text: user8875: I have about 1,000 SNP-Chip data (samples) that I'd like to impute over (for the purpose of having more rsIDs to match against GWAS data).
  However, I don't know the ancestry of each sample / the ancestry hasn't been recorded in a reliable way.
  Is there a 'quick and dirty' method to decide which imputation panel to use based on the genotype data itself? e.g.
  
  sample 1 'looks' British in England and Scotland (GBR),
  sample 2 'looks' Colombian in Medellin, Colombia (CLM),
  sample 3 'looks' Bengali in Bangladesh (BEB), etc.
  
  Once I know which group each sample matches most closely I can then perform imputation with the appropriate imputation panel.
  
  user3694: If I were you, I would use ADMIXTURE in supervised mode --supervised and use some of the 1000 genomes populations as reference populations. This is a fast and accurate way to obtain broad-scale ancestry proportion estimates for each of your individuals.
  
  user492: If you're using an hmm-based method such as for example impute2 or later, then the imputation method will perform the haplotype matching for you. No need to cut down the reference panel. Try to get a big reference panel such as 1000 genomes phase 3 or the haplotype reference consortium (disclaimer: I am an author on both those papers).
  But do be careful about relative power: a sample with haplotypes that are better represented in the ref panel than another sample will have better-imputed genotypes than the other sample.
  
original_text: 8875: <p>I have about 1,000 SNP-Chip data (samples) that I'd like to impute over (for the purpose of having more rsIDs to match against GWAS data).</p>
  <p>However, I don't know the ancestry of each sample / the ancestry hasn't been recorded in a reliable way.</p>
  <p>Is there a 'quick and dirty' method to decide which imputation panel to use based on the genotype data itself? e.g.</p>
  <ul>
  <li>sample 1 'looks' British in England and Scotland (GBR),</li>
  <li>sample 2 'looks' Colombian in Medellin, Colombia (CLM),</li>
  <li>sample 3 'looks' Bengali in Bangladesh (BEB), etc.</li>
  </ul>
  <p>Once I know which group each sample matches most closely I can then perform imputation with the appropriate imputation panel.</p>
   3694: <p>If I were you, I would use <a href="https://vcru.wisc.edu/simonlab/bioinformatics/programs/admixture/admixture-manual.pdf" rel="nofollow noreferrer">ADMIXTURE</a> in supervised mode <code>--supervised</code> and use some of the 1000 genomes populations as reference populations. This is a fast and accurate way to obtain broad-scale ancestry proportion estimates for each of your individuals.</p>
   492: <p>If you're using an hmm-based method such as for example impute2 or later, then the imputation method will perform the haplotype matching for you. No need to cut down the reference panel. Try to get a big reference panel such as 1000 genomes phase 3 or the haplotype reference consortium (disclaimer: I am an author on both those papers).</p>
  <p>But do be careful about relative power: a sample with haplotypes that are better represented in the ref panel than another sample will have better-imputed genotypes than the other sample.</p>
  