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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]
The current version of minimac should be stamped 2013.7.17 - if your version shows a different version number or date stamp when it runs, it is not current.
A full release of <code>minimac</code> is expected here soon. In the meantime, a binary only Linux version of minimac is available [ from here] for those who are willing to test pre-release software. 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.
== Multiprocessor Version ==
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 ==
== Pre-phasing ==
For the pre-phasing step the , cost for increasing increases quadratically with the number of states is quadratically and linearly with the cost for additional number of rounds is linear. The following table provides a simple example.
{| class="wikitable" border="1" cellpadding="2"
| 200 states
| 12 min = (3 min * 2^<sup>2</sup>)
| 400 states
| 48 min = (3 min * 4^<sup>2</sup>)
| 500 states
| 75 min = (3 min * 5^<sup>2</sup>)
So, in this case running haplotyping with 500 states and 10 rounds = would require 75 min * 10 = 750 min. Typically, haplotype quality improves rapidly with the number of states but only slowly with the number of rounds. We recommend running ~20 rounds of the MaCH haplotyper and selecting a number of states as high as your patience will allow (but ideally greater than 200).
== Imputation ==
These statistics refer to a single core in a modern Intel X7460 CPU running at 2.66 GHz using 1 core and , although your mileage may will 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:
=== 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).
==== NCBI build 36 / NCBI build 37 ====
The 1000G June reference panel is on build 36, future 1000G reference panels will be on build 37. This has also some impact on the pre-phasing:
your data must be on the corresponding build, or in other words, all some SNPs must be will ordered correctly based on the used differently in each genome build(we expect order will be more accurate in more recent builds!). Therefore, if you want to use your pre-phased data to impute into Purists will claim that imputation using NCBI build 36 and NCBI build 37 reference panels, you have to exclude all SNPs that change order between builds.  Here we requires phasing using the exact same reference panel -- others will provide exclusions list for various genotyping platforms:* [http://wwwclaim this makes little Metabo-Chip] 
=== Running MaCH ===
|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>
| 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 100 200 - 400 is typical.
| <code>--rounds 20</code>
| Iterations of the Markov sampler to use for haplotyping. Typically, using 20 - 100 30 rounds should give good results. To obtain better results, it is usually better to increase the <code>--states</code> parameter.
| <code>--interim 5</code>
== Imputation into Phased Haplotypes ==
Imputing genotypes using '''minimac''' is an easy and 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.
=== Creating SNP List File ===
'''Minimac''' requires a file listing markers in the haplotype file. This file can be easily generated by extracting the second column from the .dat file. In a standard Unix system, a command like this should do:
== Imputation into Phased Haplotypes == Imputing genotypes using '''minimac''' is an easy straightforward process: after selecting a set of reference haplotypes, plugging cut -in the f 2 -d " " sample.dat > target haplotypes from the previous step and setting the number of rounds to use for the model parameter estimation, imputation should proceed rapidly.snps
=== Running Minimac ===
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]. The As of this writing, the most recent set of haplotypes are based on genotype calls from August 2010were generated in May 2011 and are an interim analysis of Project's Phase I data.
=== Imputation quality evaluation ===
To evaluate imputation quality, Minimac drops hides data for each of the genotyped SNPs SNP in turn and then calculates 3 statistics:
* looRSQ - this is the estimated rsq for that SNP (as if SNP weren't typed).
* empR - this is the empirical correlation between true and imputed genotypes for the SNP. If this is negative, the SNP is probably flipped.
* empRSQ - this is the actual R2 value, comparing imputed and true genotypes.
These statistics can be found in the *.info file === 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 [[ChunkChromosome]] 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]]. == X Chromosome Imputation ===
minimac supports the imputation of genotypes on the X chromosome (non-pseudo-autosomal part).
## For females: follow the same protocol as for autosomes (phase with MaCH and impute with minimac).
## For males
### Remove the pseudo-autosomal part (for build hg18: chrX:1-2709520 and chrX:154584238-154913754 ; for build hg19 chrX:160001-2709520 2699520 and chrX:154584238154931044-154913754 155260560)### 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.
### '''<Example of a pedigree file >'''
### FAM1003 ID1234 0 0 M A/0 A/0 C/0
### FAM1004 ID5678 0 0 M 0/0 C/0 G/0
### ...
### '''<End of pedigree file>'''
:::: '''<Example of a phased haplotype male only pedigree file>''':::: FAM1003 FAM1003->ID1234 HAPLO1 AAC 0 0 M A/A A/A FAM1003->ID1234 HAPLO2 AACC/C:::: FAM1004 FAM1004->ID5678 HAPLO1 0CG 0 0 M 0/0 C/0 FAM1004->ID5678 HAPLO2 0CGG/G :::: ... :::: '''<End of phased haplotype pedigree file>'''
### Impute using minimac :::: ''Note that, consistent with the Merlin convention, hemizygous males are listed as described aboveif they were homozygous.''
:::: '''<Example of the corresponding haplotype file>'''
:::: FAM1003->ID1234 HAPLO1 AAC
:::: FAM1003->ID1234 HAPLO2 AAC
:::: FAM1004->ID5678 HAPLO1 0CG
:::: FAM1004->ID5678 HAPLO2 0CG
:::: ...
:::: '''<End of the corresponding haplotype file>'''
= 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 studies
through pre-phasing. Nature Genetics 2012 []
= Questions and Comments =
Please contact [ Goncalo Abecasis] or [ Christian Fuchsberger].
= Related Pages =