Minimac: 1000 Genomes Imputation Cookbook - Revision history

Cfuchsb at 07:40, 28 January 2015

2015-01-28T07:40:12Z

← Older revision		Revision as of 03:40, 28 January 2015
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	=== Your Own Data ===		=== 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 [http://~~www~~.sph.umich.edu~~/csg~~/abecasis/Merlin/tour/input_files.html Merlin tutorial].		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://csg.sph.umich.edu/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).		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 latest reference panel generated by the 1000 Genomes project uses NCBI Build 37 (HG 19). Make sure that your data is on Build 37 (or Minimac may ignore genotyped markers whose names have changed in Build 37). If you are trying to convert your data from an earlier genome build to Build 37, you'll probably find the [ftp://ftp.ncbi.nih.gov/snp/organisms/human_9606/database/organism_data/RsMergeArch.bcp.gz dbSNP merge table] ([http://www.ncbi.nlm.nih.gov/SNP/snp_db_table_description.cgi?t=RsMergeArch table description on the NCBI website]), which logs rs# changes between dbSNP builds, and the UCSC online [http://genome.ucsc.edu/cgi-bin/hgLiftOver liftOver tool], which converts genome positions between different genome builds, to be quite useful. We have also documented ([[LiftOver \| Link]]) a general procedure to convert genome positions and rs number between builds.		The latest reference panel generated by the 1000 Genomes project uses NCBI Build 37 (HG 19). Make sure that your data is on Build 37 (or Minimac may ignore genotyped markers whose names have changed in Build 37). If you are trying to convert your data from an earlier genome build to Build 37, you'll probably find the [ftp://ftp.ncbi.nih.gov/snp/organisms/human_9606/database/organism_data/RsMergeArch.bcp.gz dbSNP merge table] ([http://www.ncbi.nlm.nih.gov/SNP/snp_db_table_description.cgi?t=RsMergeArch table description on the NCBI website]), which logs rs# changes between dbSNP builds, and the UCSC online [http://genome.ucsc.edu/cgi-bin/hgLiftOver liftOver tool], which converts genome positions between different genome builds, to be quite useful. We have also documented ([[LiftOver \| Link]]) a general procedure to convert genome positions and rs number between builds.

	If you are planning to use imputation with the MetaboChip, you might find a list of SNPs whose order varies between NCBI genome Build 36 and 37 convenient. Here it is: [http://~~www~~.sph.umich.edu~~/csg~~/cfuchsb/metab_order_changed.txt List of Metabochip SNPs Whose Order Changes With Build]		If you are planning to use imputation with the MetaboChip, you might find a list of SNPs whose order varies between NCBI genome Build 36 and 37 convenient. Here it is: [http://csg.sph.umich.edu/cfuchsb/metab_order_changed.txt List of Metabochip SNPs Whose Order Changes With Build]

	Note: For the most recent reference panel in VCF format by default GWAS SNPs are expected to be in the chr:pos format e.g. 1:1000; otherwise, for GWAS SNPs in the rs format you have to set the --rs flag		Note: For the most recent reference panel in VCF format by default GWAS SNPs are expected to be in the chr:pos format e.g. 1:1000; otherwise, for GWAS SNPs in the rs format you have to set the --rs flag
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	=== Reference Haplotypes ===		=== 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/ MaCH download page]. In our hands, it is ideal to always use the most recent release since generation of additional sequence data, improvements in variant discovery, genotyping and haplotyping strategies typically result in noticeable improvements in data quality. When this page was written, the most recent release of 1000 Genomes project haplotypes was the [http://www.sph.umich.edu/csg/abecasis/MACH/download/1000G.2012-03-14.html Integrated Phase I release], including 1092 individuals. We recommend using the reduced GIANT all population panel (no monomorphic and singleton sites)		Reference haplotypes generated by the 1000 Genomes project and formatted so that they are ready for analysis are available from the [http://csg.sph.umich.edu/abecasis/MaCH/download/ MaCH download page]. In our hands, it is ideal to always use the most recent release since generation of additional sequence data, improvements in variant discovery, genotyping and haplotyping strategies typically result in noticeable improvements in data quality. When this page was written, the most recent release of 1000 Genomes project haplotypes was the [http://www.sph.umich.edu/csg/abecasis/MACH/download/1000G.2012-03-14.html Integrated Phase I release], including 1092 individuals. We recommend using the reduced GIANT all population panel (no monomorphic and singleton sites)
	[ftp://share.sph.umich.edu/1000genomes/fullProject/2012.03.14/GIANT.phase1_release_v3.20101123.snps_indels_svs.genotypes.refpanel.ALL.vcf.gz.tgz GIANT.phase1_release_v3.20101123.snps_indels_svs.genotypes.refpanel.ALL.vcf.gz.tgz]		[ftp://share.sph.umich.edu/1000genomes/fullProject/2012.03.14/GIANT.phase1_release_v3.20101123.snps_indels_svs.genotypes.refpanel.ALL.vcf.gz.tgz GIANT.phase1_release_v3.20101123.snps_indels_svs.genotypes.refpanel.ALL.vcf.gz.tgz]

