You should show your calculation or your code, including all the data and parameter choices. Otherwise I can’t evaluate this.
The code is pretty complicated and not something I’d expect a non-expert (even a very smart one) to be able to quickly check over; it’s not just a 100 line python script. (Or even a very smart expert for that matter, more like anyone who wasn’t already familiar with our particular codebase.) We’ll likely open source it at some point in the future, possibly soon, but that’s not decided yet. Our finemapping (inferring causal effects) procedure produces ~identical results to the software from the paper I linked above when run on the same test data (though we handle some additional things like variable per-SNP sample sizes and missing SNPs which that finemapper doesn’t handle, which is why we didn’t just use it).
The parameter choices which determine the prior over SNP effects are the number of causal SNPs (which we set to 20,000) and the SNP heritability of the phenotype (which we set to 0.19, as per the GWAS we used). The erroneous effect size adjustment was done at the end to convert from the effect sizes of the GWAS phenotype (low reliability IQ test) to the effect sizes corresponding to the phenotype we care about (high reliability IQ test).
We want to publish a more detailed write up of our methods soon(ish), but it’s going to be a fair bit of work so don’t expect it overnight.
It’s natural in your position to scrutinize low estimates but not high ones.
Yep, fair enough. I’ve noticed myself doing this sometimes and I want to cut it out. That said, I don’t think small-ish predictable overestimates to the effect sizes are going to change the qualitative picture, since with good enough data and a few hundred to a thousand edits we can boost predicted IQ by >6 SD even with much more pessimistic assumptions, which probably isn’t even safe to do (I’m not sure I expect additivity to hold that far). I’m much more worried about basic problems with our modelling assumptions, e.g. the assumption of sparse causal SNPs with additive effects and no interactions (e.g. what if rare haplotypes are deleterious due to interactions that don’t show up in GWAS since those combinations are rare?).
The code is pretty complicated and not something I’d expect a non-expert (even a very smart one) to be able to quickly check over; it’s not just a 100 line python script. (Or even a very smart expert for that matter, more like anyone who wasn’t already familiar with our particular codebase.) We’ll likely open source it at some point in the future, possibly soon, but that’s not decided yet. Our finemapping (inferring causal effects) procedure produces ~identical results to the software from the paper I linked above when run on the same test data (though we handle some additional things like variable per-SNP sample sizes and missing SNPs which that finemapper doesn’t handle, which is why we didn’t just use it).
The parameter choices which determine the prior over SNP effects are the number of causal SNPs (which we set to 20,000) and the SNP heritability of the phenotype (which we set to 0.19, as per the GWAS we used). The erroneous effect size adjustment was done at the end to convert from the effect sizes of the GWAS phenotype (low reliability IQ test) to the effect sizes corresponding to the phenotype we care about (high reliability IQ test).
We want to publish a more detailed write up of our methods soon(ish), but it’s going to be a fair bit of work so don’t expect it overnight.
Yep, fair enough. I’ve noticed myself doing this sometimes and I want to cut it out. That said, I don’t think small-ish predictable overestimates to the effect sizes are going to change the qualitative picture, since with good enough data and a few hundred to a thousand edits we can boost predicted IQ by >6 SD even with much more pessimistic assumptions, which probably isn’t even safe to do (I’m not sure I expect additivity to hold that far). I’m much more worried about basic problems with our modelling assumptions, e.g. the assumption of sparse causal SNPs with additive effects and no interactions (e.g. what if rare haplotypes are deleterious due to interactions that don’t show up in GWAS since those combinations are rare?).