Philip Ball has an article in the December issue of Prospect (ungated on his blog) arguing that consideration of the genetic basis to social problems is a distraction from socioeconomic causes. The strawman punchline for the Prospect article is “It’s delusional to believe that everything can be explained by genetics”.
The article has drawn a response from one of the people named in the article, Dominic Cummings. Ball suggests that Cummings presents “genetics as a fait accompli – if you don’t have the right genes, nothing much will help”, although this statement suggests Ball had not invested much effort getting across Cummings’s actual position (as contained in this now infamous essay). Ball responded in turn, with Cummings firing back (in an update at the bottom of the page), and Ball responding again.
Beyond the tit for tat – read their respective posts for that – there are some interesting points about whether genetics tells us anything about education policy.
As a start, Ball claims that “Social class remains the strongest predictor of educational achievement in the UK”, referencing this article. However, the authors of that article don’t consider the role of genetics or other potential predictors. The references that article gives for the claim are similarly devoid of relevant comparisons, which is unsurprising as they largely comprise policy positioning documents from various organisations. It’s hard to credibly claim something is a superior predictor when it is not assessed against the alternatives.
So, what is the evidence on this point? For one, we have twin and adoption studies. As a sample, Bruce Sacerdote studied Korean adoptees into the United States (admittedly, not the UK as per the quote) and found that shared environment (which would include socioeconomic status) explained 16 per cent of the variation in educational attainment. Genetic factors explained 44 per cent. This is a consistent finding in adoption studies, with children more closely resembling their biological parents than their adopted parents. For twin studies, an Australian analysis found a 57 per cent genetic and 24 per cent shared environment contribution to variation in education. A meta-analysis of heritability estimates of educational attainment found that, in the majority of samples, genetic variation explained more of the variation in educational attainment than shared environment.
Of course, we don’t have the genetic data or understanding at hand just yet, but there are other factors such as IQ that are better predictors of education than social class. This territory is also complicated – there are genetic effects on both IQ and social class – but IQ tends to outperform. This meta-analysis shows that IQ is a better predictor of education, income and occupation than socioeconomic status – not overwhelmingly so, but superior nonetheless.
Then there is the link between genetic factors and socioeconomic status, with a long line of studies finding a relationship. One of the more recent was by Daniel Benjamin and friends (ungated pdf). They found heritability of permanent income (20-year average) of 0.58 for men and 0.46 for women. Part of the predictive power of socioeconomic status comes from its genetic basis. Gregory Clark’s hypothesis of low social mobility being a result of genetic factors reflects this body of work.
Turning next to Ball’s pessimism of the future of genetics, he states:
In September an international consortium led by Daniel Benjamin of Cornell University in New York reported on a search for genes linked to cognitive ability using a new statistical method that overcomes the weaknesses of traditional surveys. The method cross-checks such putative associations against a “proxy phenotype” – a trait that can ‘stand in’ for the one being probed. In this case the proxy for cognitive performance was the number of years that the tens of thousands of test subjects spent in education.
From several intelligence-linked genes claimed in previous work, only three survived this scrutiny. More to the point, those three were able to account for only a tiny fraction of the inheritable differences in IQ. Someone blessed with two copies of all three of the “favourable” gene variants could expect a boost of just 1.8 IQ points relative to someone with none of these variants. As the authors themselves admitted, the three gene variants are “not useful for predicting any particular individual’s performance because the effect sizes are far too small”.
This, however, is only part of the picture. If we look at another study in which Benjamin was involved, three SNPs (single nucleotide polymorphisms – single base changes in the DNA code) were found to affect educational attainment. In total, they explained 0.02 per cent of the variation in educational attainment – practically nothing. But combine all the SNPs in the 100,000 person sample, and you edge up to 2.5 per cent. But even more interesting, they calculated that with a large enough sample they could explain over 20 per cent of the variation. Co-author Philipp Koellinger explains this in a video I recently linked. Although this study found variants with low explanatory power, it also points to the potential to explain much more with larger samples.
For more on the background to the feasibility of identifying the causal genetic variants for traits such as IQ, its worth looking at this paper by Steve Hsu. Possibly the most important point is that the causal variants for traits such as cognitive ability and height are additive in their effect. In his final response, Ball states that “And that might be because we are thinking the wrong way – too linearly – about how many if not most genes actually operate.” But the evidence shows that is how they largely work. Although a few years old now, this paper’s theoretical and empirical argument that genetic effects are largely additive has generally been affirmed in later research. This considerably simplifies the task of predicting outcomes based on someone’s genome. In fact, this is one reason selective breeding has been so successful and genetic data is already being used successfully in cattle breeding (There’s an example of the gap between entrepreneurship and policy development – while some of us are arguing whether this stuff is possible, someone else is already doing it).
Now, supposing you have this genetic data, how might this change education? Returning to the article I linked above (ungated pdf), Benjamin and friends suggested this genetic information could be used to better target interventions. They propose early identification of dyslexia as an example.
They also suggest using genetic data as controls. This could provide more precision in studies of whether interventions to target socioeconomically disadvantaged children are effective. The genetic controls allow you to hone in on what you are interested in. In the question and answer session of a video of talk by Jason Fletcher I recently linked, Benjamin pointed to the famous Perry PreSchool Project and noted that additional precision through the use of genetic data would have been of great value.
Ball also indirectly alludes to another reason to learn about genetic factors. In his last response, he writes:
Personally, I find a little chilling the idea that we might try to predict children’s attainments by reading their genome, and gear their education accordingly – not least because we know (and Plomin would of course acknowledge this) that genes operate in conjunction with their environment, and so whatever genetic hand you have been dealt, its outcomes are contingent on experience.
This argument runs both ways. Supposing there are large gene-environment interactions, how can you understand the effects of changing the environment without looking at the way that environment affects people via their genome? As an example of this, Jason Fletcher examined how variation in a gene changed the response to tobacco taxation policy (he talks about this in a video I recently linked). Those with a certain allele responded to taxation and reduced smoking. Others didn’t. Too be honest, I’m not sold on the results of this particular study, but it illustrates that genetic factors that need to be considered if these gene-environment interactions are as large as people such as Ball believe.
[I should admit at this point that G is for Genes: The Impact of Genetics on Education and Achievement is sitting unread in my reading pile….]
Putting it together, Ball is off track in his suggestion that learning about and targeting genetic factors distracts from dealing with socioeconomic issues. Understanding of genetic and socioeconomic factors are complements, and by disentangling their effects, we could better tailor education to address each.
That is not to say that the genetic enterprise is guaranteed to be successful. But there is plenty of evidence that our genes are relevant and, on that basis, should be considered.
Further, there are changes we can make today. Ball asks what genetics can add beyond recognition that some children are more talented than others. The thing is, much schooling is still structured as though we are blank slates. Maybe it is an understanding of genetics that will finally get us to a point where education is better designed for people with different capacities, improving the experience across the full range of abilities and backgrounds.