METAL Documentation - Revision history

Welchr: /* Sample Overlap Correction */

2017-12-22T19:52:43Z

Sample Overlap Correction

← Older revision		Revision as of 15:52, 22 December 2017
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	To enable correction for sample overlap in your sample size weighted meta-analysis, use OVERLAP ON command (valid only with SCHEME SAMPLESIZE). By default, METAL uses Z-statistics <1 for esimating the number of individuals that are common among studies. To change this threshold, use ZCUTOFF [number] command.		To enable correction for sample overlap in your sample size weighted meta-analysis, use OVERLAP ON command (valid only with SCHEME SAMPLESIZE). By default, METAL uses Z-statistics <1 for esimating the number of individuals that are common among studies. To change this threshold, use ZCUTOFF [number] command.

	~~Details~~ on the method can be found in [[media:METAL_sample_overlap_2017-11-15.pptx\|~~these slides~~]], ~~and eventually as a~~ manuscript (est. 2018).		More information on the method can be found in:

			* [[media:METAL_sample_overlap_2017-11-15.pptx\|Method overview and results]]
			* [[media:METAL_sample_overlap_method_2017-11-15.pdf\|Full method description]] (current draft, manuscript est. 2018)

	=== Strand Information ===		=== Strand Information ===

Welchr: /* Additional Analysis Options */

2017-12-22T19:40:46Z

Additional Analysis Options

← Older revision		Revision as of 15:40, 22 December 2017
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	METAL has the ability to apply a genomic control correction to all input files. METAL will estimate the inflation of the test statistic by comparing the median test statistic to that expected by chance, and then apply the genomic control correction to the p-values (for SAMPLESIZE weighted meta-analysis) or the standard error (for STDERR weighted meta-analysis). This should only be applied to files with whole genome data (i.e. should not be used for settings where results are only available for a candidate locus or a small number of SNPs selected for follow-up of [[GWAS]] results). Genomic control settings can be customized for each input file. We recommend applying genomic control correction to all input files that include genomewide data and, in addition, to the meta-analysis results. To apply genomic control to the meta-analysis results, just perform an initial meta-analysis and then load the initial set of results into METAL to get final, genomic control adjusted results.		METAL has the ability to apply a genomic control correction to all input files. METAL will estimate the inflation of the test statistic by comparing the median test statistic to that expected by chance, and then apply the genomic control correction to the p-values (for SAMPLESIZE weighted meta-analysis) or the standard error (for STDERR weighted meta-analysis). This should only be applied to files with whole genome data (i.e. should not be used for settings where results are only available for a candidate locus or a small number of SNPs selected for follow-up of [[GWAS]] results). Genomic control settings can be customized for each input file. We recommend applying genomic control correction to all input files that include genomewide data and, in addition, to the meta-analysis results. To apply genomic control to the meta-analysis results, just perform an initial meta-analysis and then load the initial set of results into METAL to get final, genomic control adjusted results.

			=== Sample Overlap Correction ===

			Correction for sample overlap in sample size weighted meta-analysis (developed by Sebanti Sengupta and implemented by Daniel Taliun).

			First, METAL estimates the number of individuals that are common among two or more studies based on Z-statistics from each study. Then, METAL adjusts for sample overlap when calculating overall Z-statistics by correcting the weights with the estimated number of individuals in common.

			To enable correction for sample overlap in your sample size weighted meta-analysis, use OVERLAP ON command (valid only with SCHEME SAMPLESIZE). By default, METAL uses Z-statistics <1 for esimating the number of individuals that are common among studies. To change this threshold, use ZCUTOFF [number] command.

			Details on the method can be found in [[media:METAL_sample_overlap_2017-11-15.pptx\|these slides]], and eventually as a manuscript (est. 2018).

	=== Strand Information ===		=== Strand Information ===

Goncalo: /* Specifying Weights in P-value Based Analysis */

2011-09-29T23:19:16Z

Specifying Weights in P-value Based Analysis

← Older revision		Revision as of 19:19, 29 September 2011
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	=== Specifying Weights in P-value Based Analysis ===		=== Specifying Weights in P-value Based Analysis ===

	The weight for each MARKER can be stored in a column in the table (specified with the <code>WEIGHTLABEL</code> or <code>WEIGHT</code> commands).		The weight for each MARKER can be stored in a column in the table (specified with the <code>WEIGHTLABEL</code> or <code>WEIGHT</code> commands). Most commonly, the weight will be the number of individuals contributing to that particular p-value.

