I am running ACE and nested submodels on 100 twin pairs ( 50 MZ and 50 DZ twin pairs) to estimate A,C and E for 10,000 genes. Do I need multiple hypothesis correction on these estimates?
Thanks in advance.
Like Ryne said, it's tough to say without details.
The way to think about it is like this: On a standard significance test with alpha at .05, you've got a 5% chance of erroneously rejecting the null hypothesis. Every additional test to reject the same null carries its own 5% chance of a Type-I error.
If you are performing several tests, and allowing a significant result on any of them to be evidence for the same hypothesis, you need to do some sort of multiple testing correction.
That means if you're doing 10,000 tests to reject the null hypothesis that specific genes do not influence a behavior, you should probably be doing multiple testing correction.
It's also worth noting that multiple testing correction doesn't change your estimates. It only changes your significance levels.
I apologise for not making it clear.
I have expression value of 10,000 genes from 100 twin pairs. I am running ACE and nested sub models AE,CE and E for each gene seperately in other words running 10,000 chi-square tests for each model. Based on your reponses it looks like I need to adjust the pvals for multiple hypothesis correction.
I'd need a lot more information on which and how many submodels and how you're including 10,000 genes into your model, but I'd recommend some type of multiple testing correction.