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Revision as of 09:32, 9 June 2010 by Ylwtx (talk | contribs) (How long does imputation take?)
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Why and how to perform a 2-step imputation?

When one has a large number of individuals (>1000), we recommend a 2-step imputation to speed up.

     A 2-step imputation contains the following 2 steps:

    (step 1) a representative subset of >= 200 unrelated individuals are used to calibrate model parameters; and
    (step 2) actual genotype imputation is performed for every person using parameters inferred in step 1.

      Example command lines for a 2-step imputation:

# step 1:
mach1 -d sample.dat -p subset.ped -s chr20.snps -h chr20.hap --compact --greedy --autoFlip -r 100 -o par_infer > mach.infer.log
# step 2:
mach1 -d sample.dat -p sample.ped -s chr20.snps -h chr20.hap --compact --greedy --autoFlip --errorMap par_infer.erate --crossoverMap par_infer.rec --mle --mldetails > mach.imp.log

Where can I find combined HapMap reference files?

You can find them at or on the HapMap Project website.

Where can I find HapMap III / 1000 Genomes reference files?

You can find these at the MaCH download page, which is at

Does --mle overwrite input genotypes?

Yes, but not often. The --mle option outputs the most likely genotype configuration taking into account observed genotypes and integration over the most similar reference haplotypes. The original genotypes will be changed only if the underlying reference haplotypes strongly contradict the input genotype.

How do I get imputation quality estimates?

A simple approach is to use --mask option. For example, --mask 0.02 masks 2% of the genotypes at random, impute them and compare with the masked original to estimate genotypic and allelic error rates. Messages like the following will be generated to stdout:

 Comparing 948352 masked genotypes with MLE estimates ...
 Estimated per genotype error rate is 0.0568
 Estimated per allele error rate is 0.0293 

A better approach is to mask a small proportion of SNPs (vs. genotypes in the above simple approach). One can generate a mask.dat from the original .dat file by simply changing the flag of a subset of markers from M to S2 without duplicating the .ped file. Post-imputation, one can use   CalcMatch and to estimate genotypic/allelic error rate and correlation respectively. Both programs can be downloaded from

Warning: Imputation involving masked datasets should be performed separately for imputation quality estimation. For production, one should use all available information.

Shall I apply QC before or after imputation? If so, how?

We strongly recommend QC both before and after imputation. Before imputation, we recommend the standard battery of QC filters including HWE, MAF (recommended cutoff is 1% for genotyping-based GWAS), completeness, Mendelian inconsistency etc. Post-imputation, we recommend Rsq 0.3 (which removes >70% of poorly-imputed SNPs at the cost of <0.5% well-imputed SNPs) and MAF of 1%.

How do I get reference files for an region of interest?

1. For HapMapII format, download haplotypes from 2. For MACH format, you can do the following:

  • First, find the first and last SNP in the region you are interested in. Say "rsFIRST" and "rsLAST", defined according to position.
  • Then:
 @ first = `grep -n rsFIRST orig.snps | cut -f1 -d ':'`
 @ last = `grep -n rsLAST orig.snps | cut -f1 -d ':'`
  • Finally (assuming the third field contains the actual haplotypes, where alleles are separated by whitespace):
 awk '{print $3}' orig.hap | cut -c${first}-${last} > region.hap

Do I always have to sort the pedigree file by marker position?

If you use a reference set of haplotypes, you do not have to as long as the external reference is in correct order. **HOWEVER**, you will probably avoid problems by including markers in the pedigree file sorted in chromosome order.

What if I specify --states R where R exceeds the maximum possible (2*number diploid individuals - 2 + number_haplotypes)?

Mach caps the number of states at the maximum possible value.

How is AL1 defined? Which allele dosage is .dose/.mldose counting?

AL1 is an arbitrary allele. Typically, it is the first allele read in the reference haplotypes (file fed to -h or --haps). The earliest versions (prior to April 2007) of mach counted the expected number copies of AL2 and more recent versions count the number of AL1. One can find out which allele is counted following the steps below.

  1. . First, find the two alleles for one of the markers in your data
 prompt> head -2 mlinfo/chr21.mlinfo 
 SNP      Al1 Al2 Freq1   MAF    Quality  Rsq 
 rs885550 2   4   0.9840  0.0160  0.9682  0.992
  1. . Second, check the dosage for a few individuals at this SNP.
 prompt> head -3 mldose/chr21.mldose | cut -f3 -d ' ' 
  1. . Finally, compare these dosages to genotypes.
 prompt> head -1 mlgeno/chr21.mlgeno | cut -f3 -d ' ' 

In this example, you can see that the first individual has a high dosage count (1.962) and most likely genotype 2/2. The last individual has a low dosage count and most likely genotype 4/4. Thus, the output corresponds to version of Mach released after April 2007, which should tally allele 1 counts.

Note that, on the example above, .mldose could be replaced with .dose and .mlgeno could be replaced with .geno.

Based on the three files above, we've confirmed that dosage is the number of AL1 copies: you will only to check for one informative case (i.e, dosage values close to 0 or 2) since it's consistent across all individuals and all SNPs.

Can I used an unphased reference?

Yes. You could create pedigree (.ped) and data files (.dat) that include both reference panel and sample genotypes or request that MaCH merge apppropriate files on the fly.

For example, if you have:




REF1 REF1 0 0 1 A/C C/C G/G G/A A/A




1 1 0 0 1 A/A G/G

Your could create a combined data set as:




REF1 REF1 0 0 1 A/C C/C G/G G/A A/A

  1    1 0 0 1 A/A ./. ./. G/G ./. 

Equivalently, you could write -d reference.dat,sample.dat -p reference.ped,sample.ped on the command line and MACH would merge both files on-the-fly.

How long does imputation take?

The following factors/parameters affect computational time:

  1. m, # of genotyped markers (number of markers in .dat file)
  2. n, # of individuals
  3. h, # of reference haplotypes (determined by --greedy or states, by default, h = 2*number diploid individuals - 2 + number_haplotypes)
  4. r, # of rounds (-r or --rounds, --mle corresponds to 1-2 rounds)

Computational time increases linearly with m, n, r and quadratically with h. On our Xeon 3.0GHz machine, imputation with m=25K, n=250, h=120, and r=100 takes ~20 hours.

If you have a larger number of individuals to impute (e.g., > 1,000), we recommend a 2-step imputation manner.

More questions?

Email Yun Li or Goncalo Abecasis.