	~~WEIGHT~~ N		WEIGHTLABEL N

	Alternatively, the same weight can be used for all markers for that inputfile (in which case the fixed weight can be set with the <code>DEFAULTWEIGHT</code> command). The WEIGHTLABEL command takes precedence over the DEFAULTWEIGHT command, so the WEIGHT column label in use must not match any columns in the inputfile.		Alternatively, the same weight can be used for all markers for that inputfile (in which case the fixed weight can be set with the <code>DEFAULTWEIGHT</code> command). The WEIGHTLABEL command takes precedence over the DEFAULTWEIGHT command, so the WEIGHT column label in use must not match any columns in the inputfile.

	~~WEIGHT~~ DONTUSECOLUMN		WEIGHTLABEL DONTUSECOLUMN
	DEFAULTWEIGHT 1000		DEFAULTWEIGHT 1000

Cristen: /* Specifying Weights in P-value Based Analysis */

2011-05-25T15:16:00Z

Specifying Weights in P-value Based Analysis

← Older revision		Revision as of 11:16, 25 May 2011
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	WEIGHT N		WEIGHT N

	Alternatively, the same weight can be used for all markers for that inputfile (in which case the fixed weight can be set with the <code>DEFAULTWEIGHT</code> command). The WEIGHTLABEL command takes precedence over the DEFAULTWEIGHT command, so the WEIGHT column label in use must not match any columns in the inputfile.		Alternatively, the same weight can be used for all markers for that inputfile (in which case the fixed weight can be set with the <code>DEFAULTWEIGHT</code> command). The WEIGHTLABEL command takes precedence over the DEFAULTWEIGHT command, so the WEIGHT column label in use must not match any columns in the inputfile.

	WEIGHT DONTUSECOLUMN		WEIGHT DONTUSECOLUMN

Cristen: /* Specifying Weights in P-value Based Analysis */

2011-05-25T15:15:47Z

Specifying Weights in P-value Based Analysis

← Older revision		Revision as of 11:15, 25 May 2011
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	=== Specifying Weights in P-value Based Analysis ===		=== Specifying Weights in P-value Based Analysis ===

	The weight for each MARKER can be stored in a column in the table (specified with the <code>WEIGHTLABEL</code> or <code>WEIGHT</code> commands). Alternatively, the same weight can be used for all markers for that inputfile (in which case the fixed weight can be set with the <code>DEFAULTWEIGHT</code> command). The WEIGHTLABEL command takes precedence over the DEFAULTWEIGHT command, so the WEIGHT column label in use must not match any columns in the inputfile.		The weight for each MARKER can be stored in a column in the table (specified with the <code>WEIGHTLABEL</code> or <code>WEIGHT</code> commands).

			WEIGHT N

			Alternatively, the same weight can be used for all markers for that inputfile (in which case the fixed weight can be set with the <code>DEFAULTWEIGHT</code> command). The WEIGHTLABEL command takes precedence over the DEFAULTWEIGHT command, so the WEIGHT column label in use must not match any columns in the inputfile.

			WEIGHT DONTUSECOLUMN
			DEFAULTWEIGHT 1000

	=== Reading Each Input File ===		=== Reading Each Input File ===

Cristen: /* Specifying Weights in P-value Based Analysis */

2011-05-25T15:14:00Z

Specifying Weights in P-value Based Analysis

← Older revision		Revision as of 11:14, 25 May 2011
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	=== Specifying Weights in P-value Based Analysis ===		=== Specifying Weights in P-value Based Analysis ===

	The weight for each MARKER can be stored in a column in the table (specified with the <code>WEIGHTLABEL</code> or <code>WEIGHT</code> commands) or the same weight can be used for all markers for that inputfile (in which case the fixed weight can be set with the <code>DEFAULTWEIGHT</code> command).		The weight for each MARKER can be stored in a column in the table (specified with the <code>WEIGHTLABEL</code> or <code>WEIGHT</code> commands). Alternatively, the same weight can be used for all markers for that inputfile (in which case the fixed weight can be set with the <code>DEFAULTWEIGHT</code> command). The WEIGHTLABEL command takes precedence over the DEFAULTWEIGHT command, so the WEIGHT column label in use must not match any columns in the inputfile.

