DNA Tests for Intelligence Ignore the Real Reasons Why Kids Succeed or Fail
[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?"]
It's 2019. Prenatal genetic tests are being used to help parents select from healthy and diseased eggs. Genetic risk profiles are being created for a range of common diseases. And embryonic gene editing has moved into the clinic. The science community is nearly unanimous on the question of whether we should be consulting our genomes as early as possible to create healthy offspring. If you can predict it, let's prevent it, and the sooner, the better.
There are big issues with IQ genetics that should be considered before parents and educators adopt DNA IQ predictions.
When it comes to care of our babies, kids, and future generations, we are doing things today that we never even dreamed would be possible. But one area that remains murky is the long fraught question of IQ, and whether to use DNA science to tell us something about it. There are big issues with IQ genetics that should be considered before parents and educators adopt DNA IQ predictions.
IQ tests have been around for over a century. They've been used by doctors, teachers, government officials, and a whole host of institutions as a proxy for intelligence, especially in youth. At times in history, test results have been used to determine whether to allow a person to procreate, remain a part of society, or merely stay alive. These abuses seem to be a distant part of our past, and IQ tests have since garnered their fair share of controversy for exhibiting racial and cultural biases. But they continue to be used across society. Indeed, much of the literature aimed at expecting parents justifies its recommendations (more omegas, less formula, etc.) based on promises of raising a baby's IQ.
This is the power of IQ testing sans DNA science. Until recently, the two were separate entities, with IQ tests indicating a coefficient created from individual responses to written questions and genetic tests indicating some disease susceptibility based on a sequence of one's DNA. Yet in recent years, scientists have begun to unlock the secrets of inherited aspects of intelligence with genetic analyses that scan millions of points of variation in DNA. Both bench scientists and direct-to-consumer companies have used these new technologies to find variants associated with exceptional IQ scores. There are a number of tests on the open market that parents and educators can use at will. These tests purport to reveal whether a child is inherently predisposed to be intelligent, and some suggest ways to track them for success.
I started looking into these tests when I was doing research for my book, "Social by Nature: The Promise and Peril of Sociogenomics." This book investigated the new genetic science of social phenomena, like educational attainment and political persuasion, investment strategies, and health habits. I learned that, while many of the scientists doing much of the basic research into these things cautioned that the effects of genetic factors were quite small, most saw testing as one data point among many that could help to somehow level the playing field for young people. The rationale went that in certain circumstances, some needed help more than others. Why not put our collective resources together to help them?
Good nutrition, support at home, and access to healthcare and education make a huge difference in how people do.
Some experts believed so strongly in the power of DNA behavioral prediction that they argued it would be unfair not to use predictors to determine a kid's future, prevent negative outcomes, and promote the possibility for positive ones. The educators out in the wider world that I spoke with agreed. With careful attention, they thought sociogenomic tests could help young people get the push they needed when they possessed DNA sequences that weren't working in their favor. Officials working with troubled youth told me they hoped DNA data could be marshaled early enough that kids would thrive at home and in school, thereby avoiding ending up in their care. While my conversations with folks centered around sociogenomic data in general, genetic IQ prediction was completely entangled in it all.
I present these prevailing views to demonstrate both the widespread appeal of genetic predictors as well as the well-meaning intentions of those in favor of using them. It's a truly progressive notion to help those who need help the most. But we must question whether genetic predictors are data points worth looking at.
When we examine the way DNA IQ predictors are generated, we see scientists grouping people with similar IQ test results and academic achievements, and then searching for the DNA those people have in common. But there's a lot more to scores and achievements than meets the eye. Good nutrition, support at home, and access to healthcare and education make a huge difference in how people do. Therefore, the first problem with using DNA IQ predictors is that the data points themselves may be compromised by numerous inaccuracies.
We must then ask ourselves where the deep, enduring inequities in our society are really coming from. A deluge of research has shown that poor life outcomes are a product of social inequalities, like toxic living conditions, underfunded schools, and unhealthy jobs. A wealth of research has also shown that race, gender, sexuality, and class heavily influence life outcomes in numerous ways. Parents and caregivers feed, talk, and play differently with babies of different genders. Teachers treat girls and boys, as well as members of different racial and ethnic backgrounds, differently to the point where they do better and worse in different subject areas.
