**100**wrote:

Hello,

I am using unconditional logistic regression to modelise genetic effect and genetic*environment exposure effect on my outcome.

My results a bit strange :

When modeling only main variants effect, I have no SNP associated

When modeling with interaction term exposure:SNP with additive term , I have a strong significant signal only for additive term ( towo SNP with p<<10e-8) and nothing for interaction.

I am using 0,1 and 2 codes for SNP (effect allele) and a continuous exposure variable.

I am working on case control study ( 2300 subjects) and testing 7000 SNPs

Can this be a reliable result ? How could this be explained ?

Thank you very much !

**600**• written 17 months ago by melania 2282 •

**100**

What, precisely, is your model formula? -

`outcome ~ exposure:SNP + SNP`

Working with regression models can be difficult (and 'risky') - basically, it is possible to find a statistically significant p-value by messing around with the model formula; however, the models may be meaningless. Without also looking at the standard errors, the beta coefficients, and odds ratios, one cannot really make any interpretation based solely on the p-value. Also, should you be adjusting for population stratification?

69kThank you Kevin, I am adjusting on PCA and this a result of metaanlysis of two different studies. my model is outcome ~ exposure:SNP + SNP+ exposure+ other cofactors than I did the metaanlysis from wich I get the significant result

100Ah, a model formula like this:

...is the same as:

, i.e., it is a multiplicative model, also sometimes called the 'log-additive model'. Perhaps this may assist in the interpretation? As an example, I conducted a similar study in 2016 (but it was conditional regression with

`Family ID`

as the matched strata) and I also used a multiplicative model. How are the standard errors?Lemire has provided an answer, below.

69k