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'''You may want to learn about new and improved [[Minimac4]].'''
'''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.
There are several minimac related pages on this wiki. The major ones are:
* [[Minimac]] - This page, the main minimac page.
* [[Minimac: Tutorial]] - A short minimac tutorial.
* [[Minimac: 1000 Genomes Imputation Cookbook]] - Cookbook instructions for 1000 Genomes Imputation with Minimac
* [[Minimac Command Reference]] - Summary of minimac options
* [[Minimac Diagnostics]] - Summary of diagnostics for imputation performance generated by minimac
*[ Imputation server] - We are running imputation (and pre-phasing) for you!
= Download =
A binary Linux (64 bit) version of minimac is available [ from here] and source code [ from here]
A full release of <code>minimac</code> is expected here soon. In the meantime, a binary only Linux The current version of minimac is available [ from here] for those who are willing to test pre-release software. The current version should be stamped 20112013.087.12 17 - if your version shows a different version number or date stamp when it runs, it is not current.
If you use this beta version, please be sure to stop by the [ MaCH download page] and fill out the registration form, so that we can let you know when an official release is available and keep you updated with respect to any bug fixes.
The current version of minimac comes in two flavours, <code>minimac</code> and <code>minimac-omp</code>. The latter version uses the [[OpenMP]] protocol to support multi-threading, resulting in faster throughput.
BE AWARE: since this version of minimac runs in parallel the order of samples in the output files (*dose, *haps,...) will vary between runs. Therefore, e.g. chunks have to be merged by sample id.
== Change log ==
- minor bug fixes
-- all variants (SNPs, InDels, SVs) in the reference VCF will be imputed - independent from the FILTER column setting
- improved performance (Thanks to David Hinds - see also [[minimac2]] for the full set of performance improvements)
- minor bug fixes
- added: improved support for [ ShapeIT] phased haplotypes
- added: full support for reference panel based chunking
- fixed: chunk chromosome bug
2012.8.6 (early adopter)
- added: chromosome X support
- fixed: problem with --startposition
- added: VCF support
- added: IDR (Insertion, Deletion, Reference) support
== Questions and Comments ==
=== Preparing Your 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).
|style=white-space:nowrap|<code>-d sample.dat</code>
| Data file in [ Merlin format]. Markers should be listed according to their order along the chromosome.
| <code>-p sample.ped</code>
| Pedigree file in [ Merlin format]. Alleles should be labeled on the forward strand.
| <code>--states 200</code>
A typical minimac command line might look like this:
==== using a VCF reference panel ====
minimac --vcfReference --refHaps ref.vcf.gz --haps target.hap.gz --snps target.snps.gz --rounds 5 --states 200 --prefix results
'''Note''': GWAS SNPs (file --snps target.snps.gz) are by default expected to be in the chr:pos format e.g. 1:1000 and on build37/hg19; otherwise, please set the --rs flag
==== using a MaCH reference panel ====
minimac --refHaps ref.hap.gz --refSnps ref.snps.gz --haps target.hap.gz --snps target.snps.gz --rounds 5 --states 200 --prefix results
| <code>--refHaps ref.hap.gz </code>
| Reference haplotypes (e.g. from [ MaCH download page])|- | <code>--vcfReference </code> | This option specifies that the provided --refHaps file is provided in VCF format , no --refSNPs file needed.|- | <code>--rs </code> | In combination with --vcfReference, allows to use rs GWAS SNP identifiers
| <code>--snps target.snps.gz </code>
| <code>--haps target.hap.gz </code>
| Phased haplotypes where missing genotypes will be imputed.
| <code>--sample target.sample </code>
| Sample list in ShapeIT format.
| <code>--shape_haps target.hap.gz </code>
| ShapeIT phased haplotypes where missing genotypes will be imputed.
| <code>--chr 22</code>
| Chromosome for which we will carry out imputation (needed to run ShapeIT with chr:pos identifiers - default setting).
| <code>--rounds 5</code>
=== 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]. As of this writing, the most recent set of haplotypes are based on genotype calls were generated in May 2011 and are an interim analysis of Project's Phase I data.
=== Imputation quality evaluation ===
=== Additional Sources of Information ===
If the combination of MaCH and Minimac still runs too slowly for you, and you have access to a multi-processor compute cluster, you can look at [[ChunkChromosomesChunkChromosome]] page to learn how to conveniently split each chromosome into multiple segments that can be analyzed in parallel. For information on how to put the resulting chunks back together, see [[Ligate Minimac|this page]].
If you are especially interested in 1000 Genomes Imputation, then you should look at the [[Minimac: 1000 Genomes Imputation Cookbook]].
= Post-imputation Association Analysis =
== Quantitative Traits ==
Please use [ mach2qtl].
== Binary Traits ==
Please use [ mach2dat]. Versions 1.0.18 and above accommodate to minimac output. = Reference = If you use [[minimac]] or [[minimac2]] please cite:  Fuchsberger C, Abecasis GR, Hinds DA. minimac2: faster genotype imputation. Bioinformatics 2014 [] Howie B, Fuchsberger C, Stephens M, Marchini J, and Abecasis GR.Fast and accurate genotype imputation in genome-wide association studiesthrough pre-phasing. Nature Genetics 2012 [] = Questions and Comments = Please contact [ Goncalo Abecasis] or [ Christian Fuchsberger].
= Related Pages =