P-Value calculation from iHS and XP-EHH scores

data<-read.table("CHR.xpehh-norm.reformat1.txt",header=FALSE,sep=" ") p<-vector() for (i in 1:dim(data)[1]) {if (data[i,4]>0) p[i]<-pnorm(data[i,4],lower.tail=FALSE) else p[i]<-pnorm(data[i,4],lower.tail=TRUE) } write.table(p,file="xpehh.p.chr.txt")

2.2 years ago by

GabrielMontenegro • 520

United Kingdom

GabrielMontenegro • 520 wrote:

On the paper introducing iHS they recommended using that threshold, but that still won't give you a P-value. If you check the paper you will see that they decided to compute empirical P-values using an outlier approach. To do that you simply sort all the scores genome-wide and then divide the rank by the total number of values in your distribution.

For iHS you can use the absolute standardised iHS scores. For XP-EHH -because this test is directional-, you should only use positive values.

Then you have another option which is to compute approximate P-values by simulating the distribution of your selection statistics under a neutral demographic model. In this case you should have a fairly good understanding on the history of your population, to be able to accurately reproduce its demographic history.

Hope this helps!

ADD COMMENT • link modified 2.2 years ago • written 2.2 years ago by GabrielMontenegro • 520

Hi @JM88,

Thanks for your response, However, here in the comments you mentioned:

"For iHS you can use the absolute standardised iHS scores. For XP-EHH -because this test is directional-, you should only use positive values." "Another option which is to compute approximate P-values by simulating the distribution of your selection statistics under a neutral demographic model"

Questions: 1. Can you please explain Why we need to use only positive values for XP-EHH? 2. Can you please suggest any previously accepted software/methods by which i can accurately reproduce the demographic history and intergrate the results with selection model genome-wide?

Thanks!

ADD REPLY • link written 2.2 years ago by SOHAIL • 260

Check the paper where Sabeti et al. introcuded the XP-EHH statistic. If I remember correctly you will find a detailed explanation of how XP-EHH is computed. Basically it is a ratio of the iHH of popA and popB, therefore directional. Also, if I remember correctly (again) the selscan manual has also a summary of all the statistics included in their software - including XP-EHH.

Probably the simulator ms (Hudson, 2002) would be a good way to start? Also check papers where they used this method instead of the outlier approach. I think the nSL paper used a simulation approach.

ADD REPLY • link written 2.2 years ago by GabrielMontenegro • 520

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