The Shiny–and Potentially Dangerous—New Tool for Predicting Human Behavior
[Editor's Note: This essay is in response to our current Big Question, which we posed to experts with different perspectives: "How should DNA tests for intelligence be used, if at all, by parents and educators?"]
Imagine a world in which pregnant women could go to the doctor and obtain a simple inexpensive genetic test of their unborn child that would allow them to predict how tall he or she would eventually be. The test might also tell them the child's risk for high blood pressure or heart disease.
Can we use DNA not to understand, but to predict who is going to be intelligent or extraverted or mentally ill?
Even more remarkable -- and more dangerous -- the test might predict how intelligent the child would be, or how far he or she could be expected to go in school. Or heading further out, it might predict whether he or she will be an alcoholic or a teetotaler, or straight or gay, or… you get the idea. Is this really possible? If it is, would it be a good idea? Answering these questions requires some background in a scientific field called behavior genetics.
Differences in human behavior -- intelligence, personality, mental illness, pretty much everything -- are related to genetic differences among people. Scientists have known this for 150 years, ever since Darwin's half-cousin Francis Galton first applied Shakespeare's phrase, "Nature and Nurture" to the scientific investigation of human differences. We knew about the heritability of behavior before Mendel's laws of genetics had been re-discovered at the end of the last century, and long before the structure of DNA was discovered in the 1950s. How could discoveries about genetics be made before a science of genetics even existed?
The answer is that scientists developed clever research designs that allowed them to make inferences about genetics in the absence of biological knowledge about DNA. The best-known is the twin study: identical twins are essentially clones, sharing 100 percent of their DNA, while fraternal twins are essentially siblings, sharing half. To the extent that identical twins are more similar for some trait than fraternal twins, one can infer that heredity is playing a role. Adoption studies are even more straightforward. Is the personality of an adopted child more like the biological parents she has never seen, or the adoptive parents who raised her?
Twin and adoption studies played an important role in establishing beyond any reasonable doubt that genetic differences play a role in the development of differences in behavior, but they told us very little about how the genetics of behavior actually worked. When the human genome was finally sequenced in the early 2000s, and it became easier and cheaper to obtain actual DNA from large samples of people, scientists anticipated that we would soon find the genes for intelligence, mental illness, and all the other behaviors that were known to be "heritable" in a general way.
But to everyone's amazement, the genes weren't there. It turned out that there are thousands of genes related to any given behavior, so many that they can't be counted, and each one of them has such a tiny effect that it can't be tied to meaningful biological processes. The whole scientific enterprise of understanding the genetics of behavior seemed ready to collapse, until it was rescued -- sort of -- by a new method called polygenic scores, PGS for short. Polygenic scores abandon the old task of finding the genes for complex human behavior, replacing it with black-box prediction: can we use DNA not to understand, but to predict who is going to be intelligent or extraverted or mentally ill?
Prediction from observing parents works better, and is far easier and cheaper, than anything we can do with DNA.
PGS are the shiny new toy of human genetics. From a technological standpoint they are truly amazing, and they are useful for some scientific applications that don't involve making decisions about individual people. We can obtain DNA from thousands of people, estimate the tiny relationships between individual bits of DNA and any outcome we want — height or weight or cardiac disease or IQ — and then add all those tiny effects together into a single bell-shaped score that can predict the outcome of interest. In theory, we could do this from the moment of conception.
Polygenic scores for height already work pretty well. Physicians are debating whether the PGS for heart disease are robust enough to be used in the clinic. For some behavioral traits-- the most data exist for educational attainment -- they work well enough to be scientifically interesting, if not practically useful. For traits like personality or sexual orientation, the prediction is statistically significant but nowhere close to practically meaningful. No one knows how much better any of these predictions are likely to get.
Without a doubt, PGS are an amazing feat of genomic technology, but the task they accomplish is something scientists have been able to do for a long time, and in fact it is something that our grandparents could have done pretty well. PGS are basically a new way to predict a trait in an individual by using the same trait in the individual's parents — a way of observing that the acorn doesn't fall far from the tree.
The children of tall people tend to be tall. Children of excellent athletes are athletic; children of smart people are smart; children of people with heart disease are at risk, themselves. Not every time, of course, but that is how imperfect prediction works: children of tall parents vary in their height like anyone else, but on average they are taller than the rest of us. Prediction from observing parents works better, and is far easier and cheaper, than anything we can do with DNA.
