An Enormous Study of the Genes Related to Staying in School
When scientists publish their research, it’s rare for them to write an accompanying FAQ that explains what they found and what it means. It’s especially rare for that FAQ to be three times longer than the research paper itself. But Daniel Benjamin and his colleagues felt the need to do so, because they work on a topic that is frequently and easily misunderstood: the genetics of education…
Now, after scanning the genomes of 1,100,000 people of European descent—one of the largest studies of this kind—they have a much bigger list of 1,271 education-associated genetic variants. The team—which includes Peter Visscher, David Cesarini, James Lee, Robbee Wedow, and Aysu Okbay—also identified hundreds of variants that are associated with math skills and performance on tests of mental abilities.
The team hasn’t discovered “genes for education.” Instead, many of these variants affect genes that are active in the brains of fetuses and newborns. These genes influence the creation of neurons and other brain cells, the chemicals these cells secrete, the way they react to new information, and the way they connect with each other. This biology affects our psychology, which in turn affects how we move through the education system.
This isn’t to say that staying in school is “in the genes.” Each genetic variant has a tiny effect on its own, and even together, they don’t control people’s fates. The team showed this by creating a “polygenic score”—a tool that accounts for variants across a person’s entire genome to predict how much formal education they’re likely to receive. It does a lousy job of predicting the outcome for any specific individual, but it can explain 11 percent of the population-wide variation in years of schooling.
That’s terrible when compared with, say, weather forecasts, which can correctly predict about 95 percent of the variation in day-to-day temperatures. But when it comes to predicting education, it’s comparable to classic factors such as household income or how educated your parents are. “Within social science, that’s basically unheard of,” says Benjamin, who works at the University of Southern California, Los Angeles. “We can explain education as well with saliva samples as with demographics.”…
On the flip side, there are fears that this kind of research could lead to discrimination against, or stigmatization of, people with certain genetic variants. Such fears aren’t unreasonable: Many forefathers of genetics were also proponents of eugenics, advocating that people with supposedly inferior genes should be discouraged from reproducing.
Paige Harden, a clinical psychologist at the University of Texas at Austin, thinks that neither these dystopian perils nor the more beneficent applications around personalized education are realistic. “I don’t think that’s where we are,” she says, because one still cannot use a person’s DNA to accurately predict their scholastic fates.
Benjamin and his colleagues agree. Look at the graph below, which plots years of schooling against subjects’ polygenic scores. Each dot is a person. Sure, on average, those with higher scores got more education than those with the lowest. But for any given score, there are huge variations in years of schooling. “Should we use the score to put some people into more advanced classes and others into more remedial classes?” says Benjamin. “That’s a total nonstarter because of the low predictive power for any given individual.” The same applies to mathematical prowess, or overall cognitive ability…
“It’s actually quite reassuring in showing that you could not accurately predict educational outcome from DNA,” says Dorothy Bishop, a neuroscientist and geneticist at the University of Oxford. “The disturbing scenario of people screening babies in the hope of selecting the brightest does not seem supported by this study.”
Indeed, Benjamin suspects that there’s a ceiling of accuracy that his team has almost hit. Even if they study millions more people, he believes they won’t be able to predict the educational fate of a single person with much more reliability than they can now. For that reason, the team have this to say in their FAQ:
What policy lessons or practical advice do you draw from this study?
None whatsoever. Any practical response—individual or policy-level—to this or similar research would be extremely premature and unsupported by the science…
Perhaps counterintuitively, Benjamin thinks that his team’s research “is really important for research on improving educational systems.” To understand how, forget genes for a moment, and think about wealth.
It’s uncontroversial to say that people who are born into rich families are more likely to fare better in school than those from poorer backgrounds. Of course, poor kids can still soar in school, and rich ones can flunk out, but few would deny that money is a powerful influence on people’s futures. Now, consider that household income explains just 7 percent of the variation in educational attainment, which is less than what genes can now account for. “Most social scientists wouldn’t do a study without accounting for socioeconomic status, even if that’s not what they’re interested in,” says Harden. The same ought to be true of our genes.
Imagine that authorities are planning to provide free preschool to kids from disadvantaged backgrounds. To see if such a policy actually helps children stay in school for longer, scientists would randomly assign the free classes to some kids but not others. Then, they would look at how the two groups fared. In doing so, they’d always try to account for factors like wealth that might also vary between the two groups. Similarly, “you can now wash away the genetic effects so you don’t have to worry about them,” says Benjamin. And in doing so, researchers could more precisely work out whether a policy change has any benefits—and they could do it through smaller, cheaper studies.
This, he argues, is the most powerful reason to study the genetics of education or cognitive ability—and ironically, it has very little to do with genes. Instead, it’s a way of making social science more powerful.
The team is essentially studying genes so they can more thoroughly ignore them.
Courtesy of Doximity
J. Lee, Robbee Wedow, Aysu Okbay, Edward Kong, Omeed Maghzian, Meghan Zacher, Tuan Anh Nguyen-Viet, Peter Bowers, Julia Sidorenko, Richard Karlsson Linnér, Mark Alan Fontana, Tushar Kundu, Chanwook Lee, Hui L, Ruoxi Li, Rebecca Royer, Pascal N. Timshel1, Raymond K. Walters, Emily A. Willoughby, Loïc Yengo, 23andMe Research Team, COGENT (Cognitive Genomics Consortium), Social Science Genetic Association Consortium, Maris Alver, Yanchun Bao, David W. Clark, Felix R. Day, Nicholas A. Furlotte, Peter K. Joshi, Kathryn E. Kemper, Aaron Kleinman, Claudia Langenberg, Reedik Mägi, Joey W. Trampush, Shefali Setia Verma, Yang Wu, Max Lam, Jing Hua Zhao, Zhili Zheng, Jason D. Boardman, Harry Campbell, Jeremy Freese, Kathleen Mullan Harris, Caroline Hayward, Pamela Herd, Meena Kumari, Todd Lencz, Jian’an Luan, Anil K. Malhotra, Andres Metspalu11, Lili Milani, Ken K. Ong, John R. B. Perry, David J. Porteous, Marylyn D. Ritchie, Melissa C. Smart, Blair H. Smith, Joyce Y. Tung, Nicholas J. Wareham, James F. Wilson, Jonathan P. Beauchamp, Dalton C. Conley, Tõnu Esko, Steven F. Lehrer, Patrik K. E. Magnusson, Sven Oskarsson, Tune H. Pers, Matthew R. Robinson, Kevin Thom, Chelsea Watson, Christopher F. Chabris, Michelle N. Meyer, David I. Laibson, Jian Yang, Magnus Johannesson, Philipp D. Koellinger, Patrick Turley, Peter M. Visscher, Daniel J. Benjamin and David Cesarini. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nature Genetics| Vol 50 | August 2018 | 1112–1121 |
Here we conducted a large-scale genetic association analysis of educational attainment in a sample of approximately 1.1 million individuals and identify 1,271 independent genome-wide-significant SNPs. For the SNPs taken together, we found evidence of heterogeneous effects across environments. The SNPs implicate genes involved in brain-development processes and neuron-to-neuron communication. In a separate analysis of the X chromosome, we identify 10 independent genome-wide-significant SNPs and estimate a SNP heritability of around 0.3% in both men and women, consistent with partial dosage compensation. A joint (multi-phenotype) analysis of educational attainment and three related cognitive phenotypes generates polygenic scores that explain 11–13% of the variance in educational attainment and 7–10% of the variance in cognitive performance. This predic-tion accuracy substantially increases the utility of polygenic scores as tools in research.