Healthcare providers consistently racially profile, using diagnostics and prescribing therapies differently for the same health conditions. Access to good schools and healthcare are strongly mitigated by one's race and socioeconomic status. But even youth from privileged backgrounds suffer worse health and life outcomes when they identify or are identified as queer. These are but a few examples of the ways in which social inequities affect our chances in life. Therefore, the second problem with using DNA IQ predictors is that it obscures these very real, and frankly lethal, determinants. Instead of attending to the social environment, parents and educators take inborn genetics as the reason for a child's successes or failures.
It is time that we shift our priorities from seeking genetic causes to fixing the social causes we know to be real.
The other problem with using DNA IQ predictors is that research into the weightiness of DNA evidence has shown time and again that people take DNA evidence more seriously than they do other kinds of evidence. So it's not realistic to say that we can just consider IQ genetics as merely one tiny data point. People will always give more weight to DNA evidence than it deserves. And given its proven negligible effect, it would be irresponsible to do so.
It is time that we shift our priorities from seeking genetic causes to fixing the social causes we know to be real. Parents and educators need to be wary of solutions aimed at them and their individual children.
[Editor's Note: Read another perspective in the series here.]
A new type of cancer therapy is shrinking deadly brain tumors with just one treatment
Few cancers are deadlier than glioblastomas—aggressive and lethal tumors that originate in the brain or spinal cord. Five years after diagnosis, less than five percent of glioblastoma patients are still alive—and more often, glioblastoma patients live just 14 months on average after receiving a diagnosis.
But an ongoing clinical trial at Mass General Cancer Center is giving new hope to glioblastoma patients and their families. The trial, called INCIPIENT, is meant to evaluate the effects of a special type of immune cell, called CAR-T cells, on patients with recurrent glioblastoma.
How CAR-T cell therapy works
CAR-T cell therapy is a type of cancer treatment called immunotherapy, where doctors modify a patient’s own immune system specifically to find and destroy cancer cells. In CAR-T cell therapy, doctors extract the patient’s T-cells, which are immune system cells that help fight off disease—particularly cancer. These T-cells are harvested from the patient and then genetically modified in a lab to produce proteins on their surface called chimeric antigen receptors (thus becoming CAR-T cells), which makes them able to bind to a specific protein on the patient’s cancer cells. Once modified, these CAR-T cells are grown in the lab for several weeks so that they can multiply into an army of millions. When enough cells have been grown, these super-charged T-cells are infused back into the patient where they can then seek out cancer cells, bind to them, and destroy them. CAR-T cell therapies have been approved by the US Food and Drug Administration (FDA) to treat certain types of lymphomas and leukemias, as well as multiple myeloma, but haven’t been approved to treat glioblastomas—yet.
CAR-T cell therapies don’t always work against solid tumors, such as glioblastomas. Because solid tumors contain different kinds of cancer cells, some cells can evade the immune system’s detection even after CAR-T cell therapy, according to a press release from Massachusetts General Hospital. For the INCIPIENT trial, researchers modified the CAR-T cells even further in hopes of making them more effective against solid tumors. These second-generation CAR-T cells (called CARv3-TEAM-E T cells) contain special antibodies that attack EFGR, a protein expressed in the majority of glioblastoma tumors. Unlike other CAR-T cell therapies, these particular CAR-T cells were designed to be directly injected into the patient’s brain.
The INCIPIENT trial results
The INCIPIENT trial involved three patients who were enrolled in the study between March and July 2023. All three patients—a 72-year-old man, a 74-year-old man, and a 57-year-old woman—were treated with chemo and radiation and enrolled in the trial with CAR-T cells after their glioblastoma tumors came back.
