Nathan Treff was diagnosed with type 1 diabetes at 24. It’s a disease that runs in families, but it has complex causes. More than one gene is involved. And the environment plays a role too.

So you don’t know who will get it. Treff’s grandfather had it, and lost a leg. But Treff’s three young kids are fine, so far. He’s crossing his fingers they won’t develop it later.

Now Treff, an in vitro fertilization specialist, is working on a radical way to change the odds. Using a combination of computer models and DNA tests, the startup company he’s working with, Genomic Prediction, thinks it has a way of predicting which IVF embryos in a laboratory dish would be most likely to develop type 1 diabetes or other complex diseases. Armed with such statistical scorecards, doctors and parents could huddle and choose to avoid embryos with failing grades.

IVF clinics already test the DNA of embryos to spot rare diseases, like cystic fibrosis, caused by defects in a single gene. But these “preimplantation” tests are poised for a dramatic leap forward as it becomes possible to peer more deeply at an embryo’s genome and create broad statistical forecasts about the person it would become.

The advance is occurring, say scientists, thanks to a growing flood of genetic data collected from large population studies. As statistical models known as predictors gobble up DNA and health information about hundreds of thousands of people, they’re getting more accurate at spotting the genetic patterns that foreshadow disease risk. But they have a controversial side, since the same techniques can be used to project the eventual height, weight, skin tone, and even intelligence of an IVF embryo.

In addition to Treff, who is the company’s chief scientific officer, the founders of Genomic Prediction are Stephen Hsu, a physicist who is vice president for research at Michigan State University, and Laurent Tellier, a Danish bioinformatician who is CEO. Both Hsu and Tellier have been closely involved with a project in China that aims to sequence the genomes of mathematical geniuses, hoping to shed light on the genetic basis of IQ.

Spotting outliers

The company’s plans rely on a tidal wave of new knowledge showing how small genetic differences can add up to put one person, but not another, at high odds for diabetes, a neurotic personality, or a taller or shorter height. Already, such “polygenic risk scores” are used in direct-to-consumer gene tests, such as reports from 23andMe that tell customers their genetic chance of being overweight.

For adults, risk scores are little more than a novelty or a source of health advice they can ignore. But if the same information is generated about an embryo, it could lead to existential consequences: who will be born, and who stays in a laboratory freezer.

“I remind my partners, ‘You know, if my parents had this test, I wouldn’t be here,’” says Treff, a prize-winning expert on diagnostic technology who is the author of more than 90 scientific papers.

Genomic Prediction was founded this year and has raised funds from venture capitalists in Silicon Valley, though it declines to say who they are. Tellier, whose inspiration is the science fiction film Gattaca, says the company plans to offer reports to IVF doctors and parents identifying “outliers”—those embryos whose genetic scores put them at the wrong end of a statistical curve for disorders such as diabetes, late-life osteoporosis, schizophrenia, and dwarfism, depending on whether models for those problems prove accurate.

A days-old human embryo in an IVF clinic. Some cells can be removed to perform DNA tests. dallasfertility.com

The company’s concept, which it calls expanded preimplantation genetic testing, or ePGT, would effectively add a range of common disease risks to the menu of rare ones already available, which it also plans to test for. Its promotional material uses a picture of a mostly submerged iceberg to get the idea across. “We believe it will become a standard part of the IVF process,” says Tellier, just as a test for Down syndrome is a standard part of pregnancy.

Some experts contacted by MIT Technology Review said they believed it’s premature to introduce polygenic scoring technology into IVF clinics—though perhaps not by very much. Matthew Rabinowitz, CEO of the prenatal-testing company Natera, based in California, says he thinks predictions obtained today could be “largely misleading” because DNA models don’t function well enough. But Rabinowitz agrees that the technology is coming.

“You are not going to stop the modeling in genetics, and you are not going to stop people from accessing it,” he says. “It’s going to get better and better.”

Sharp questions

Testing embryos for disease risks, including risks for diseases that develop only late in life, is considered ethically acceptable by U.S. fertility doctors. But the new DNA scoring models mean parents might be able to choose their kids on the basis of traits like IQ or adult weight. That’s because, just like type 1 diabetes, these traits are the result of complex genetic influences the predictor algorithms are designed to find.

The model predicted people’s height from their DNA data to within three or four centimeters.

“It’s the camel’s nose under the tent. Because if you are doing it for something more serious, then it’s trivially easy to look for anything else,” says Michelle Meyer, a bioethicist at the Geisinger Health System who analyzes issues in reproductive genetics. “Here is the genomic dossier on each embryo. And you flip through the book.” Imagine picking the embryo most likely to get into Harvard like Mom, or to be tall like Dad.

For Genomic Prediction, a tiny startup based out of a tech incubator in New Jersey, such questions will be especially sharply drawn. That is because of Hsu’s long-standing interest in genetic selection for superior intelligence.

In 2014, Hsu authored an essay titled “Super-Intelligent Humans Are Coming,” in which he argued that selecting embryos for intelligence could boost the resulting child’s IQ by 15 points.

Genomic Prediction says it will only report diseases—that is, identify those embryos it thinks would develop into people with serious medical problems. Even so, on his blog and in public statements, Hsu has for years been developing a vision that goes far beyond that.

“Suppose I could tell you embryo four is going to be the tallest, embryo three is going to be the smartest, embryo two is going to be very antisocial. Suppose that level of granularity was available in the reports,” he told the conservative radio and YouTube personality Stefan Molyneux this spring. “That is the near-term future that we as a civilization face. This is going to be here.”

IVF specialist Nathan Treff is a cofounder of Genomic Prediction, which aims to “score” embryos for diseases like type 1 diabetes. genomicprediction.com

Measuring height

The fuel for the predictive models is a deluge of new data, most recently genetic readouts and medical records for 500,000 middle-aged Britons that were released in July by the U.K. Biobank, a national precision-medicine project in that country.

The data trove included, for each volunteer, a map of about 800,000 single-nucleotide polymorphisms, or SNPs—points where their DNA differs slightly from another person’s. The release caused a pell-mell rush by geneticists to update their calculations about exactly how much of human disease, or even routine behaviors like bread consumption, these genetic differences could explain.

Armed with the U.K. data, Hsu and Tellier claimed a breakthrough. For one easily measured trait, height, they used machine-learning techniques to create a predictor that behaved flawlessly. They reported that the model could, for the most part, predict people’s height from their DNA data to within three or four centimeters.

Height is currently the easiest trait to predict. It’s determined mostly by genes, and it’s always recorded in population databases. But Tellier says genetic databases are “rapidly approaching” the size needed to make accurate predictions about other human features, including risk for diseases whose true causes aren’t even known.

Tellier says Genomic Prediction will zero in on disease traits for which the predictors already perform fairly well, or will soon. Those include autoimmune disorders like the illness Treff suffers from. In those conditions, a smaller set of genes dominates the predictions, sometimes making them more reliable.