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Imputing a set of microarray data

Ok, so we've called genetic variants, we've QCd them, and we've phased them into haplotypes. What can we do with that?

One of the important ways that genetic variation reference panels is used is to impute genotypes in other sample sets that (for reasons of cost or practicality) haven't been sequenced. This is the basic paradigm, for example, for most analyses of the genetic resource in the UK Biobank.

Imputation will be covered in more detail in another session, but the basic idea is that - even if we haven't genotyped a particular variant in our sample set - we have genotyped variants on the same haplotype. So by statistically matching haplotypes between our genotyped set and the reference panel we've created using sequencing we might be able to infer their genotypes, even at untyped markers. (This process relies on the interesting structure that human haplotypes have, in which variants along the genome become correlated due to genetic drift, but these patterns are broken down by recombination.)

Imputation from very large reference panels can now be done easily online using for example the Michigan Imputation Server or the Sanger imputation service. But here we want to demonstrate how it's done.

To this end we've placed a dataset from microarray-based genotyping of a second set of Gambian samples in the file: GGVP/omni2.5M/GGVP-illumina_omni2.5M.phased.vcf.gz. (They come from the Gambian Genome Variation Project which is why we've called them GGVP.)

Note. We have already QCd and phased these genotypes for you - in a real analysis you might have to do that yourself.

Let's see if we can impute the secretor status SNP. You can check that it is not genotype in the input data:

bcftools view -H -i 'POS=48703417' GGVP/omni2.5M/GGVP-illumina_omni2.5M.phased.vcf.gz | wc -l

Note. recall that wc -l counts the number of lines in its output.

To use imputation we will use the program MINIMAC. The first step is to convert our reference panel:

Minimac3 \
  --refHaps GWD_30x_calls.phased.vcf.gz \
  --processReference \
  --prefix GWD_30x_calls.phased \
  --chr "chr19"

This should output a transformed file GWD_30x_calls.phased.m3vcf.gz.

Now use it to impute (note we'll use minimac version 4 for this):

minimac4 \
  --refHaps "GWD_30x_calls.phased.m3vcf.gz" \
  --mapFile genetic_map/genetic_map_hg38_withX.txt.gz \
  --haps GGVP/omni2.5M/GGVP-illumina_omni2.5M.phased.vcf.gz \
  --ignoreDuplicates \
  --format GT \
  --prefix GGVP-illumina_omni2.5M.imputed

What did imputation do?​

What's happened there? Well let's look at how many variants were in the files.

#Number of variants in the microarray data:
bcftools view -H GGVP/omni2.5M/GGVP-illumina_omni2.5M.phased.vcf.gz | wc -l

# Number of variants in the reference panel
bcftools view -H GWD_30x_calls.phased.vcf.gz | wc -l

# Number of variants in the imputed data
bcftools view -H GGVP-illumina_omni2.5M.imputed.dose.vcf.gz | wc -l

Question. What do these numbers mean?

Inspecting the secretor status​

Let's have a look at the secretor status SNP in the output:

bcftools view -H -i 'POS=48703417' GGVP-illumina_omni2.5M.imputed.dose.vcf.gz

You should see something like this:

chr19   48703417    chr19:48703417:G:A  G   A   .   PASS    AF=0.53395;MAF=0.46605;R2=0.99822;IMPUTED   GT  0|0 1|0 1|0 1|1 0|1 1|0 0|0 1|1 0|0 0|1 0|1 1|0 0|1 0|1 0|0 1|1 1|1 1|0 0|1 1|1 0|0 0|0 1|0 1|1 0|0 0|1 0|0 1|1 0|0 1|0 0|0 1|1 0|1 0|1 0|0 1|1 1|0 1|1 1|1 0|0 1|0 0|0 0|1 1|0 1|0 1|1 0|0 0|0 1|0 0|1 0|0 0|0 0|1 0|0 0|1 0|1 1|0 1|1 0|1 1|1 1|1 1|0 1|1 0|0 0|1 1|0 0|0 1|0 1|1 1|0 0|0 1|0 0|1 0|1 1|1 1|0 1|1 0|0 0|1 0|0 0|1 0|1 1|0 1|1 0|0 1|0 0|1 0|1 1|0 1|1 1|0 0|0 1|0 1|0 0|0 1|1 1|1 1|0 1|0 1|0 0|1 1|1 1|1 1|0 0|0 0|1 0|1 0|0 1|1 0|0 1|1 1|1 0|0 1|0 1|0 0|1 1|0 1|1 1|1 0|1 1|0 0|1 1|1 1|1 1|1 1|1 1|0 0|0 1|0 0|1 1|0 1|0 1|0 0|1 1|1 0|1 1|1 0|1 0|1 0|0 0|1 1|0 0|0 1|0 1|1 0|0 1|1 0|0 0|1 1|1 0|1 0|1 0|1 1|1 0|1 0|0 0|1 1|0 0|1 0|0 1|1 1|0 0|0 1|0 1|0 1|0 1|1 1|1 1|0 1|1 1|0 1|1 1|1 1|0 1|1 1|0 1|0 1|0 1|1 1|1 0|0 0|1 0|1 0|1 1|1 0|1 1|1 1|1 0|1 0|0 0|0 0|0 1|1 0|1 0|0 1|1 1|1 1|0 1|0 1|1 0|1 0|1 1|1 1|0 1|0 1|1 1|1 1|0 0|0 0|0 1|0 1|0 0|1 1|1 0|1 0|1 1|1 1|0 1|1 0|0 1|1 0|0 1|1 1|1 1|0 0|0 1|1 1|0 1|1 0|0 1|1 0|1 0|1 1|0 1|0 0|0 0|1 0|0 1|0 0|0 0|0 1|1 0|1 1|1 0|1 0|1 1|0 0|1 0|1 1|0 0|1 1|0 0|0 0|1 0|0 1|1 1|0 1|0 1|1 0|0 1|1 0|1 1|0 0|0 1|1 0|1 1|0 1|0 1|0 0|1 0|0 0|1 0|1 1|1 1|1 0|1 0|0 1|0 1|1 1|0 1|1 1|0 1|0 1|0 0|0 1|0 1|1 1|1 1|0 0|0 1|1 0|0 1|0 1|1 1|0 1|1 1|0 0|1 0|0 1|0 0|1 0|1 0|1 0|0 1|1 1|1 1|1 1|0 0|0 1|1 1|1 1|0 1|0 1|0 0|1 1|1 1|1 1|0 0|0 1|1 1|0 0|1 0|1 0|1 1|1 1|0 1|0 1|0 0|1 0|1 1|0 0|1 1|0 0|1 1|0 1|1 1|0 1|0 0|1 1|1 1|0 0|1 1|0 0|1 1|1 1|0 1|1 0|0 0|1 1|1 0|0 0|0 1|1 1|0 0|0 0|1 0|1 0|0 1|0 1|1 0|0 0|1 0|0 0|1 1|0 0|1 1|0 0|1 1|0 0|0 1|0 0|0 1|1 0|1 1|1 0|1 1|0 0|0 1|1 0|1 1|1 0|0 1|1 1|0 1|1 0|0 1|0 1|0 1|0 0|1 1|0 1|1 1|0 0|0 1|1 1|0 0|1 0|0 0|1 1|1 0|1 0|0 1|1 0|0 1|0 1|1 0|1

It worked! Imputation has generated best-guess genotypes for rs601338 (at chr19:48703417) for us.

Next steps​

You have successfully used a set of sequence data to identify genetic variants, quality control and phase them. And you have used that to impute the important secretor status (and other variants) into another dataset. Congratulations!

To finish the practical, go back and try the challenge questions.