The results, which were published earlier this year in the New England Journal of Medicine (NEJM), were called “rapid” and “dramatic” by doctors involved in the trial. After just a single infusion of the CAR-T cells, each patient experienced a significant reduction in their tumor sizes. Just two days after receiving the infusion, the glioblastoma tumor of the 72-year-old man decreased by nearly twenty percent. Just two months later the tumor had shrunk by an astonishing 60 percent, and the change was maintained for more than six months. The most dramatic result was in the 57-year-old female patient, whose tumor shrank nearly completely after just one infusion of the CAR-T cells.
The results of the INCIPIENT trial were unexpected and astonishing—but unfortunately, they were also temporary. For all three patients, the tumors eventually began to grow back regardless of the CAR-T cell infusions. According to the press release from MGH, the medical team is now considering treating each patient with multiple infusions or prefacing each treatment with chemotherapy to prolong the response.
While there is still “more to do,” says co-author of the study neuro-oncologist Dr. Elizabeth Gerstner, the results are still promising. If nothing else, these second-generation CAR-T cell infusions may someday be able to give patients more time than traditional treatments would allow.
“These results are exciting but they are also just the beginning,” says Dr. Marcela Maus, a doctor and professor of medicine at Mass General who was involved in the clinical trial. “They tell us that we are on the right track in pursuing a therapy that has the potential to change the outlook for this intractable disease.”
Since the early 2000s, AI systems have eliminated more than 1.7 million jobs, and that number will only increase as AI improves. Some research estimates that by 2025, AI will eliminate more than 85 million jobs.
But for all the talk about job security, AI is also proving to be a powerful tool in healthcare—specifically, cancer detection. One recently published study has shown that, remarkably, artificial intelligence was able to detect 20 percent more cancers in imaging scans than radiologists alone.
Published in The Lancet Oncology, the study analyzed the scans of 80,000 Swedish women with a moderate hereditary risk of breast cancer who had undergone a mammogram between April 2021 and July 2022. Half of these scans were read by AI and then a radiologist to double-check the findings. The second group of scans was read by two researchers without the help of AI. (Currently, the standard of care across Europe is to have two radiologists analyze a scan before diagnosing a patient with breast cancer.)
The study showed that the AI group detected cancer in 6 out of every 1,000 scans, while the radiologists detected cancer in 5 per 1,000 scans. In other words, AI found 20 percent more cancers than the highly-trained radiologists.
Scientists have been using MRI images (like the ones pictured here) to train artificial intelligence to detect cancers earlier and with more accuracy. Here, MIT's AI system, MIRAI, looks for patterns in a patient's mammograms to detect breast cancer earlier than ever before. news.mit.edu
But even though the AI was better able to pinpoint cancer on an image, it doesn’t mean radiologists will soon be out of a job. Dr. Laura Heacock, a breast radiologist at NYU, said in an interview with CNN that radiologists do much more than simply screening mammograms, and that even well-trained technology can make errors. “These tools work best when paired with highly-trained radiologists who make the final call on your mammogram. Think of it as a tool like a stethoscope for a cardiologist.”
AI is still an emerging technology, but more and more doctors are using them to detect different cancers. For example, researchers at MIT have developed a program called MIRAI, which looks at patterns in patient mammograms across a series of scans and uses an algorithm to model a patient's risk of developing breast cancer over time. The program was "trained" with more than 200,000 breast imaging scans from Massachusetts General Hospital and has been tested on over 100,000 women in different hospitals across the world. According to MIT, MIRAI "has been shown to be more accurate in predicting the risk for developing breast cancer in the short term (over a 3-year period) compared to traditional tools." It has also been able to detect breast cancer up to five years before a patient receives a diagnosis.
The challenges for cancer-detecting AI tools now is not just accuracy. AI tools are also being challenged to perform consistently well across different ages, races, and breast density profiles, particularly given the increased risks that different women face. For example, Black women are 42 percent more likely than white women to die from breast cancer, despite having nearly the same rates of breast cancer as white women. Recently, an FDA-approved AI device for screening breast cancer has come under fire for wrongly detecting cancer in Black patients significantly more often than white patients.
As AI technology improves, radiologists will be able to accurately scan a more diverse set of patients at a larger volume than ever before, potentially saving more lives than ever.