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What If an Algorithm Could Predict Your Unborn Child’s Intelligence?
Stephen Hsu’s startup Genomic Predictions analyzes genetic data to predict the chance of diseases like diabetes and cancer — and forecast IQ
For years, hopeful parents pursuing in vitro fertilization (IVF) treatment have had the option of screening embryos for severe heritable diseases like cystic fibrosis, hemophilia, and Tay-Sachs disease. These rare and often deadly conditions, known as monogenic disorders, can be easily identified through genetic screening because they arise due to a mutation on a single gene. For doctors, diagnosis is a simple positive or negative.
But the diseases that are most likely to shadow the average person’s life — cancer, heart disease, diabetes — are polygenic, meaning that they result from interactions between thousands of genetic signals. In the past, this has made these diseases — which kill millions of Americans each year — all but impossible to screen for with genetic tests.
But Genomic Prediction, a New Jersey-based company that analyzes genetic data using machine learning, is hoping to change that. Taking advantage of the new troves of genetic sequences that have accumulated over the past decade, the company is offering what is known as polygenic risk scores, a screening process that attempts to establish the statistical probability of a person developing diseases like diabetes or hypertension throughout their life.
It’s easy to imagine that this new technology could be the first step in a dystopian science fiction scenario: designer babies.
More controversially, however, Genomic Prediction is also offering IVF patients the option of screening embryos for projected cognitive ability. While the company says that at this stage it will only inform parents about the risks of potential intellectual disability — defined as 25 points below the average IQ — it’s easy to imagine that this new technology could be the first step in a dystopian science fiction scenario: designer babies.
Genomic Prediction was co-founded in 2017 by Stephen Hsu, a professor of theoretical physics at…