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	mach1 -d chr1.dat -p chr1.ped --rounds 20 --states 200 --phase --interim 5 --sample 5 --prefix chr$chr.haps		mach1 -d chr1.dat -p chr1.ped --rounds 20 --states 200 --phase --interim 5 --sample 5 --prefix chr$chr.haps

	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 [http://~~www~~.sph.umich.edu~~/csg~~/abecasis/MaCH/ MaCH website]):		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 [http://csg.sph.umich.edu/abecasis/MaCH/ MaCH website]):

	{\| class="wikitable" border="1" cellpadding="2"		{\| class="wikitable" border="1" cellpadding="2"
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	\|-		\|-
	\|style=white-space:nowrap\|<code>-d sample.dat</code>		\|style=white-space:nowrap\|<code>-d sample.dat</code>
	\| 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.		\| Data file in [http://csg.sph.umich.edu/abecasis/Merlin/tour/input_files.html Merlin format]. Markers should be listed according to their order along the chromosome.
	\|-		\|-
	\| <code>-p sample.ped</code>		\| <code>-p sample.ped</code>
	\| 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.		\| Pedigree file in [http://csg.sph.umich.edu/abecasis/Merlin/tour/input_files.html Merlin format]. Alleles should be labeled on the forward strand.
	\|-		\|-
	\| <code>--states 200</code>		\| <code>--states 200</code>
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	\|-		\|-
	\| <code>--refHaps ref.hap.gz </code>		\| <code>--refHaps ref.hap.gz </code>
	\| Reference haplotypes (e.g. from [http://~~www~~.sph.umich.edu~~/csg~~/abecasis/MACH/download/1000G-2010-06.html MaCH download page])		\| Reference haplotypes (e.g. from [http://csg.sph.umich.edu/abecasis/MACH/download/1000G-2010-06.html MaCH download page])
	\|-		\|-
	\| <code>--vcfReference </code>		\| <code>--vcfReference </code>

Cfuchsb: /* Your Own Data */

2013-07-26T15:35:39Z

Your Own Data

← Older revision		Revision as of 11:35, 26 July 2013
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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).

	The latest reference panel generated by the 1000 Genomes project uses NCBI Build 37 (HG 19). Make sure that your data is on Build 37 (or Minimac may ignore genotyped markers whose names have changed in ~~build~~ 37). If you are trying to convert your data from an earlier genome build to ~~build~~ 37, you'll probably find the [ftp://ftp.ncbi.nih.gov/snp/organisms/human_9606/database/organism_data/RsMergeArch.bcp.gz dbSNP merge table] ([http://www.ncbi.nlm.nih.gov/SNP/snp_db_table_description.cgi?t=RsMergeArch table description on the NCBI website]), which logs rs# changes between dbSNP builds, and the UCSC online [http://genome.ucsc.edu/cgi-bin/hgLiftOver liftOver tool], which converts genome positions between different genome builds, to be quite useful. We have also documented ([[LiftOver \| Link]]) a general procedure to convert genome positions and rs number between builds.		The latest reference panel generated by the 1000 Genomes project uses NCBI Build 37 (HG 19). Make sure that your data is on Build 37 (or Minimac may ignore genotyped markers whose names have changed in Build 37). If you are trying to convert your data from an earlier genome build to Build 37, you'll probably find the [ftp://ftp.ncbi.nih.gov/snp/organisms/human_9606/database/organism_data/RsMergeArch.bcp.gz dbSNP merge table] ([http://www.ncbi.nlm.nih.gov/SNP/snp_db_table_description.cgi?t=RsMergeArch table description on the NCBI website]), which logs rs# changes between dbSNP builds, and the UCSC online [http://genome.ucsc.edu/cgi-bin/hgLiftOver liftOver tool], which converts genome positions between different genome builds, to be quite useful. We have also documented ([[LiftOver \| Link]]) a general procedure to convert genome positions and rs number between builds.

