'''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:
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. The parallel version of minimac will achieve an even better performance.
== Release Date ==
A public release of <code>minimac</code> is expected here by October 5, 2010.
== Getting Started ==
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 [http://
www.sph.umich.edu /csg/abecasis/Merlin/tour/input_files.html 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).
Genome pilot project genotypes use NCBI Build 36 .
=== 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.
== Usage ==== mach1 - d sample.dat - p sample. ped --rounds 20 --states 200 --phase --interim 1 --sample 1 --compact
==== Parameters ==== - p sample.pedpedigree file in [http: //www.sph.umich. edu/csg/abecasis/Merlin/tour/input_files. html Merlin Tutorial] format
dat file in [http://www.sph.umich.edu/csg/abecasis/Merlin/tour/input_files.html Merlin Tutorial] format
how many iterations of the Markov sampler should be run.
--states ST use a random subset of ST haplotypes as reference. We recommend values between 200 - 500. More states result in more accurate haplotypes, but are computational more expensive and require more memory.
--interim I output a set of best-guess haplotypes every I rounds by building consensus from all previous Markov iterations. These intermediate haplotypes can be used for imputation.
sample SA output a set of haplotypes every SA rounds based on random sampling from the last Markov iteration. These intermediate results can be combined and used as input for the imputation process.
- -phase enables [[ MaCH]] phasing mode.
--compact reduces the amount of memory needed dramatically, but doubles execution time.
== Step 2: Imputation ===
==== Reference Haplotypes ====
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.
==== Usage ==== minimac -- refHaps ref. hap. gz --refSnps ref. snps. gz --haps target. hap.gz --snps target.snps.gz --rounds 10
== Parameters ==== --refHaps ref.hap Reference haplotypes (e.g. [http://www.sph.umich.edu/csg/abecasis/MACH/download/1000G-2010-06.html MaCH download page]) --refSnps ref.snps Reference SNPs (e.g. [http://www.sph.umich.edu/csg/abecasis/MACH/download/1000G-2010-06.html MaCH download page]) --haps target.hap Target haplotypes (from the pre-phasing step) --snps target.snps Target snps (from the pre-phasing step) --rounds R how many iterations should be run to estimate the model parameters: (i) error rate for each marker (*.erate) and (ii) crossover rates (*.rec) for each interval. --rec file.rec use crossover rates from this file --erate file.erate use error rates from this file --prefix [minimac] prefix for output files, default minimac
== 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.