’s ability “to learn, reason, and solve
problems.” They believe parents will test toddlers and use the results to make
school plans.
To some, using foggy genetic horoscopes to decide who goes to
college and who ends up in trade school sounds like an extraordinarily bad idea.
On his blog Gloomy Prospect, Eric Turkheimer, a prominent psychologist at the
University of Virginia, says the danger is that the scores will be
overinterpreted to “recommend some truly dreadful social policies.” That, he
thinks, would be “the worst possible kind of biologically determinist
discrimination.” To Turkheimer, polygenic scores are “less than meets the eye”
and about as fair as “predicting your IQ from a cousin you haven’t met.”
Such views aren’t stopping the rapid pace of genetic exploration.
Until last year, no gene variant had ever been tied directly to IQ test
results. Since then, studies involving more than 300,000 people’s DNA have
linked 206 variants to intelligence. It means genetic scores can now account
for 10 percent of a person’s performance on an IQ test. That could reach 25
percent within a few years, as more data accumulates. One US company, Genomic Prediction, even says it wants to test IVF embryos for
intelligence, so parents can discard those expected to be mentally unfit.
Dystopia, dubious medicine, or a breakthrough in prevention?
Genomic prediction may well be all three. What is clear is that, with the data
needed to create predictors becoming freely available online, 2018 will be a
breakout year for DNA fortune-telling.
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