Hello!

I'm using GenABEL to perform GWAS. Since some traits of analysis are not in normal distribution, I want to use the permutation method. I used the formula "qtscore" and got the empirical P value. The question is for some traits which are in normal distribution there is no use permutation, and the results of P value are in form such as xxe-7, for the traits that used permutation, the empirical results are in form of such as 0.03. If I want to show the results in the same level, how can I get the true P value of each SNP and the value of threshold after permutation ?

I've read the R program of "qtscore", but not quite understood of it. I can't solve this problem and looking forward to your reply.

Thanks a lot,

Daisy

## [SOLVED] P value of GenABEL when using permutation

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### [SOLVED] P value of GenABEL when using permutation

Last edited by daisy8098 on Tue May 03, 2011 8:01 am, edited 1 time in total.

### Re: P value of GenABEL when using permutation

Hi Daisy,

I think there is some misunderstanding here -- 'permutation' p-values reported by GenABEL's qtscore, etc. using 'times' argument (or emp.qtscore, etc.) are experiment-wise empirical P-values, not "nominal empirical" P-values. In that, any 'permutation' P<0.05 is to be considered significant.

When you run it genome-wide, this is genome-wide P-values. If you run only a region, this would tell you region-wise significance.

So this is very much expected, that (say for normally distributed trait for simplicity) you do a GWAS with 300k, and if for a certain SNP you get nominal p value of say 1e-7, then

* after Bonferroni correction this corresponds to multiple testing adjusted P-value of 1e-7*300000=0.03

* after permutations procedure you get 0.02 or so

Hope this helps -- let us know!

I think there is some misunderstanding here -- 'permutation' p-values reported by GenABEL's qtscore, etc. using 'times' argument (or emp.qtscore, etc.) are experiment-wise empirical P-values, not "nominal empirical" P-values. In that, any 'permutation' P<0.05 is to be considered significant.

When you run it genome-wide, this is genome-wide P-values. If you run only a region, this would tell you region-wise significance.

So this is very much expected, that (say for normally distributed trait for simplicity) you do a GWAS with 300k, and if for a certain SNP you get nominal p value of say 1e-7, then

* after Bonferroni correction this corresponds to multiple testing adjusted P-value of 1e-7*300000=0.03

* after permutations procedure you get 0.02 or so

Hope this helps -- let us know!

Note that (Gen)ABELs are dynamically developing; while this post is intended to provide full information at the time of posting, please read on further posts, if any, as the topic may be updated with novel solutions at a later stage.

best regards,

Yurii

best regards,

Yurii

### Re: P value of GenABEL when using permutation

I think perhaps you think such expression is much intuitive when we used permutation.

The question confused me is I'm analysing about 10 traits. First I did the Shapiro test to check if they were normal distribution and found about half of them were not. For the normal distribution traits I used qtscore with times=1 and the rest used permutation method. I want to graphics display the results. There is no problem with the first part of the traits, but for the second part of the traits, the P value of most SNPs is close to 1. For example, I got 3 SNPs with P<0.05, 28 SNPs with 0.05<P<0.98 and the rest P=1. When I draw a picture by -log10 transform, 19 chromosomes were straight line and the distance between the significance SNPs and the P=1 SNPs is very large. I means that I want to show the results of the 10 traits together, can I get the original P value and the threshold for the permutation traits or how can I solve such problem?

I used BOX-COX transformation for such traits, but I think permutation is a better method.

Thank you.

The question confused me is I'm analysing about 10 traits. First I did the Shapiro test to check if they were normal distribution and found about half of them were not. For the normal distribution traits I used qtscore with times=1 and the rest used permutation method. I want to graphics display the results. There is no problem with the first part of the traits, but for the second part of the traits, the P value of most SNPs is close to 1. For example, I got 3 SNPs with P<0.05, 28 SNPs with 0.05<P<0.98 and the rest P=1. When I draw a picture by -log10 transform, 19 chromosomes were straight line and the distance between the significance SNPs and the P=1 SNPs is very large. I means that I want to show the results of the 10 traits together, can I get the original P value and the threshold for the permutation traits or how can I solve such problem?

I used BOX-COX transformation for such traits, but I think permutation is a better method.

Thank you.

### Re: P value of GenABEL when using permutation

Well, this is my personal opinion, and it can be argued, but if I was aiming to a manuscript I would have either used permutation procedure for all the traits, or no permutations for all the traits. For me it sounds like a more straightforward procedure to explain.

And if you use permutations to estimate genome-wide significance, you would not normally show the graphs -- they are ugly

And if you use permutations to estimate genome-wide significance, you would not normally show the graphs -- they are ugly

Note that (Gen)ABELs are dynamically developing; while this post is intended to provide full information at the time of posting, please read on further posts, if any, as the topic may be updated with novel solutions at a later stage.

best regards,

Yurii

best regards,

Yurii

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