Answers.Body: <p>There is a risk of a type 2 error.</p>
  <p>My advice would be to use <code>Structure</code> if it is still available given the situation. Its not great and it is old and its a GUI and I don't know whether it has been updated (I'm not exactly selling this am I?), but it will do the job of navigating through a type 2 error.</p>
  <p>What you do is provide the number of populations <em>a priori</em> and it will assign a Bayesian estimate of HW equilibrium for that number of populations. You manually input 1,2,3,4,.... 30 run and wait. The point at which the joint marginal plateaus is the minimum number of populations which do not exhibit LD (thus if two of your populations exhibit LD it collapse them into one). Thus if your answer is '20' it give some confidence it is a real result. If it is like 10 (optimistically) the conclusion would be in pretty reasonable shape.</p>
  <p>There are other ways of looking at this, but that would be my starting point.</p>
  <p>The classic calculation is LDHat, here <a href="https://github.com/auton1/LDhat" rel="nofollow noreferrer">https://github.com/auton1/LDhat</a>, but the last update was 2015 and in this particular circumstance I'm not sure.</p>
  
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Answers.CreationDate: 2022-04-27T15:49:06.270Z
Answers.Text: I don't understand.
Answers.UserDisplayName: Dan Bolser
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Answers.Text: Sorry if this has not been helpful. I would therefore suggest a data pipeline around what appears the current standard which is plink https://www.cog-genomics.org/plink/1.9/ld . To understand LD stats a background in population genetics and stats is really helpful. The answer above is as good as I could do without writing a data pipeline.
Answers.UserDisplayName: M__
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Answers.Text: We can chat about it, but the solution I proposed was trying to over the type 2 error in the data and I'd need to explain that in depth because its fundamental to how LD works. I'm not up to date with plink, I left LD prior to plink, but I do understand how the numbers work in LD.
Answers.UserDisplayName: M__
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Answers.CreationDate: 2022-04-28T09:48:58.310Z
Answers.Text: How would you like to chat?
Answers.UserDisplayName: Dan Bolser
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Answers.Text: I forget how you do invites and stuff, but I think you have to visit the bioinformatics chat and that activates an invite.
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Body: <p>I have 3 SNPs that are in LD in 'several' populations here:
  <a href="https://www.ebi.ac.uk/gwas/variants/rs13078854#ld_panel_label" rel="nofollow noreferrer">https://www.ebi.ac.uk/gwas/variants/rs13078854#ld_panel_label</a></p>
  <p>It's a pain to look at all (30) populations one by one to manually check if the 3 SNPs are in LD in a given population or not.</p>
  <p>Is there a way to ask this question more systematically? e.g. in which common imputation populations are a given set of SNPs in LD (above a certain threshold)?</p>
  
Tags: <snp><imputation><ld>
Title: SNPs in LD in which populations?
ThreadText: # SNPs in LD in which populations?
  
  (Asked by: username_0 on 2022-02-28)
  
  I have 3 SNPs that are in LD in 'several' populations here:
  <https://www.ebi.ac.uk/gwas/variants/rs13078854#ld_panel_label>
  
  It's a pain to look at all (30) populations one by one to manually check if the 3 SNPs are in LD in a given population or not.
  
  Is there a way to ask this question more systematically? e.g. in which common imputation populations are a given set of SNPs in LD (above a certain threshold)?
  
  **Tags:** snp, imputation, ld
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2022-02-28. Score: 2
  
  There is a risk of a type 2 error.
  
  My advice would be to use `Structure` if it is still available given the situation. Its not great and it is old and its a GUI and I don't know whether it has been updated (I'm not exactly selling this am I?), but it will do the job of navigating through a type 2 error.
  
  What you do is provide the number of populations *a priori* and it will assign a Bayesian estimate of HW equilibrium for that number of populations. You manually input 1,2,3,4,.... 30 run and wait. The point at which the joint marginal plateaus is the minimum number of populations which do not exhibit LD (thus if two of your populations exhibit LD it collapse them into one). Thus if your answer is '20' it give some confidence it is a real result. If it is like 10 (optimistically) the conclusion would be in pretty reasonable shape.
  
  There are other ways of looking at this, but that would be my starting point.
  
  The classic calculation is LDHat, here <https://github.com/auton1/LDhat>, but the last update was 2015 and in this particular circumstance I'm not sure.
  
  > **Comments:**
  > **username_0**: I don't understand.  
  > **username_1**: Sorry if this has not been helpful. I would therefore suggest a data pipeline around what appears the current standard which is plink https://www.cog-genomics.org/plink/1.9/ld . To understand LD stats a background in population genetics and stats is really helpful. The answer above is as good as I could do without writing a data pipeline.  
  > **username_1**: We can chat about it, but the solution I proposed was trying to over the type 2 error in the data and I'd need to explain that in depth because its fundamental to how LD works. I'm not up to date with plink, I left LD prior to plink, but I do understand how the numbers work in LD.  
  > **username_0**: How would you like to chat?  
  > **username_1**: I forget how you do invites and stuff, but I think you have to visit the bioinformatics chat and that activates an invite.  
  
  ---
  
  