Some of you have been reading me since 2002. Therefore, you’ve seen a lot of changes in my interests (and to a lesser extent, my life…no more cat pictures because my cats died). Whereas today I incessantly flog Who We Are and How We Got Here: Ancient DNA and the New Science of the Human Past, in 2002 I would talk about Steven Pinker’s The Blank Slate: The Modern Denial of Human Nature quite a bit. The reason I don’t talk much about The Blank Slate is that some point in the 2000s I realized my future deep interests were going to be in population genetics, rather than behavior genetics and cognitive psychology. If you are not a specialist who doesn’t follow the literature. Who doesn’t “read the supplements”. You’re going to stop gaining anything more from books at a certain point.
Similarly, after I read In Gods We Trust: The Evolutionary Landscape of Religion, I read a lot of books on the cognitive anthropology of religion. Until I didn’t. Now that Harvey Whitehouse has teamed up with Peter Turchin, I suspect I’ll check in on this literature again.
But life comes at you fast. Today I think the broad thesis of The Blank Slate seems so correct, that we are not a “blank slate”, that no one would argue with that. Rather, the implications of that thesis are highly “problematic,” and social and cultural constructionism has really gone much further on the Left operationally than they were in the early 2000s. To give a concrete example, you can admit that sex differences are real and significant, but you have to be very careful in mentioning or highlighting specific instances or cases where they matter.
Moving to a more controversial topic, for a long while I’ve pretty much ignored the genomic study of the normal variation of cognition. The reason is that until recently all the studies were very underpowered to detect much of anything. The sample sizes were too small in relation to the genetic architecture of the trait because of the “Fourth Law of Behavior Genetics.”
As 2018 proceeds I think we can say that we are now in new territory. On Twitter, Steve Hsu seems positively ecstatic over a paper that just came out in PNAS. His blog post, Game Over: Genomic Prediction of Social Mobility summarizes it pretty well, but you should read the open access paper.
Genetic analysis of social-class mobility in five longitudinal studies:
Genome-wide association study (GWAS) discoveries about educational attainment have raised questions about the meaning of the genetics of success. These discoveries could offer clues about biological mechanisms or, because children inherit genetics and social class from parents, education-linked genetics could be spurious correlates of socially transmitted advantages. To distinguish between these hypotheses, we studied social mobility in five cohorts from three countries. We found that people with more education-linked genetics were more successful compared with parents and siblings. We also found mothers’ education-linked genetics predicted their children’s attainment over and above the children’s own genetics, indicating an environmentally mediated genetic effect. Findings reject pure social-transmission explanations of education GWAS discoveries. Instead, genetics influences attainment directly through social mobility and indirectly through family environments.
Why does this matter? I’m assuming most of you have seen charts like the ones below, which “prove” how the game is rigged against the poor:
The problem that most behavior geneticists immediately have with these popular analyses, which now suffuse our public culture (e.g., the “representation” argument in academic science often takes as a cartoonish model that all groups will have equal representation in all fields given no discrimination; substantively almost everyone believes this isn’t true in some way, but for the sake of argumentation this is a bullet-proof line of attack which every white male academic is going to retreat away from), is that they ignore genetic confounds. This paper is an attempt to address that. Measure it. Quantify it. Characterize it.
The two most interesting results for me have to do with siblings and mothers. Unsurprisingly siblings who have a higher predicted educational attainment score genetically tend to have higher educational attainments. As you know, siblings vary in relatedness. They vary in the segregation of alleles from their parents. Some siblings are tall. Some are short. This is due to variation in genetics across the pedigree. People within a family are related to each other, but unless you are talking Targaryens they aren’t exactly alike. Similarly, some siblings are smart and some are not so smart, because they’re “born that way.”
We knew that. Soon we’ll understand that genomically I suspect.
Second, we see again the importance of maternal effects and non-transmitted alleles. Mothers who have a higher predicted level of education have children with more education even if those children don’t inherit those alleles.* One natural conclusion here is mothers with a particular disposition shaped by genes are creating particular environments for their children, and those environments let them flourish even if they do not have their mother’s genetic endowments. This actually has “news you can use” implications in life choices people make in relation to their partners.
The study ends on a cautionary note. Residual population substructure can cause issues, correcting which can attenuate or eliminate such subtle and small signals. The sample sizes could always get bigger. And ethnically diverse panels have to come into the picture at some point.
But Razib abides. This study had a combined sample size of >20,000 individuals. Then you have the other recent paper with 270,000 individuals, Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence. All well and good, but I wait for greater things. There is no shame in waiting for better things. And I prophesy that a greater sample size shall come to pass before this year turns into the new.
And you know what’s better than 1 million samples? How about 1 billion samples!
* Note that the models are controlling for a lot of background socioeconomic variables.