<p>I gather that you want to use the background SNP frequency as a prior for input to your SNP calling algorithm?</p>

<p>I'm not sure of a canned algorithm for doing this, but a quick google shows up some promising links:</p>

<ul>
<li><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3593722/" rel="nofollow noreferrer">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3593722/</a></li>
<li><a href="https://software.broadinstitute.org/gatk/documentation/article.php?id=4723" rel="nofollow noreferrer">https://software.broadinstitute.org/gatk/documentation/article.php?id=4723</a></li>
<li><a href="https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-015-0489-0" rel="nofollow noreferrer">https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-015-0489-0</a></li>
</ul>

CC BY-SA 3.0
2017-06-22T04:08:02.713Z
Thanks for the response - to clarify, I need additional details on how to correctly assign my priors using these public resources to jointly analyse multiple samples (our patient exomes and the population controls). The first link touches on this, but I'm unsure how to actually implement this approach.

The third link would be useful, except our coverage is fine and instead we lack the sample size they have by several orders of magnitude (gulp)!
carsweshau
CC BY-SA 3.0
2017-06-25T21:55:05.140Z
Yeah, I don't know of any tools that do this using existing data. What you /can/ do is re-analyze your data in the context of the other data in a single multi-sample call. This seems like a hack TBH, but should work.
Dan
CC BY-SA 3.0
2017-06-21T09:26:47.787Z
2017-06-21T14:00:21.773Z
2017-06-21T14:00:21.773Z
Dan
<p>I'm currently attempting association analysis with an extremely small set of patient exomes (n=10), with no control or parental exomes available. Downloading the ExAC VCF of variant sites (<a href="http://exac.broadinstitute.org/downloads" rel="noreferrer">http://exac.broadinstitute.org/downloads</a>) or the 1000G integrated call sets (<a href="http://ftp.1000genomes.ebi.ac.uk/" rel="noreferrer">http://ftp.1000genomes.ebi.ac.uk/</a>) and combining this with our pooled patient VCFs has not been successful (I suspect the approach of attempting to merge such large VCFs generated from different pipelines is rather naive).</p>

<p>Looking at the primary literature, I have gathered it should be possible to use these resources to help increase statistical power for our analysis. My question is how do I take these large .vcfs with many samples and successfully merge them to our patient .vcfs, such that the combined VCF can be used downstream to run analysis packages? (PODKAT, PLINK, etc.)</p>

<ngs><public-databases><variation><exome>
What is a good pipeline for using public domain exomes as controls?
# What is a good pipeline for using public domain exomes as controls?

(Asked by: username_0 on 2017-06-21)

I'm currently attempting association analysis with an extremely small set of patient exomes (n=10), with no control or parental exomes available. Downloading the ExAC VCF of variant sites (<http://exac.broadinstitute.org/downloads>) or the 1000G integrated call sets (<http://ftp.1000genomes.ebi.ac.uk/>) and combining this with our pooled patient VCFs has not been successful (I suspect the approach of attempting to merge such large VCFs generated from different pipelines is rather naive).

Looking at the primary literature, I have gathered it should be possible to use these resources to help increase statistical power for our analysis. My question is how do I take these large .vcfs with many samples and successfully merge them to our patient .vcfs, such that the combined VCF can be used downstream to run analysis packages? (PODKAT, PLINK, etc.)

**Tags:** ngs, public-databases, variation, exome

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## 1 answers

### Answer by username_1 on 2017-06-21. Score: 1

I gather that you want to use the background SNP frequency as a prior for input to your SNP calling algorithm?

I'm not sure of a canned algorithm for doing this, but a quick google shows up some promising links:

* <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3593722/>
* <https://software.broadinstitute.org/gatk/documentation/article.php?id=4723>
* <https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-015-0489-0>

> **Comments:**
> **username_0**: Thanks for the response - to clarify, I need additional details on how to correctly assign my priors using these public resources to jointly analyse multiple samples (our patient exomes and the population controls). The first link touches on this, but I'm unsure how to actually implement this approach.  The third link would be useful, except our coverage is fine and instead we lack the sample size they have by several orders of magnitude (gulp)!  
> **username_1**: Yeah, I don't know of any tools that do this using existing data. What you /can/ do is re-analyze your data in the context of the other data in a single multi-sample call. This seems like a hack TBH, but should work.  

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