But wait a minute. Prediction from parents isn't strictly genetic. Smart parents not only pass on their genes to their kids, but they also raise them. Smart families are privileged in thousands of ways — they make more money and can send their kids to better schools. The same is true for PGS.
The ability of a genetic score to predict educational attainment depends not only on examining the relationship between certain genes and how far people go in school, but also on every personal and social characteristic that helps or hinders education: wealth, status, discrimination, you name it. The bottom line is that for any kind of prediction of human behavior, separation of genetic from environmental prediction is very difficult; ultimately it isn't possible.
Still, experts are already discussing how to use PGS to make predictions for children, and even for embryos.
This is a reminder that we really have no idea why either parents or PGS predict as well or as poorly as they do. It is easy to imagine that a PGS for educational attainment works because it is summarizing genes that code for efficient neurological development, bigger brains, and swifter problem solving, but we really don't know that. PGS could work because they are associated with being rich, or being motivated, or having light skin. It's the same for predicting from parents. We just don't know.
Still, experts are already discussing how to use PGS to make predictions for children, and even for embryos.
For example, maybe couples could fertilize multiple embryos in vitro, test their DNA, and select the one with the "best" PGS on some trait. This would be a bad idea for a lot of reasons. Such scores aren't effective enough to be very useful to parents, and to the extent they are effective, it is very difficult to know what other traits might be selected for when parents try to prioritize intelligence or attractiveness. People will no doubt try it anyway, and as a matter of reproductive freedom I can't think of any way to stop them. Fortunately, the practice probably won't have any great impact one way or another.
That brings us to the ethics of PGS, particularly in the schools. Imagine that when a child enrolls in a public school, an IQ test is given to her biological parents. Children with low-IQ parents are statistically more likely to have low IQs themselves, so they could be assigned to less demanding classrooms or vocational programs. Hopefully we agree that this would be unethical, but let's think through why.
First of all, it would be unethical because we don't know why the parents have low IQs, or why their IQs predict their children's. The parents could be from a marginalized ethnic group, recognizable by their skin color and passed on genetically to their children, so discriminating based on a parent's IQ would just be a proxy for discriminating based on skin color. Such a system would be no more than a social scientific gloss on an old-fashioned program for perpetuating economic and cognitive privilege via the educational system.
People deserve to be judged on the basis of their own behavior, not a genetic test.
Assigning children to classrooms based on genetic testing would be no different, although it would have the slight ethical advantage of being less effective. The PGS for educational attainment could reflect brain-efficiency, but it could also depend on skin color, or economic advantage, or personality, or literally anything that is related in any way to economic success. Privileging kids with higher genetic scores would be no different than privileging children with smart parents. If schools really believe that a psychological trait like IQ is important for school placement, the sensible thing is to administer the children an actual IQ test – not a genetic test.
IQ testing has its own issues, of course, but at least it involves making decisions about individuals based on their own observable characteristics, rather than on characteristics of their parents or their genome. If decisions must be made, if resources must be apportioned, people deserve to be judged on the basis of their own behavior, the content of their character. Since it can't be denied that people differ in all sorts of relevant ways, this is what it means for all people to be created equal.
[Editor's Note: Read another perspective in the series here.]
A new injection is helping stave off RSV this season
In November 2021, Mickayla Wininger’s then one-month-old son, Malcolm, endured a terrifying bout with RSV, the respiratory syncytial (sin-SISH-uhl) virus—a common ailment that affects all age groups. Most people recover from mild, cold-like symptoms in a week or two, but RSV can be life-threatening in others, particularly infants.
Wininger, who lives in southern Illinois, was dressing Malcolm for bed when she noticed what seemed to be a minor irregularity with this breathing. She and her fiancé, Gavin McCullough, planned to take him to the hospital the next day. The matter became urgent when, in the morning, the boy’s breathing appeared to have stopped.
After they dialed 911, Malcolm started breathing again, but he ended up being hospitalized three times for RSV and defects in his heart. Eventually, he recovered fully from RSV, but “it was our worst nightmare coming to life,” Wininger recalled.
It’s a scenario that the federal government is taking steps to prevent. In July, the Food and Drug Administration approved a single-dose, long-acting injection to protect babies and toddlers. The injection, called Beyfortus, or nirsevimab, became available this October. It reduces the incidence of RSV in pre-term babies and other infants for their first RSV season. Children at highest risk for severe RSV are those who were born prematurely and have either chronic lung disease of prematurity or congenital heart disease. In those cases, RSV can progress to lower respiratory tract diseases such as pneumonia and bronchiolitis, or swelling of the lung’s small airway passages.
