<p>In simple GWAS setups, each SNP is analyzed independently.  In those cases you can filter out SNPs with poor INFO scores at any point.</p>

<p>For analyses that combine information across SNPs (for example FaSTLMM), I would recommend filtering out SNPs before running the step that combines information across SNPs.</p>

<p>So, if you filter right after running Impute2, you should be safe.</p>

CC BY-SA 3.0
2018-02-23T18:58:17.970Z
Thanks so much, winni2k. What is the best way to filter the SNPs? Is that usually an option when inputing files to a software, or something that should be done separately (e.g., through something like python)?
hp2018
CC BY-SA 3.0
2018-03-01T07:57:03.373Z
Plink is wildly popular and very fast. But do use version 2 if you want to avoid surprise allele reordering.
winni2k
CC BY-SA 3.0
2018-02-16T21:43:29.387Z
2018-02-16T21:43:29.387Z
winni2k
<p>Depending on the size of your samples (1K, 10K, 100K?) or the size of imputation region (1 gene, 1 chromosome, whole genome?) imputation files even after zipping are usually huge.</p>

<p>Instead of filtering SNPs and creating yet more gazillions of subset files. I prefer to have <code>excludeSNP.txt</code> file list of SNPs. This list then used as input for further analysis using <em>SNPTEST</em> flag <code>-exclude_snps_g</code>. This list is part of <a href="https://mathgen.stats.ox.ac.uk/impute/output_file_options.html" rel="nofollow noreferrer">IMPUTE2 output</a> or could be additional list of SNPs that we wish to exclude for other reasons. In short, filter at the point of analysis not the imputated files.</p>

<p>If <em>info</em> file is missing we can run <em>SNPTEST</em> with <code>-summary_stats_only</code> flag, which gives you the info score.</p>

<p>Here is an old still relevant post at BioStars <a href="https://www.biostars.org/p/6476/" rel="nofollow noreferrer">post-imputation QC</a>.</p>

CC BY-SA 3.0
2018-02-25T21:32:18.797Z
2018-02-25T21:32:18.797Z
zx8754
<p>this is a very basic question, but I cannot find it explicitly stated anywhere: When exactly should I filter the info scores after imputation?</p>

<p>I did imputation in Impute2 and am planning on using SNPtest to analyze, but cannot tell if I should filter the scores in Impute2, SNPtest, Gtool, QCtool, or through python.</p>

<p>I'm hoping to get rid of the poorly imputed results (with an info score cutoff &lt;0.5) in a population of ~4000 (half cases, half controls). After this, I'm running an association analysis -- probably through PLINK, but I'm still figuring out the best way to go from Impute2 to PLINK.</p>

<p>Any help would be greatly appreciated!!</p>

<gwas><imputation>
How to filter info score post-imputation?
# How to filter info score post-imputation?

(Asked by: username_0 on 2018-02-16)

this is a very basic question, but I cannot find it explicitly stated anywhere: When exactly should I filter the info scores after imputation?

I did imputation in Impute2 and am planning on using SNPtest to analyze, but cannot tell if I should filter the scores in Impute2, SNPtest, Gtool, QCtool, or through python.

I'm hoping to get rid of the poorly imputed results (with an info score cutoff <0.5) in a population of ~4000 (half cases, half controls). After this, I'm running an association analysis -- probably through PLINK, but I'm still figuring out the best way to go from Impute2 to PLINK.

Any help would be greatly appreciated!!

**Tags:** gwas, imputation

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## 2 answers

### Answer by username_1 on 2018-02-16. Score: 2

In simple GWAS setups, each SNP is analyzed independently. In those cases you can filter out SNPs with poor INFO scores at any point.

For analyses that combine information across SNPs (for example FaSTLMM), I would recommend filtering out SNPs before running the step that combines information across SNPs.

So, if you filter right after running Impute2, you should be safe.

> **Comments:**
> **username_0**: Thanks so much, winni2k. What is the best way to filter the SNPs? Is that usually an option when inputing files to a software, or something that should be done separately (e.g., through something like python)?  
> **username_1**: Plink is wildly popular and very fast. But do use version 2 if you want to avoid surprise allele reordering.  

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### Answer by username_2 on 2018-02-25. Score: 1

Depending on the size of your samples (1K, 10K, 100K?) or the size of imputation region (1 gene, 1 chromosome, whole genome?) imputation files even after zipping are usually huge.

Instead of filtering SNPs and creating yet more gazillions of subset files. I prefer to have `excludeSNP.txt` file list of SNPs. This list then used as input for further analysis using *SNPTEST* flag `-exclude_snps_g`. This list is part of [IMPUTE2 output](https://mathgen.stats.ox.ac.uk/impute/output_file_options.html) or could be additional list of SNPs that we wish to exclude for other reasons. In short, filter at the point of analysis not the imputated files.

If *info* file is missing we can run *SNPTEST* with `-summary_stats_only` flag, which gives you the info score.

Here is an old still relevant post at BioStars [post-imputation QC](https://www.biostars.org/p/6476/).


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