Body: <p>I am running a GWAS on a large dataset. I am using the H3aBioNet QC workflow (<a href="https://github.com/h3abionet/h3agwas/blob/master/qc/README.md" rel="nofollow noreferrer">https://github.com/h3abionet/h3agwas/blob/master/qc/README.md</a>).</p>
  <p>I have run the QC on initial parameters:</p>
  <ol>
  <li>MAF &lt; 0.01</li>
  <li>GENO &gt; 0.05</li>
  <li>HWE 0.0000001</li>
  </ol>
  <p>My question is now that I have the output, do I need to change these parameters to a better estimate?</p>
  <p>I have the following graphs:
  Figure 1 SNP missingness: For each level of missingness specied on the x axis, the corresponding y-value shows the proportion of SNPs which have missingness less than this.</p>
  <p>Figure 2 Missingness per indvididual: For each level of missingness specied on the x axis, the corresponding y-value shows the proportion of individuals which have missingness less than this.</p>
  <p>Figure 3 Cumulative frequency of SNPs. For a frequency shown on the x-axis, the corresponding y-value shows the proportion of SNPs with frequency at least this frequency; that is, it shows the proportion of SNPs which will remain if the MAF lter of this x-value is chosen.</p>
  <p>Figure 4 HWE distribution. For an HWE-value shown on the x-axis, the corresponding y-value shows the proportion of SNPs with HWE p-value at least this frequency; that is, it shows the proportion of SNPs which will be removed if the HWE lter of this x-value is chosen.</p>
  <p>Figure 5 QQ plot for Hardy-Weinberg scores.</p>
  <p>Please let me know which one is the best to attach to the question.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-12-05T20:25:13.467Z
Comments.Text: How many SNPs do you have before and after applying those filters? If you’ve ended up removing most of them, then I’d suggest maybe rethinking what values you choose, otherwise I don’t think it necessary. Is there a reason why you thought you should?
Comments.UserDisplayName: user438383
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-12-06T08:41:15.390Z
Comments.Text: @user438383 I think its due to the graphs that were produced. I dont know how to interpret them and I cannot add them all to the question here. There are 295218 SNPs, 80370 SNPs were left over, too stringent?
Comments.UserDisplayName: Shelby Labuschagne
Tags: <gwas><qc><maf>
Title: How to chose values for QC for a GWAS?
ThreadText: # How to chose values for QC for a GWAS?
  
  (Asked by: username_0 on 2021-12-05)
  
  I am running a GWAS on a large dataset. I am using the H3aBioNet QC workflow (<https://github.com/h3abionet/h3agwas/blob/master/qc/README.md>).
  
  I have run the QC on initial parameters:
  
  1. MAF < 0.01
  2. GENO > 0.05
  3. HWE 0.0000001
  
  My question is now that I have the output, do I need to change these parameters to a better estimate?
  
  I have the following graphs:
  Figure 1 SNP missingness: For each level of missingness specied on the x axis, the corresponding y-value shows the proportion of SNPs which have missingness less than this.
  
  Figure 2 Missingness per indvididual: For each level of missingness specied on the x axis, the corresponding y-value shows the proportion of individuals which have missingness less than this.
  
  Figure 3 Cumulative frequency of SNPs. For a frequency shown on the x-axis, the corresponding y-value shows the proportion of SNPs with frequency at least this frequency; that is, it shows the proportion of SNPs which will remain if the MAF lter of this x-value is chosen.
  
  Figure 4 HWE distribution. For an HWE-value shown on the x-axis, the corresponding y-value shows the proportion of SNPs with HWE p-value at least this frequency; that is, it shows the proportion of SNPs which will be removed if the HWE lter of this x-value is chosen.
  
  Figure 5 QQ plot for Hardy-Weinberg scores.
  
  Please let me know which one is the best to attach to the question.
  
  **Tags:** gwas, qc, maf
  
  > **Comments:**
  > **username_1**: How many SNPs do you have before and after applying those filters? If you’ve ended up removing most of them, then I’d suggest maybe rethinking what values you choose, otherwise I don’t think it necessary. Is there a reason why you thought you should?  
  > **username_0**: @username_1 I think its due to the graphs that were produced. I dont know how to interpret them and I cannot add them all to the question here. There are 295218 SNPs, 80370 SNPs were left over, too stringent?  
  
  ---
  
  ## 0 answers
  
  