Each year, RSV is responsible for 2.1 million outpatient visits among children younger than five-years-old, 58,000 to 80,000 hospitalizations in this age group, and between 100 and 300 deaths, according to the Centers for Disease Control and Prevention. Transmitted through close contact with an infected person, the virus circulates on a seasonal basis in most regions of the country, typically emerging in the fall and peaking in the winter.
In August, however, the CDC issued a health advisory on a late-summer surge in severe cases of RSV among young children in Florida and Georgia. The agency predicts "increased RSV activity spreading north and west over the following two to three months.”
Infants are generally more susceptible to RSV than older people because their airways are very small, and their mechanisms to clear these passages are underdeveloped. RSV also causes mucus production and inflammation, which is more of a problem when the airway is smaller, said Jennifer Duchon, an associate professor of newborn medicine and pediatrics in the Icahn School of Medicine at Mount Sinai in New York.
In 2021 and 2022, RSV cases spiked, sending many to emergency departments. “RSV can cause serious disease in infants and some children and results in a large number of emergency department and physician office visits each year,” John Farley, director of the Office of Infectious Diseases in the FDA’s Center for Drug Evaluation and Research, said in a news release announcing the approval of the RSV drug. The decision “addresses the great need for products to help reduce the impact of RSV disease on children, families and the health care system.”
Sean O’Leary, chair of the committee on infectious diseases for the American Academy of Pediatrics, says that “we’ve never had a product like this for routine use in children, so this is very exciting news.” It is recommended for all kids under eight months old for their first RSV season. “I would encourage nirsevimab for all eligible children when it becomes available,” O’Leary said.
For those children at elevated risk of severe RSV and between the ages of 8 and 19 months, the CDC recommends one dose in their second RSV season.
The drug will be “really helpful to keep babies healthy and out of the hospital,” said O’Leary, a professor of pediatrics at the University of Colorado Anschutz Medical Campus/Children’s Hospital Colorado in Denver.
An antiviral drug called Synagis (palivizumab) has been an option to prevent serious RSV illness in high-risk infants since it was approved by the FDA in 1998. The injection must be given monthly during RSV season. However, its use is limited to “certain children considered at high risk for complications, does not help cure or treat children already suffering from serious RSV disease, and cannot prevent RSV infection,” according to the National Foundation for Infectious Diseases.
Until the approval this summer of the new monoclonal antibody, nirsevimab, there wasn’t a reliable method to prevent infection in most healthy infants.
Both nirsevimab and palivizumab are monoclonal antibodies that act against RSV. Monoclonal antibodies are lab-made proteins that mimic the immune system’s ability to fight off harmful pathogens such as viruses. A single intramuscular injection of nirsevimab preceding or during RSV season may provide protection.
The strategy with the new monoclonal antibody is “to extend protection to healthy infants who nonetheless are at risk because of their age, as well as infants with additional medical risk factors,” said Philippa Gordon, a pediatrician and infectious disease specialist in Brooklyn, New York, and medical adviser to Park Slope Parents, an online community support group.
No specific preventive measure is needed for older and healthier kids because they will develop active immunity, which is more durable. Meanwhile, older adults, who are also vulnerable to RSV, can receive one of two new vaccines. So can pregnant women, who pass on immunity to the fetus, Gordon said.
Until the approval this summer of the new monoclonal antibody, nirsevimab, there wasn’t a reliable method to prevent infection in most healthy infants, “nor is there any treatment other than giving oxygen or supportive care,” said Stanley Spinner, chief medical officer and vice president of Texas Children’s Pediatrics and Texas Children’s Urgent Care.
As with any virus, washing hands frequently and keeping infants and children away from sick people are the best defenses, Duchon said. This approach isn’t foolproof because viruses can run rampant in daycare centers, schools and parents’ workplaces, she added.
Mickayla Wininger, Malcolm’s mother, insists that family and friends wear masks, wash their hands and use hand sanitizer when they’re around her daughter and two sons. She doesn’t allow them to kiss or touch the children. Some people take it personally, but she would rather be safe than sorry.
Wininger recalls the severe anxiety caused by Malcolm's ordeal with RSV. After returning with her infant from his hospital stays, she was terrified to go to sleep. “My fiancé and I would trade shifts, so that someone was watching over our son 24 hours a day,” she said. “I was doing a night shift, so I would take caffeine pills to try and keep myself awake and would end up crashing early hours in the morning and wake up frantically thinking something happened to my son.”
