<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>

CC BY-SA 4.0
2021-12-05T20:25:13.467Z
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?
user438383
CC BY-SA 4.0
2021-12-06T08:41:15.390Z
@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?
Shelby Labuschagne
<gwas><qc><maf>
How to chose values for QC for a GWAS?
# 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**: @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?  

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## 0 answers

