Hi,

I have conducted a search for statistical epistasis using a simple dosage model:

```
Y ~ A + B + AB
```

where Y is the phenotype, in this case, gene expression values and A and B are vectors of genotype information for ~500 samples. I wish to determine a signficance threshold using permutation testing in order to correct for multiple testing.

To date, I have recalculated the p-values for the interaction term (AB) for 100 permutations (I permuted the phenotype values) and am unsure how to proceed in order to derive a false discovery rate (FDR).

Any suggestions?

Thanks, D.

Couldn't you just use the fdr method of Benjamini&Hochberg, 1995 in R: p.adjust(p, method="fdr")? I think that should also be valid for permutation p-values. Concerns anyone?

@Michael: For an additive genetic model with genotypes AA AB BB, one assumes that each B or A allele has an incremental effect on the phenotype, such that AA[?]AB>BB. This is intuitively similar to treating each the A or B allele as a drug with increasing dosage. In this case the genotypes are ordinal, not categorical. For a test of epistasis, you are looking for deviation from an additive model and trying to fit an interaction term; I think it's standard to check only the interaction term.

Some things I don't quite understand: 1. how did you compute your p-values? genotypes are categorial data, how does a dosage model apply then? Why did you compute p-values only on interaction term? Why so few permutations? Given 500! possible permutations of 500 samples, I would have expected more to get a reliable estimate.