Two years later, her anxiety has become more manageable, and Malcolm is doing well. “He is thriving now,” Wininger said. He recently had his second birthday and "is just the spunkiest boy you will ever meet. He looked death straight in the eyes and fought to be here today.”
Story by Big Think
For most of history, artificial intelligence (AI) has been relegated almost entirely to the realm of science fiction. Then, in late 2022, it burst into reality — seemingly out of nowhere — with the popular launch of ChatGPT, the generative AI chatbot that solves tricky problems, designs rockets, has deep conversations with users, and even aces the Bar exam.
But the truth is that before ChatGPT nabbed the public’s attention, AI was already here, and it was doing more important things than writing essays for lazy college students. Case in point: It was key to saving the lives of tens of millions of people.
AI-designed mRNA vaccines
As Dave Johnson, chief data and AI officer at Moderna, told MIT Technology Review‘s In Machines We Trust podcast in 2022, AI was integral to creating the company’s highly effective mRNA vaccine against COVID. Moderna and Pfizer/BioNTech’s mRNA vaccines collectively saved between 15 and 20 million lives, according to one estimate from 2022.
Johnson described how AI was hard at work at Moderna, well before COVID arose to infect billions. The pharmaceutical company focuses on finding mRNA therapies to fight off infectious disease, treat cancer, or thwart genetic illness, among other medical applications. Messenger RNA molecules are essentially molecular instructions for cells that tell them how to create specific proteins, which do everything from fighting infection, to catalyzing reactions, to relaying cellular messages.
Johnson and his team put AI and automated robots to work making lots of different mRNAs for scientists to experiment with. Moderna quickly went from making about 30 per month to more than one thousand. They then created AI algorithms to optimize mRNA to maximize protein production in the body — more bang for the biological buck.
For Johnson and his team’s next trick, they used AI to automate science, itself. Once Moderna’s scientists have an mRNA to experiment with, they do pre-clinical tests in the lab. They then pore over reams of data to see which mRNAs could progress to the next stage: animal trials. This process is long, repetitive, and soul-sucking — ill-suited to a creative scientist but great for a mindless AI algorithm. With scientists’ input, models were made to automate this tedious process.
“We don’t think about AI in the context of replacing humans,” says Dave Johnson, chief data and AI officer at Moderna. “We always think about it in terms of this human-machine collaboration, because they’re good at different things. Humans are really good at creativity and flexibility and insight, whereas machines are really good at precision and giving the exact same result every single time and doing it at scale and speed.”
All these AI systems were in put in place over the past decade. Then COVID showed up. So when the genome sequence of the coronavirus was made public in January 2020, Moderna was off to the races pumping out and testing mRNAs that would tell cells how to manufacture the coronavirus’s spike protein so that the body’s immune system would recognize and destroy it. Within 42 days, the company had an mRNA vaccine ready to be tested in humans. It eventually went into hundreds of millions of arms.
Biotech harnesses the power of AI
Moderna is now turning its attention to other ailments that could be solved with mRNA, and the company is continuing to lean on AI. Scientists are still coming to Johnson with automation requests, which he happily obliges.
“We don’t think about AI in the context of replacing humans,” he told the Me, Myself, and AI podcast. “We always think about it in terms of this human-machine collaboration, because they’re good at different things. Humans are really good at creativity and flexibility and insight, whereas machines are really good at precision and giving the exact same result every single time and doing it at scale and speed.”
Moderna, which was founded as a “digital biotech,” is undoubtedly the poster child of AI use in mRNA vaccines. Moderna recently signed a deal with IBM to use the company’s quantum computers as well as its proprietary generative AI, MoLFormer.
Moderna’s success is encouraging other companies to follow its example. In January, BioNTech, which partnered with Pfizer to make the other highly effective mRNA vaccine against COVID, acquired the company InstaDeep for $440 million to implement its machine learning AI across its mRNA medicine platform. And in May, Chinese technology giant Baidu announced an AI tool that designs super-optimized mRNA sequences in minutes. A nearly countless number of mRNA molecules can code for the same protein, but some are more stable and result in the production of more proteins. Baidu’s AI, called “LinearDesign,” finds these mRNAs. The company licensed the tool to French pharmaceutical company Sanofi.
Writing in the journal Accounts of Chemical Research in late 2021, Sebastian M. Castillo-Hair and Georg Seelig, computer engineers who focus on synthetic biology at the University of Washington, forecast that AI machine learning models will further accelerate the biotechnology research process, putting mRNA medicine into overdrive to the benefit of all.
This article originally appeared on Big Think, home of the brightest minds and biggest ideas of all time.