estimating SNP effects on residual

[Holly Poore]

Hello, 

I would like to estimate SNP effects on the residual of mdd from the model below after accounting for its loading on the INT factor. I ran the below model, munged the output, and then used LDSC to estimate the genetic correlations between INT and residual mdd. I thought the residual mdd should be basically uncorrelated with the factor, but the correlation is .67. Does this sytax look correct? Thank you!

model <- "INT =~ 1*mdd + anx + neuro + ptsd
          INT ~~ NA*INT
         
          INT ~ SNP
          mdd ~ SNP"

results <- userGWAS(LDSCoutput,
                    sumstats,
                    estimation = "DWLS",
                    model = model,
                    printwarn = TRUE,
                    sub=c("mdd~SNP"),
                    cores=1,
                    toler = 1e-50)

[Michel Nivard]

Hi Holly,

Its grat that you checked and did not simply assume the rg was 0. The generic correlation between the factor and MDD is proportional to the standardized loading. The residuals are uncorrelated to the factor by definition. If you could (you can't it's not identified) estimate the SNP effects on all the residual/indicators that might have gotten some kind of orthogonal effects. Currently the average SNP effect on ANX NEURO, PTSD are captured by  INT ~ SNP, with specific deviations from those captured by mdd ~ SNP (such that the two always combien to perfectly recapitulate MDD ~ SNP). It is absolutely possible for mdd ~ SNP to be higher when  INT ~ SNP is high and low when  INT ~ SNP is low. 

This is a long winded way of me saying that I don't know for sure that it should be 0 in.a model with some, but not all, SNP effects allowed. That being said, you chose to fix the loading form INT to MDD to 1, which basically means the SNP effects on int are scaled using MDD, this might in this case be working to your disadvantage in some way, as your looking for the residual for MDD? Not sure but I'd certainly try:

 model <- "INT =~ mdd + anx + neuro + ptsd

          INT ~~ 1*INT

          
          INT ~ SNP
          mdd ~ SNP"

results <- userGWAS(LDSCoutput,
                    sumstats,
                    estimation = "DWLS",
                    model = model,
                    printwarn = TRUE,
                    sub=c("mdd~SNP"),
                    cores=1,
                    toler = 1e-50)

Not sure that'll matter but it might? I assume your eventual goal is to establish some  rg's with other traits? or do some other downstream analysis? you could see which of those you could run in GenomicSEM, because the act of doing a GWAS in GenomicSEM could recapitulate error/bias that does not exist in the SEM model itself. YOu can intuitively compare this to doing PRS analysis, the PRS analysis usually does not recapitulate the whole  of h2_SNP because while the totl effects of all SNPs equals h2_SNP, the sum of their individual effects encodes errors, and biases.  We shared his worry in the GWAS by subtraction paper and we ran all the rg's outside the sem model with LDSC and inside the sem model, and compared. I think we also tried to see if the rg with a holdout Cog GWAS was low/absent I believe. Should be somewhere in the supplements.

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[Holly Poore]

Thank you so much for your helpful response! I will try rerunning the model freely estimating the mdd loading and report back. 

I would like to do some PGS analyses with these results, so I do need to go outside the model. Do you think there is a better way to model these residuals (like a Cholesky or something like that?) or is the same problem likely to persist in other models?

Thanks again,

Holly

[Michel Nivard]

So we also did PRS in the GWAS by subtraction paper. But the first PRS analysis we ran was on the traits in the model, cognitive scores, and EA. We confirmed the PRs for Non-cog explained way less variance (not zero) in cognitive tests then the cog or EA scores, but did still predict EA. I imagine you’ll have to do something similar, find positive controls (things it should still predict) and negative controls (things it should predict less (at least, ideally near 0)).

Op vr 20 feb 2026 om 21:26 schreef Holly Poore <hollyb...@gmail.com>

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