	If you are planning to use imputation with the MetaboChip, you might find a list of SNPs whose order varies between NCBI genome ~~build~~ 36 and 37 convenient. Here it is: [http://www.sph.umich.edu/csg/cfuchsb/metab_order_changed.txt List of Metabochip SNPs Whose Order Changes With Build]		If you are planning to use imputation with the MetaboChip, you might find a list of SNPs whose order varies between NCBI genome Build 36 and 37 convenient. Here it is: [http://www.sph.umich.edu/csg/cfuchsb/metab_order_changed.txt List of Metabochip SNPs Whose Order Changes With Build]

	Note: For the most recent reference panel in VCF format by default GWAS SNPs are expected to be in the chr:pos format e.g. 1:1000; otherwise, for GWAS SNPs in the rs format you have to set the --rs flag		Note: For the most recent reference panel in VCF format by default GWAS SNPs are expected to be in the chr:pos format e.g. 1:1000; otherwise, for GWAS SNPs in the rs format you have to set the --rs flag

Cfuchsb: /* 1. Split X chromosome pedigree file by sex */

2013-07-26T15:33:19Z

1. Split X chromosome pedigree file by sex

← Older revision		Revision as of 11:33, 26 July 2013
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	# For females: follow the same protocol as for autosomes		# For females: follow the same protocol as for autosomes
	# For males: have only one X chromosome and are therefore already phased. Simply convert the pedigree file into a MaCH haplotype file (missing genotypes should be encoded as: "0" or "." or "N" )		# For males: they have only one X chromosome and are therefore already phased. Simply convert the pedigree file into a MaCH haplotype file (missing genotypes should be encoded as: "0" or "." or "N" )

Cfuchsb: /* Imputation into Phased Haplotypes - minimac */

2013-07-26T15:29:06Z

Imputation into Phased Haplotypes - minimac

← Older revision		Revision as of 11:29, 26 July 2013
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	Again, you can speed up ~~imputation~~ by running ~~this step~~ in parallel ~~and~~ in our cluster we'd use:		Again, you can speed up things by running imputation in parallel. For example, in our cluster we'd use:

	<source lang="text">		<source lang="text">

Cfuchsb: /* Imputation into Phased Haplotypes - minimac */

2013-07-26T15:26:34Z

Imputation into Phased Haplotypes - minimac

← Older revision		Revision as of 11:26, 26 July 2013
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	Again, you can speed up ~~things~~ by running this step in parallel and in our cluster we'd use:		Again, you can speed up imputation by running this step in parallel and in our cluster we'd use:

	<source lang="text">		<source lang="text">

Cfuchsb: /* Imputation into Phased Haplotypes - minimac */

2013-07-26T15:26:01Z

Imputation into Phased Haplotypes - minimac

← Older revision		Revision as of 11:26, 26 July 2013
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	Again, you can speed-up things by running this step in parallel and in our cluster we'd use:		Again, you can speed up things by running this step in parallel and in our cluster we'd use:

	<source lang="text">		<source lang="text">

Cfuchsb: /* Imputation into Phased Haplotypes - minimac */

2013-07-26T15:24:39Z

Imputation into Phased Haplotypes - minimac

← Older revision		Revision as of 11:24, 26 July 2013
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	=== Imputation into Phased Haplotypes - minimac ===		=== Imputation into Phased Haplotypes - minimac ===

	Imputing genotypes using '''minimac''' is a 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 estimating model parameters (which describe the length and conservation of haplotype stretches shared between the reference panel and your study samples), imputation should proceed rapidly. Because marker names can change between dbSNP versions, it is usually a good idea to include ''aliases'' file that provides mappings between earlier marker names and the current preferred name for each polymorphism.		Imputing genotypes using '''minimac''' is a 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 estimating model parameters (which describe the length and conservation of haplotype stretches shared between the reference panel and your study samples), imputation should proceed rapidly. Because marker names can change between dbSNP versions, it is usually a good idea to include an ''aliases'' file that provides mappings between earlier marker names and the current preferred name for each polymorphism.

