Question: How to find Standard Error (SE) values when not provided in GWAS summary stats?
gravatar for ydadmehr
12 weeks 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 12 weeks ago by Sam2.9k • written 12 weeks ago by ydadmehr0
gravatar for Sam
12 weeks ago by
New York
Sam2.9k 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 12 weeks ago by Sam2.9k

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

ADD REPLYlink written 12 weeks 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 12 weeks ago by Sam2.9k
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