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Minimac: 1000 Genomes Imputation Cookbook (view source)

Revision as of 18:17, 10 February 2011

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Within each file, markers should be stored by chromosome position. Alleles should be stored in the forward strand and can be encoded as 'A', 'C', 'G' or 'T' (there is no need to use numeric identifiers for each allele).  
 
Within each file, markers should be stored by chromosome position. Alleles should be stored in the forward strand and can be encoded as 'A', 'C', 'G' or 'T' (there is no need to use numeric identifiers for each allele).  
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The 1000 Genome pilot project genotypes use NCBI Build 36.
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The 1000 Genome pilot project genotypes use NCBI Build 37.
    
=== Reference Haplotypes ===
 
=== Reference Haplotypes ===
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Reference haplotypes generated by the 1000 Genomes project and formatted so that they are ready for analysis are available from the [http://www.sph.umich.edu/csg/abecasis/MaCH/download/1000G-2010-06.html MaCH download page]. The most recent set of haplotypes were generated in June 2010 by combining genotype calls generated at the Broad, Sanger and the University of Michigan. In our hands, this June 2010 release is substantially better than previous 1000 Genome Project genotype call sets.
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Reference haplotypes generated by the 1000 Genomes project and formatted so that they are ready for analysis are available from the [http://www.sph.umich.edu/csg/abecasis/MaCH/download/1000G-2010-08.html MaCH download page]. In our hands, this August 2010 release is substantially better than previous 1000 Genome Project genotype call sets.
    
== MaCH Imputation ==
 
== MaCH Imputation ==
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== minimac Imputation ==
 
== minimac Imputation ==
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=== Running MaCH ===
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A typical MaCH command line to estimate phased haplotypes might look like this:
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  mach1 -d sample.dat -p sample.ped --rounds 20 --states 200 --phase --interim 5 --sample 5 --compact
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This will request that MaCH estimate haplotypes for your sample, using 20 iterations of its Markov sampler and conditioning each update on up to 200 haplotypes. A summary description of these parameters follows (but for a more complete description, you should go to the MaCH website):
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{| class="wikitable" border="1" cellpadding="2"
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|- bgcolor="lightgray"
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! Parameter
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! Description
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|-
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|style=white-space:nowrap|<code>-d sample.dat</code>
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| Data file in [http://www.sph.umich.edu/csg/abecasis/Merlin/tour/input_files.html Merlin format]. Markers should be listed according to their order along the chromosome.
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|-
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| <code>-p sample.ped</code>
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| Pedigree file in [http://www.sph.umich.edu/csg/abecasis/Merlin/tour/input_files.html Merlin format]. Alleles should be labeled on the forward strand.
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|-
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| <code>--states 200</code>
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| Number of haplotypes to consider during each update. Increasing this value will typically lead to better haplotypes, but can dramatically increase computing time and memory use. A value of 100 - 400 is typical.
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|-
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| <code>--rounds 20</code>
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| Iterations of the Markov sampler to use for haplotyping. Typically, using 20 - 100 rounds should give good results. To obtain better results, it is usually better to increase the <code>--states</code> parameter.
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|-
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| <code>--interim 5</code>
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| Request that intermediate results should be saved to disk periodically. These will facilitate analyses in case a run doesn't complete.
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|-
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| <code>--sample 5</code>
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| Request that random (but plausible) sets of haplotypes for each individual should be drawn every 5 iterations. This parameter is optional, but for some rare variant analyses, these alternative haplotypes can be very useful.
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|-
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| <code>--phase</code>
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| Tell [[MaCH]] to estimate phased haplotypes for each individual.
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|-
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| <code>--compact</code>
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| Reduce memory use at the cost of approximately doubling runtime. This option is recommended for most GWAS scale datasets and computing platforms.
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|}
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== Imputation into Phased Haplotypes ==
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Imputing genotypes using '''minimac''' is an easy straightforward process: after selecting a set of reference haplotypes, plugging-in the target haplotypes from the previous step and setting the number of rounds to use for the model parameter estimation, imputation should proceed rapidly.
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=== Running Minimac ===
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A typical minimac command line might look like this:
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  minimac --refHaps ref.hap.gz --refSnps ref.snps.gz --haps target.hap.gz --snps target.snps.gz --rounds 5 --states 200 --prefix results
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A detailed description of all minimac options is available [[Minimac Command Reference|elsewhere]]. Here is a brief description of the above parameters:
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{| class="wikitable" border="1" cellpadding="2"
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|- bgcolor="lightgray"
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! Parameter
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! Description
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|-
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| <code>--refSnps ref.snps.gz </code>
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| List of SNPs in the reference panel
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|-
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| <code>--refHaps ref.hap.gz </code>
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| Reference haplotypes (e.g. from [http://www.sph.umich.edu/csg/abecasis/MACH/download/1000G-2010-06.html MaCH download page])
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|-
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| <code>--snps target.snps.gz </code>
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| SNPs in phased haplotypes. These should largely be a subset of the SNPs in the reference panel.
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|-
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| <code>--haps target.hap.gz </code>
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| Phased haplotypes where missing genotypes will be imputed.
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|-
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| <code>--rounds 5</code>
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| Rounds of optimization for model parameters, which describe population recombination rates and per SNP error rates.
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|-
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| <code>--states 200</code>
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| Maximum number of reference (or target) haplotypes to be examined during parameter optimization.
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|-
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| <code>--prefix imputed</code>
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| Optionally, a string that is used to help generate output file names.
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|}
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=== Imputation quality evaluation ===
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Minimac drops each of the genotyped SNPs in turn and then calculates 3 statistics:
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* looRSQ - this is the estimated rsq for that SNP (as if SNP weren't typed).
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* empR - this is the empirical correlation between true and imputed genotypes for the SNP. If this is negative, the SNP is probably flipped.
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* empRSQ - this is the actual R2 value, comparing imputed and true genotypes.
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These statistics can be found in the *.info file
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=== X Chromosome Imputation ===
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minimac supports the imputation of genotypes on the X chromosome (non-pseudo-autosomal part).
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# Split the X chromosome pedigree file by sex.
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## For females: follow the same protocol as for autosomes (phase with MaCH and impute with minimac).
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## For males
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### Remove the pseudo-autosomal part (for build hg18: chrX:1-2709520 and chrX:154584238-154913754 ; for build hg19 chrX:60001-2699520 and chrX:154931044-155260560)
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### Convert the pedigree file into a MaCH haplotype file (missing genotypes should be encoded as:  "0" or "." or "N" ) and impute using minimac as described above.
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::::  '''<Example of a male only pedigree file >'''
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:::: FAM1003  ID1234  0  0  M  A/0  A/0  C/0
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:::: FAM1004  ID5678  0  0  M  0/0  C/0  G/0
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::::  ...
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::::  '''<End of pedigree file>'''
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::::  '''<Example of the corresponding haplotype file>'''
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::::  FAM1003->ID1234 HAPLO1 AAC
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::::  FAM1003->ID1234 HAPLO2 AAC
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::::  FAM1004->ID5678 HAPLO1 0CG
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::::  FAM1004->ID5678 HAPLO2 0CG
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::::  ...
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::::  '''<End of the corresponding haplotype file>'''
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Cfuchsb
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