With the rise of technology and genetic science, Stephen Hsu’s startup Genomic Predictions analyzes genetic data to predict the chance of diseases like diabetes and cancer — and forecast IQ. This arises a question whether an algorithm could predict your unborn child’s intelligence.
Is it possible to have an an algorithm could predict your unborn child’s intelligence?
Recently, hopeful oldsters following in vitro fertilization (IVF) treatment have had the choice of screening embryos for severe genetic diseases like fibrocystic disease of the pancreas, hemophilia, and lipidosis. 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 — that kill lots of Americans annually — almost not possible 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 method that makes an attempt to ascertain the statistical probability of an individual developing diseases like polygenic disease or high blood pressure throughout their life as well as answer the question if an algorithm could predict your unborn child’s intelligence. It could be the first step in a dystopian science fiction scenario: designer babies.
A dystopian science fiction scenario: designer babies
Genomic Prediction was co-founded in 2017 by Stephen Hsu, a professor of theoretical physics at Michigan State University. A decade ago, Hsu noticed that the continually decreasing cost of genetic sequencing and genotyping was so steep and rapid that it might allow him to solve a fundamental problem that had fascinated him since childhood.
“When I first learned about evolution in biology class, I took an interest during an explicit mathematical, theoretical question,” Hsu told OneZero. “If traits are heritable, and you have access to all of a person’s DNA, how much can you predict about them?” That also leads him to a permanent question if an algorithm could predict your unborn child’s intelligence. More polemically, however, Genomic Prediction is additionally giving IVF patients the choice of screening embryos for projected psychological feature ability.
Whereas the corporate says that at this stage it'll solely inform oldsters concerning the risks of potential intellectual disability — outlined as twenty five points below the common I.Q. — it’s straightforward to imagine that this new technology might be the primary step to create an algorithm could predict your unborn child’s intelligence. They believed that once trained, the algorithm would be able to predict the likelihood of a complex trait by identifying a combination of genetic signals in the DNA.
The algorithm is about identifying a combination of genetic signals in the DNA
In 2017, Hsu and his collaborators released a report showing that they could successfully use this approach to predict height from a genotype within roughly one inch. That same year, Hsu co-founded Genomic Prediction and quickly began using the technology to predict the risk from polygenic diseases.
Currently, the company offers polygenic risk scores, which assign a probability to diseases that might arise in a customer given their genetic makeup. Unlike monogenic disorders like cystic fibrosis, “we can’t just look at a person and say yes or no, you’re going to have breast cancer,” Hsu told me. “But what we can do is tell someone that they are in the 99th percentile of people who have risk for breast cancer, which might mean that they have a 50% chance of developing it in their lifetime.”
At this stage, Genomic Prediction is offering polygenic risk scores to create help prospective parents using IVF — which usually involves the creation of multiple embryos — to select those embryos that increase the probability of having a healthy child and then develop an algorithm could predict your unborn child’s intelligence. But Hsu sees polygenic screening becoming a part of routine medical checkups. “At some point in the near future, when you go for your physical, your doctor will ask you for blood to get a genotype for you,” he said. “And then the doctor will get a report from us that will inform them on how best to look after you.”
While Hsu believes that polygenic risk scores will become part of standard clinical health care within a few years, other experts have their doubts.
Polygenic risk scores will become a part of standard clinical health care
Cecile Janssens, professor of epidemiology at Emory University who has been studying genetic prediction for over a decade, told me that she was skeptical of how much meaning Hsu assigns to the output of these algorithms, especially an algorithm could predict your unborn child’s intelligence. “Prediction is extremely difficult, and probabilities are hard to interpret,” she explained. “Would you not choose an embryo because it’s risk of getting breast cancer was increased from 8% to 14% or its risk of diabetes was 18% instead of 11%? Would you even know what that all means?”
At this stage, polygenic risk scores are most useful in assessing statistical outliers — those cases where an embryo is far more likely to develop a certain disease than other embryos — but expects significant improvement as more genomic data is collected. “For some diseases, we only have between 10,000 to 100,000 cases, but if we start to build larger and larger databases, that could cause a significant material advance in the quality of the predictor,” he explained.
Polygenic risk analysis includes a panel for cognitive ability
Which is more controversial is that Genomic Prediction’s polygenic risk analysis includes a panel for cognitive ability. According to Catherine Bliss, a professor of sociology at the University of California San Francisco and the author of two books on genomics, predicting for cognitive ability turns on the presupposition that intelligence is a highly heritable trait, which is, to say the least, a historically fraught and controversial claim.
As decades of research has shown, the science of cognitive heredity is always in flux, mostly due to the complex way intelligence interacts with our social environments, including education. Coming to a problem about an algorithm could predict your unborn child’s intelligence, “I don’t think predicting for cognitive capacity is a good idea until we have a better grasp on this gene-environment relationship,” Bliss told me.
Hsu has been embroiled in controversy concerning genetics and IQ before, due to his work for a Shenzen-based research institute, BGI, which allegedly mapped the genome of unusually intelligent people so as to try and separate an intelligence gene. But he told me that at this stage, the company’s polygenic test for intelligence will only indicate which embryo is a genetic outlier in terms of cognitive impairment, which correlates to an IQ 25 points below average. “If you’re a parent going through IVF and five embryos are fine, but one of them has scored in the bottom percentile for a cognitive score, I think that you deserve the option of knowing this,” he said.
Wealthy people may soon be able to design their children better genes?
When it comes to the social implications of this technology, Hsu is more concerned about accessibility and how these technologies will exacerbate class inequality. In addition to being able to give their child a private education and “even buy their way into Stanford,” Hsu says, wealthy people may soon be able to design their children better genes. While Hsu worries that a kind of genetic caste system looms on the near horizon, he hopes that predictive technology might also be used for socially progressive ends, or what he calls a “redistribution of genetic endowments.”
“Maybe let people from disadvantaged groups select from 20 embryos, instead of seven,” he said, which presumably raise the possibility of getting a healthy embryo that scores high on cognitive ability. “If this was done over a long period of time, maybe it would eventually catch that group up.” Underpinning this vision is Hsu’s fundamental belief that genetics can be used to not only predict and select for traits in individuals, but to engineer positive social change. “If I tell a couple that they are carriers of a disease and that there is a one in four chance that their kid is going to die in a horrible way, and allow them to select the healthy embryo, is that eugenics? ” Hsu said. “But if I give them the option to make to make a choice, and don’t coerce them, I don’t think there’s ethically anything wrong with it.”
Effects is changing over time and based on different cultures
While few would argue that selection in some circumstances is beneficial — and though it has long been the practice for prospective parents to screen for severe genetic disorders like cystic fibrosis and muscular dystrophy — in the U.S., embryonic screening is largely unregulated and takes place in the private IVF sector. Legally there is nothing stopping Hsu from adding cosmetic traits like hair color, height, and skin tone to the polygenic screening. He could also innovate an algorithm could predict your unborn child’s intelligence to test for high IQ and heritable personality traits like a predisposition to violence.
But Hsu believes that attitudes around an algorithm could predict your unborn child’s intelligence will change over time, as they already have, and points out that they already vary between cultures. In South and East Asia, for instance, there is more cultural acceptance of the idea of selecting for embryos in order to enhance desirable characteristics like intelligence, rather than just avoid genetic diseases.
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