	A typical minimac command line, where the string $chr should be replaced with an appropriate chromosome number, might look like this:		A typical minimac command line, where the string $chr should be replaced with an appropriate chromosome number, might look like this:

Cfuchsb: /* Reference based chunking */

2013-07-26T15:22:55Z

Reference based chunking

← Older revision		Revision as of 11:22, 26 July 2013
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	REMARK: this works only with SNPIDs in the chr:pos format, or in other words, rs numbers are not supported here.		REMARK: this works only with SNPIDs in the chr:pos format, or in other words, rs numbers are not supported here.

	In case you didn't split your data with ChunkChromosome, we highly recommend ~~to run~~ imputation using our reference based chunking method (minimac.11.16.12 or newer required)		In case you didn't split your data with ChunkChromosome, we highly recommend running imputation using our reference based chunking method (minimac.11.16.12 or newer required)

	<source lang="bash">		<source lang="bash">

Cfuchsb: /* Reference Haplotypes */

2013-07-26T15:22:06Z

Reference Haplotypes

← Older revision		Revision as of 11:22, 26 July 2013
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	=== Reference Haplotypes ===		=== 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/ MaCH download page]. In our hands, it is ideal to always use the most recent release since generation of additional sequence data, improvements in variant discovery, genotyping and haplotyping strategies typically result in noticeable improvements in data quality. When this page was written, the most recent release of 1000 Genomes project haplotypes was the [http://www.sph.umich.edu/csg/abecasis/MACH/download/1000G.2012-03-14.html Integrated Phase I release], including 1092 individuals. We recommend ~~to use~~ the reduced GIANT all population panel (no monomorphic and singleton sites)		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/ MaCH download page]. In our hands, it is ideal to always use the most recent release since generation of additional sequence data, improvements in variant discovery, genotyping and haplotyping strategies typically result in noticeable improvements in data quality. When this page was written, the most recent release of 1000 Genomes project haplotypes was the [http://www.sph.umich.edu/csg/abecasis/MACH/download/1000G.2012-03-14.html Integrated Phase I release], including 1092 individuals. We recommend using the reduced GIANT all population panel (no monomorphic and singleton sites)
	[ftp://share.sph.umich.edu/1000genomes/fullProject/2012.03.14/GIANT.phase1_release_v3.20101123.snps_indels_svs.genotypes.refpanel.ALL.vcf.gz.tgz GIANT.phase1_release_v3.20101123.snps_indels_svs.genotypes.refpanel.ALL.vcf.gz.tgz]		[ftp://share.sph.umich.edu/1000genomes/fullProject/2012.03.14/GIANT.phase1_release_v3.20101123.snps_indels_svs.genotypes.refpanel.ALL.vcf.gz.tgz GIANT.phase1_release_v3.20101123.snps_indels_svs.genotypes.refpanel.ALL.vcf.gz.tgz]

Cfuchsb: /* Quality Control */

2013-07-26T15:19:29Z

Quality Control

← Older revision		Revision as of 11:19, 26 July 2013
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	With older genotyping platforms, low frequency SNPs are also often excluded because they are hard to genotype accurately. With more modern genotyping arrays, the accuracy of genotype calls for low frequency SNPs is less of a concern.		With older genotyping platforms, low frequency SNPs are also often excluded because they are hard to genotype accurately. With more modern genotyping arrays, the accuracy of genotype calls for low frequency SNPs is less of a concern.

	Two good ways to verify that you have used appropriate quality control steps are to generate a Q-Q plot for your dataset and to calculate a genomic control parameter. If these are not satisfactory, it may be useful to investigate before proceeding to imputation.		Two good ways to verify that you have used appropriate quality control steps are to generate a Q-Q plot for your dataset and to calculate a genomic control parameter. If these verifications are not satisfactory, it may be useful to investigate before proceeding to imputation.

	=== Reference Haplotypes ===		=== Reference Haplotypes ===