Question: How to find Standard Error (SE) values when not provided in GWAS summary stats?
gravatar for ydadmehr
7 months ago by
ydadmehr0 wrote:

Hi everyone!

I'm trying to do a fixed effect meta-analysis on a couple of GWASes based on p-values, Standard error and effect estimates (Beta) using METAL genetics software. For one of my GWAS studies SE is not reported explicitly in the summary stats, I have Q-test value and I^2 value for heterogeneity in addition to Beta and p value. I was wondering how I could find SE from Q-value. I do not know the sample size or the degrees of freedom.

Another on of my studies mentions T-score in addition to number of samples, p-values and beta. How can I estimate SE based on Tscore? I read somewhere that SE can be estimated using T-scores and beta by the following formula: SE=abs(beta/sqrt(t-score)) But wasn't able to confirm the validity of such formula since T-score can also be negative and can't take square root of that. How could one apply that formula?

Any advice would help!


ADD COMMENTlink modified 7 months ago by Sam3.3k • written 7 months ago by ydadmehr0
gravatar for Sam
7 months ago by
New York
Sam3.3k wrote:

If you have beta, then I think you can backward engineer the SE as Z-score = Beta / SE and there is a 1-1 mapping from Z-score to p-value.

In R, it'd be something like

z <- qnorm(p)
se <- abs(beta/z)

(might need to take care of lower.tail parameter in qnorm)

ADD COMMENTlink written 7 months ago by Sam3.3k

Thank you very much! Could you please explain what you mean by taking care of the lower.tail ?

ADD REPLYlink written 7 months ago by ydadmehr0

qnorm return a z-score, depending on the tail, the z-score will either be positive or negative. But in this case, I don't think that should be too much of a problem (because SE is not signed)

ADD REPLYlink written 7 months ago by Sam3.3k
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