Scantwo - normal model for binary data

[Conor Simpson]

Hi all,

I am having trouble with generating permutations using the binary model for scantwo. The model is not converging and does not seem to ever pass 1 permutation so trying to run 1000 seems impossible.

For all my binary traits, I get pretty much the same output using the binary or normal model - with exactly the same significant QTL being identified. Is this sufficient to justify using the normal model to generate scantwo permutations?

Thanks,

Conor

[Karl Broman]

I would only use permutation results that were derived with the same method I used for the analysis.

karl

[Conor Simpson]

Hi Karl,

I understand that but what I want to know is if it is OK to use the normal method for a binary trait since I cannot seem to generate scantwo permutations using binary method. The binary method for such traits yield the same results for scanone as it does when I use normal on these binary traits. So can I assume that it will be the same for scantwo and simply use the normal method even though they are binary traits?

Thanks,

Conor

[Karl Broman]

The binary model uses logistic regression, so there will be three main sources for differences in the results:

- forcing fitted values to be in the interval [0,1]

- handling differences in variance (if probability is near 0.5, the results will be more variable than if the probability is near 0 or 1)

- interactions will be on a different scale

karl

[Conor Simpson]

Thank you for this explanation!

Best,

Conor