## Genetics

• Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals by Okbay et al. This is the newest iteration of the educational attainment GWAS by the SSGAC consortium, now with a sample of three million people. It was published today and I have only skimmed it. The number of SNPs identified is about 4,000 now, up from 1,300 in the previous GWAS, while the $R^2$ increased from 11–13% to 12–16%, depending on the validation sample. They also conclude that there are no common SNPs with substantial dominance effects for educational attainment, underlining the validity of the additive genetic model. The within-family effect sizes are about 56% of the population effect sizes for educational attainment, while the same ratio is 82% for IQ and more than 90% for height and BMI. The discrepancy between the within-family and population estimates is probably mostly due to indirect genetic effects (“genetic nurture”) and assortative mating. Replicating SNP effects from the previous, smaller education GWAS sample, they find that 99.7% of the SNP effects have matching signs in the new data, and that 93% are significant at the 1% level or lower, which fits theoretical predictions well (it seems that the GWAS enterprise has vindicated the much-derided null hypothesis significance testing paradigm).
• Cross-trait assortative mating is widespread and inflates genetic correlation estimates by Border et al. The genetic correlation is a statistic measuring the extent to which genetic effects on two different traits are correlated. It is easy enough to calculate, but not easy to interpret because while the simplest interpretation is pleiotropy, several other causal and non-causal explanations are possible. This paper suggests that many genetic correlations are non-causal and result from cross-assortative mating, e.g., smarter than average women preferring to have children with taller than average men, which leads to a genetic correlation between IQ and height genes in the next generation even if height genes have no effect on IQ nor IQ genes on height. Among other findings, the paper suggests that the importance of the general factor of psychopathology has been overestimated due to a failure to consider cross-trait assortative mating.
• Modeling assortative mating and genetic similarities between partners, siblings, and in-laws by Torvik et al. This is a nice example of using psychometric methods to infer latent genetic parameters.
• Behavioral geneticist Lindon Eaves has died. He was one of the major creative forces behind the methodology of modern twin and family studies. I did not know that he was also an ordained Anglican priest. You do not see many men of the cloth in science these days (or at least their creed is rather different now). Eric Turkheimer says that he never heard Eaves utter an illiberal word, but I do notice some forbidden literature on his bookshelf at the first link.

## Miscellaneous

• More waves in longitudinal studies do not help against initial sampling error by Emil Kirkegaard. Speaking of James Heckman, he has published another one of his endless reanalyses of the Perry Preschool study. Emil has a fun takedown of this absurd enterprise.
• Assortative Mating and the Industrial Revolution: England, 1754-2021 by Clark & Cummings. In another installment of Greg Clark’s studies into the persistence of social status across generations, he has apparently found a constant, latent status correlation of 0.80 between spouses in England over the last few centuries. This suggest that grooms and brides matched tightly on underlying educational and occupational abilities even when higher education was rare and female participation in the labor market was limited. I have previously commented briefly on Clark’s work and the role of assortative mating in it here