<p>Yes. The general term for identifying subsets from a mixed group is <a href="https://en.wikipedia.org/wiki/Deconvolution" rel="nofollow noreferrer" title="Wikipedia Deconvolution">deconvolution</a>.</p>

<p>This is easier to do when there is haplotype-level information available (e.g. sequenced reads that span multiple SNPs), but it is possible to get some indication of sample mixes from unlinked SNPs alone.</p>

<p>Deconvolution is helped a lot by having proportional data in the input (e.g. frequencies of genotypes, rather than a single genotype). Most SNPchip platforms have multiple probes per variant, and can provide some indication of variant proportion when digging down into the raw data. Deconvolution is also a lot easier with some knowledge about the component mixes. It's much easier to simulate mixes than to guess at possible mixes.</p>

<p><a href="https://investigativegenetics.biomedcentral.com/articles/10.1186/s13323-014-0018-3" rel="nofollow noreferrer">Here's a paper</a> about the application of deconvolution in forensics.</p>

<p><a href="https://www.nature.com/articles/nmeth.3337" rel="nofollow noreferrer">Here's a paper</a> about a program called CIBERSORT which carries out a deconvolution of immune populations based on gene counts:</p>

<p>Here's another <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3874291/" rel="nofollow noreferrer">paper</a> which discusses the idea in more general terms.</p>

<p>You might find more information on one of these Biostars posts:</p>

<ul>
<li><a href="https://www.biostars.org/p/160961/" rel="nofollow noreferrer">Deconvolution Methods on RNA-Seq Data</a></li>
<li><a href="https://www.biostars.org/p/121286/" rel="nofollow noreferrer">Differential gene expession analysis in cell populations of mixed tumor and normal cells</a></li>
</ul>

CC BY-SA 4.0
2018-09-14T19:29:53.057Z
I am familiar with the expression-based deconvolution, but that's a different problem. And you can have the same mix of cell populations regardless of how many individuals are mixed in a single library. Do you have any suggestions for SNP-based deconvolution?
burger
CC BY-SA 4.0
2018-09-14T23:12:35.637Z
Could you please clarify this in your question. It's not clear how much research you've done on this already, and whether or not you have haplotype-level information available.
gringer
CC BY-SA 4.0
2018-09-13T23:20:56.530Z
2018-09-14T07:06:31.890Z
2018-09-14T07:06:31.890Z
gringer
<p>Is there a way to identify mixed samples based on SNPs?</p>

<p>Example input (table of genotypes for multiple samples):</p>

<pre><code>|      | sample1 | sample2 | sample3 |
|------|---------|---------|---------|
| rs1  | AA      | BB      | AB      |
| rs2  | AA      | BB      | AB      |
| rs3  | AA      | BB      | AB      |
| rs4  | AA      | BB      | AB      |
</code></pre>

<p>Desired output: Sample relationships/distances. In this example, sample1 and sample2 are distinct, but sample3 is likely a mix of two (biological or technical).</p>

<p>Based on this simple example, the calculation seems doable, but maybe it's impossible in more complex realistic scenarios.</p>

<p>There is a nice tool <a href="https://github.com/brentp/peddy" rel="nofollow noreferrer">peddy</a> that does something conceptually similar, but it expects pedigree information as well as genotype frequencies for both the input samples and population.</p>

CC BY-SA 4.0
2018-09-15T01:48:14.683Z
I updated the question. I didn't initially realize it was not clear.
burger
CC BY-SA 4.0
2018-09-15T05:19:20.043Z
Thanks, but this still remains a question that is mostly without context. What is the specific problem that you are trying to solve?

As presented here, those samples could be individuals and the problem is a phasing problem, rather than mixed groups. What platform is being used to carry out the genotyping? How far away from each other are the variants?
gringer
<snp><genotyping>
using SNPs to identify mixed samples
# using SNPs to identify mixed samples

(Asked by: username_0 on 2018-09-13)

Is there a way to identify mixed samples based on SNPs?
Example input (table of genotypes for multiple samples):
|      | sample1 | sample2 | sample3 |
|------|---------|---------|---------|
| rs1  | AA      | BB      | AB      |
| rs2  | AA      | BB      | AB      |
| rs3  | AA      | BB      | AB      |
| rs4  | AA      | BB      | AB      |

Desired output: Sample relationships/distances. In this example, sample1 and sample2 are distinct, but sample3 is likely a mix of two (biological or technical).
Based on this simple example, the calculation seems doable, but maybe it's impossible in more complex realistic scenarios.
There is a nice tool peddy that does something conceptually similar, but it expects pedigree information as well as genotype frequencies for both the input samples and population.

**Tags:** snp, genotyping
> **Comments:**
> **username_0**: I updated the question. I didn't initially realize it was not clear.  
> **username_1**: Thanks, but this still remains a question that is mostly without context. What is the specific problem that you are trying to solve?  As presented here, those samples could be individuals and the problem is a phasing problem, rather than mixed groups. What platform is being used to carry out the genotyping? How far away from each other are the variants?  

---

## 1 answers

### Answer by username_1 on 2018-09-13. Score: 2

Yes. The general term for identifying subsets from a mixed group is [deconvolution](https://en.wikipedia.org/wiki/Deconvolution "Wikipedia Deconvolution").

This is easier to do when there is haplotype-level information available (e.g. sequenced reads that span multiple SNPs), but it is possible to get some indication of sample mixes from unlinked SNPs alone.

Deconvolution is helped a lot by having proportional data in the input (e.g. frequencies of genotypes, rather than a single genotype). Most SNPchip platforms have multiple probes per variant, and can provide some indication of variant proportion when digging down into the raw data. Deconvolution is also a lot easier with some knowledge about the component mixes. It's much easier to simulate mixes than to guess at possible mixes.

[Here's a paper](https://investigativegenetics.biomedcentral.com/articles/10.1186/s13323-014-0018-3) about the application of deconvolution in forensics.

[Here's a paper](https://www.nature.com/articles/nmeth.3337) about a program called CIBERSORT which carries out a deconvolution of immune populations based on gene counts:

Here's another [paper](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3874291/) which discusses the idea in more general terms.

You might find more information on one of these Biostars posts:

* [Deconvolution Methods on RNA-Seq Data](https://www.biostars.org/p/160961/)
* [Differential gene expession analysis in cell populations of mixed tumor and normal cells](https://www.biostars.org/p/121286/)

> **Comments:**
> **username_0**: I am familiar with the expression-based deconvolution, but that's a different problem. And you can have the same mix of cell populations regardless of how many individuals are mixed in a single library. Do you have any suggestions for SNP-based deconvolution?  
> **username_1**: Could you please clarify this in your question. It's not clear how much research you've done on this already, and whether or not you have haplotype-level information available.  

---

