Minimac

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Revision as of 03:34, 12 October 2010 by Goncalo (talk | contribs) (Parameters)
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minimac is a low memory, computationally efficient implementation of the MaCH algorithm for genotype imputation. It is designed to work on phased genotypes and can handle very large reference panels with hundreds or thousands of haplotypes. The name has two parts. The first, "mini", refers to the modest amount of computational resources it requires. The second, "mac", is short hand for MaCH, our widely used algorithm for genotype imputation.

Performance

A good rule of thumb is that minimac should take about 1 hour to impute 1,000,000 markers in 1,000 individuals using a reference panel with 100 haplotypes. Performance should scale linearly with respect to all these factors. So, your approximate computing time in hours should be about:


E(\mbox{Run Time in Hours}) = N_{markers} * N_{individuals} * N_{haplotypes} * 10^{-11}

These statistics refer to Intel X7460 CPU running at 2.66 GHz using 1 core and your mileage may vary; most modern CPUs should be no more than a few times faster (or slower) than that.

If you are estimating model parameters at the same time as imputing missing genotypes, you can account for the time needed for parameter estimation with the following formula:


E(\mbox{Run Time in Hours}) = N_{markers} * ({N_{individuals} + N_{rounds} * N_{states} * 0.75 }) * N_{haplotypes} * 10^{-11}

In this updated formula, Nrounds represents the number of iterations used for parameter refinement and Nstates represents the maximum number of reference and target haplotypes considered for each update.

Release Date

A public release of minimac is expected here soon. Beta-version available upon request (cfuchsb@umich.edu or goncalo@umich.edu)

Getting Started

Step 1: Pre-Phasing

For the pre-phasing step we recommend MaCH using the --phase command line option. As input MaCH needs a Merlin format pedigree and data file. All markers must be ordered according to their physical position.

Your Own Data

To get started, you will need to store your data in Merlin format pedigree and data files, one per chromosome. For details, of the Merlin file format, see the Merlin Tutorial.

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).

The 1000 Genome pilot project genotypes use NCBI Build 36.

Usage

 mach1 -d sample.dat -p sample.ped --rounds 20 --states 200 --phase --interim 1 --sample 1 --compact

Parameters

Parameter Description
-d sample.dat Data file in Merlin format. Markers should be listed according to their order along the chromosome.
-p sample.ped Pedigree file in Merlin format. Alleles should be labeled on the forward strand.
--states 200 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.
--rounds 50 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 --states parameter.
--interim 5 Request that intermediate results should be saved to disk periodically.
--phase Tell MaCH to estimate phased haplotypes for each individual.
--compact Reduce memory use at the cost of approximately doubling runtime. This option is recommended for most GWAS scale datasets and computing platforms.

Step 2: Imputation

Imputing genotypes using minimac is an easy straightforward process: after selecting a set of reference haplotypes (see below how to get the latest 1000 Genomes reference panel ready to go with minimac ), plugging-in the target haplotypes from the pre-phasing step and setting the number of rounds to use for the model parameter estimation, samples get imputed once a second.


Reference Haplotypes

Reference haplotypes generated by the 1000 Genomes project and formatted so that they are ready for analysis are available from the 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.

Usage

 minimac --refHaps ref.hap.gz --refSnps ref.snps.gz --haps target.hap.gz --snps target.snps.gz

Parameters

Parameter Description
--refSnps ref.snps List of SNPs in the reference panel
--refHaps ref.hap Reference haplotypes (e.g. from MaCH download page)
--snps target.snps SNPs in phased haplotypes. These should largely be a subset of the SNPs in the reference panel.
--haps target.hap Phased haplotypes where missing genotypes will be imputed.
--rounds 5 Rounds of optimization for model parameters, which describe population recombination rates and per SNP error rates.
--states 200 Maximum number of reference (or target) haplotypes to examined during parameter optimization.
--prefix imputed Optionally, a string that is used to help generate output file names.

Related Pages

If you are looking to learn about small computers made by Apple, Inc., you have come to the wrong page. Try looking at http://www.apple.com/macmini/, instead.

If you are looking for a low calorie version of the Big Mac sandwich, you'll be sad to know the Mini Mac has been discontinued. However, you are not the only one who likes the idea of a Mini Mac and you'll probably find some company on the web [1].