This is an article from a new blog by Sasha Gusev, a statistical geneticist and a professor at Harvard medical school. I thought this article was a pretty good introduction to technical issues...
This is an article from a new blog by Sasha Gusev, a statistical geneticist and a professor at Harvard medical school. I thought this article was a pretty good introduction to technical issues around IQ and genetic testing:
This is called a genetic association and each GWAS estimates millions of them. Even though these associations are genetic, they are still mostly not causal, for three fundamental reasons:
Variants that are nearby in the genome are correlated, so any causal variant will often have many other correlated non-causal associations around it. In truth, these studies do not identify variants, but rather large associated regions with many correlated associations of which only one or two are truly causal. […]
Variants are inherited from parents (and grand-parents, and so on), so if a variant is actually doing its something in your parents (or uncles, or grand-parents, etc) and that has an impact on your phenotype, it will naively look like it’s doing something in you. […]
Even variants that do nothing at all in anyone can still be picked up as associations if they are incidentally correlated with the phenotype through population stratification or other technical confounders. […]
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[O]ne of the ways non-causal correlations sneak into the genetic predictor is through intergenerational correlation of phenotypes. While for most traits this type of confounding appears to be negligible, for traits related to education and socioeconomic status it is substantial. A simple way to appreciate this is to look at how a genetic predictor of education changes when adding parent education into the model: when [Lee et al. 2018] added parental education to a model with a genetic predictor of educational attainment, the accuracy of the genetic predictor dropped from 12% to 5%. Thus, you can already approximate the majority of your genetic score simply by asking your parents how much schooling they received. Indeed, parental education alone achieved a prediction accuracy of >20% for educational attainment in [Lee et al. 2018], greater than what could be explained by all GWAS variants.
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It is tempting to conclude that [indirect effects] capture genetic variation acting through parenting, and may thus be predictors for the next generation. The field has, in fact, often leapt to this conclusion by labeling such effects “genetic nurture”. However, recent work looking at educational achievement (more-or-less a proxy for IQ from the perspective of genetics) has revealed that these “indirect” correlations may not be acting exclusively within nuclear families (i.e. through parenting), but could be entirely explained by genetic variation in extended family members such as uncles and aunts [Nivard et al. 2024]. Thus, the already weak predictive accuracies described above are expected to be substantially attenuated by environmental factors including — unsurprisingly — broader “dynastic” education and wealth.
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[M]y critique here is fundamentally methodological: genetic prediction of cognitive phenotypes simply exhibits more confounds than most other traits.
This is an article from a new blog by Sasha Gusev, a statistical geneticist and a professor at Harvard medical school. I thought this article was a pretty good introduction to technical issues around IQ and genetic testing:
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