text: user35: 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.
  
  user73: Yes. The general term for identifying subsets from a mixed group is 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 about the application of deconvolution in forensics.
  Here's a paper about a program called CIBERSORT which carries out a deconvolution of immune populations based on gene counts:
  Here's another paper 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
  Differential gene expession analysis in cell populations of mixed tumor and normal cells
  
  
original_text: 35: <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>
   73: <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>
  