It is becoming increasingly clear that for many traits, the genetic effect sizes estimated by genetic association studies are substantially inflated for a few reasons. These include confounding due to uncontrolled population stratification, such as dynastic effects, and perhaps also genetic nurture[1]. It is also clear that traits strongly mediated through society and behaviour, such as cognitive ability, are especially strongly affected by these mechanisms.
You can avoid much of this confounding by performing GWAS on only the differences between siblings (“within-sibship GWAS”) or between other pairs of family members (“family GWAS”). When you do this for cognitive ability, you find substantial deflation: heritability estimates decrease by around 45%, and the effect sizes estimated in population-level GWAS only correlate with these more direct effect estimates by about 0.55.
Does your analysis take this into account, for instance by using effect sizes estimated by within-sibship/family GWAS? If not, it would follow that genome editing would yield substantially lower increases in IQ than you estimate.
Genetic nurture is a little complicated. The classic example in the GWAS context is parental genetic nurture. Here, you find an effect of a genetic variant on a trait in people, but the actual “direct” effect manifests only in those people’sparents – one of whom must carry the variant, otherwise it would not be observed in the people in your study – and affected how they nurtured their kid, which then affected the trait you were measuring in their offspring. Genetically editing such a variant into an embryo would thus have no effect on that embryo when they are born and develop.
We accounted for inflation of effect sizes due to assortative mating, assuming a mate IQ correlation of 0.4 and total additive heritability of 0.7 for IQ.
IIUC that R = 0.55 number was just the raw correlation between the beta values of the sibling and population GWASes, which is going to be very noisy given the small sample sizes and given that effects are sparse. You can see that the LDSC based estimate is nearly 1, suggesting ~null indirect effects.
Subtle population stratification not accounted for by the original GWAS could still be an issue, though I don’t expect this would inflate the effects very much. If we had access to raw data we could take into account small correlations between distant variants during finemapping, which would automatically handle assortative mating and stratification.
I think you should take seriously that in the first paper linked in my comment, the population-wide SNP heritability for cognitive ability is estimated at 0.24 and the within-sibship heritability at 0.14. This is very far from the 0.7 estimate from twin studies. While a perfect estimate of direct additive heritability would be higher than 0.14, I don’t think that rare variants (and gene-gene interactions, but this would no longer be additive heritability) would get you anywhere close to 0.7. Note also that UK Biobank with its purportedly poor IQ test represents only ~30% of the sample size in that paper.
Instead, I think it is becoming clear that traditional twin studies made overly strong assumptions about shared and non-shared environments, such that they over-estimated the contribution of genetics to all kinds of traits from height to blood creatinine concentration (compare gold-standard RDR estimates vs twin estimates here). As implied in my original comment, this is likely especially true for traits strongly mediated by society and behaviour. I find it somewhat counter-intuitive, but this kind of finding keeps cropping up again and again in papers that estimate direct heritability with the most current methods.
I’ll need to do a deep dive to understand the methods of the first paper, but isn’t this contradicted by the recent Tan et. al. paper you linked finding SNP heritability of 0.19 for both direct and population effects of intelligence (which matches Savage Jansen 2018)? They also found ~perfect LDSC correlation between direct and population effects, which would imply the direct and population SNP heritabilities are tagging the exact same genetic effects. (Also interesting that 0.19 is the exactly in the middle of 0.24 and 0.14, not sure what to make of that if anything).
It is becoming increasingly clear that for many traits, the genetic effect sizes estimated by genetic association studies are substantially inflated for a few reasons. These include confounding due to uncontrolled population stratification, such as dynastic effects, and perhaps also genetic nurture[1]. It is also clear that traits strongly mediated through society and behaviour, such as cognitive ability, are especially strongly affected by these mechanisms.
You can avoid much of this confounding by performing GWAS on only the differences between siblings (“within-sibship GWAS”) or between other pairs of family members (“family GWAS”). When you do this for cognitive ability, you find substantial deflation: heritability estimates decrease by around 45%, and the effect sizes estimated in population-level GWAS only correlate with these more direct effect estimates by about 0.55.
Does your analysis take this into account, for instance by using effect sizes estimated by within-sibship/family GWAS? If not, it would follow that genome editing would yield substantially lower increases in IQ than you estimate.
Genetic nurture is a little complicated. The classic example in the GWAS context is parental genetic nurture. Here, you find an effect of a genetic variant on a trait in people, but the actual “direct” effect manifests only in those people’s parents – one of whom must carry the variant, otherwise it would not be observed in the people in your study – and affected how they nurtured their kid, which then affected the trait you were measuring in their offspring. Genetically editing such a variant into an embryo would thus have no effect on that embryo when they are born and develop.
We accounted for inflation of effect sizes due to assortative mating, assuming a mate IQ correlation of 0.4 and total additive heritability of 0.7 for IQ.
IIUC that R = 0.55 number was just the raw correlation between the beta values of the sibling and population GWASes, which is going to be very noisy given the small sample sizes and given that effects are sparse.You can see that the LDSC based estimate is nearly 1, suggesting ~null indirect effects.Subtle population stratification not accounted for by the original GWAS could still be an issue, though I don’t expect this would inflate the effects very much. If we had access to raw data we could take into account small correlations between distant variants during finemapping, which would automatically handle assortative mating and stratification.
Actually I don’t think this is correct, it accounted for sampling error somehow. I’ll need to look into this deeper.
I think you should take seriously that in the first paper linked in my comment, the population-wide SNP heritability for cognitive ability is estimated at 0.24 and the within-sibship heritability at 0.14. This is very far from the 0.7 estimate from twin studies. While a perfect estimate of direct additive heritability would be higher than 0.14, I don’t think that rare variants (and gene-gene interactions, but this would no longer be additive heritability) would get you anywhere close to 0.7. Note also that UK Biobank with its purportedly poor IQ test represents only ~30% of the sample size in that paper.
Instead, I think it is becoming clear that traditional twin studies made overly strong assumptions about shared and non-shared environments, such that they over-estimated the contribution of genetics to all kinds of traits from height to blood creatinine concentration (compare gold-standard RDR estimates vs twin estimates here). As implied in my original comment, this is likely especially true for traits strongly mediated by society and behaviour. I find it somewhat counter-intuitive, but this kind of finding keeps cropping up again and again in papers that estimate direct heritability with the most current methods.
I’ll need to do a deep dive to understand the methods of the first paper, but isn’t this contradicted by the recent Tan et. al. paper you linked finding SNP heritability of 0.19 for both direct and population effects of intelligence (which matches Savage Jansen 2018)? They also found ~perfect LDSC correlation between direct and population effects, which would imply the direct and population SNP heritabilities are tagging the exact same genetic effects. (Also interesting that 0.19 is the exactly in the middle of 0.24 and 0.14, not sure what to make of that if anything).