	=== Reading Each Input File ===		=== Reading Each Input File ===

Goncalo: /* History */

2011-03-25T13:43:39Z

History

← Older revision		Revision as of 09:43, 25 March 2011
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	== History ==		== History ==

	METAL was developed by Goncalo Abecasis, Yun Li and Cristen Willer [http://www.sph.umich.edu/csg/abecasis/publications/pdf/Bioinformatics.vol.26-pp.2190.pdf manuscript available here]. The first version was developed in 2007 and was used for the analyses presented in [http://www.sph.umich.edu/csg/abecasis/publications/18193045.html Sanna et al (2008)] and [http://www.sph.umich.edu/csg/abecasis/publications/18193043.html Willer et al (2008)]. Since then, it has become quite a popular tool for the analysis of genomewide association scans.		METAL was developed by Goncalo Abecasis, Yun Li and Cristen Willer ([http://www.sph.umich.edu/csg/abecasis/publications/pdf/Bioinformatics.vol.26-pp.2190.pdf manuscript available here]). The first version was developed in 2007 and was used for the analyses presented in [http://www.sph.umich.edu/csg/abecasis/publications/18193045.html Sanna et al (2008)] and [http://www.sph.umich.edu/csg/abecasis/publications/18193043.html Willer et al (2008)]. Since then, it has become quite a popular tool for the analysis of genomewide association scans.

	== Brief Description ==		== Brief Description ==

Goncalo: /* History */

2011-03-25T13:43:16Z

History

← Older revision		Revision as of 09:43, 25 March 2011
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	== History ==		== History ==

	METAL was developed by Goncalo Abecasis, Yun Li and Cristen Willer. The first version was developed in 2007 and was used for the analyses presented in [http://www.sph.umich.edu/csg/abecasis/publications/18193045.html Sanna et al (2008)] and [http://www.sph.umich.edu/csg/abecasis/publications/18193043.html Willer et al (2008)]. Since then, it has become quite a popular tool for the analysis of genomewide association scans.		METAL was developed by Goncalo Abecasis, Yun Li and Cristen Willer [http://www.sph.umich.edu/csg/abecasis/publications/pdf/Bioinformatics.vol.26-pp.2190.pdf manuscript available here]. The first version was developed in 2007 and was used for the analyses presented in [http://www.sph.umich.edu/csg/abecasis/publications/18193045.html Sanna et al (2008)] and [http://www.sph.umich.edu/csg/abecasis/publications/18193043.html Willer et al (2008)]. Since then, it has become quite a popular tool for the analysis of genomewide association scans.

	== Brief Description ==		== Brief Description ==

Goncalo: /* Selecting an Analysis Scheme */

2010-05-17T13:01:01Z

Selecting an Analysis Scheme

← Older revision		Revision as of 09:01, 17 May 2010
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	SCHEME SAMPLESIZE - default approach, uses p-value and direction of effect, weighted according to sample size		SCHEME SAMPLESIZE - default approach, uses p-value and direction of effect, weighted according to sample size
	SCHEME STDERR - classical approach, uses effect size estimates and standard errors		SCHEME STDERR - classical approach, uses effect size estimates and standard errors
			STDERR SE - specify the label for the standard error column.

	By default, METAL combines p-values across studies taking into account a study specific weight (typically, the sample size) and direction of effect. This behavior can be requested explicitly with the <code>SCHEME SAMPLESIZE</code> command. An alternative can be requested with the <code>SCHEME STDERR</code> command and weights effect size estimates using the inverse of the corresponding standard errors. While standard error based weights are more common in the biostatistical literature, if you decide to use this approach, it is very important to ensure that effect size estimates (''beta'' coefficients) and standard errors use the same units in all studies (i.e. make sure that the exact same trait was examined in each study and that the same transformations were applied). Inconsistent use of measurement units across studies is the most common cause of discrepancies between these two analysis strategies.		By default, METAL combines p-values across studies taking into account a study specific weight (typically, the sample size) and direction of effect. This behavior can be requested explicitly with the <code>SCHEME SAMPLESIZE</code> command. An alternative can be requested with the <code>SCHEME STDERR</code> command and weights effect size estimates using the inverse of the corresponding standard errors. To enable this option, you will also need to specify which of your input columns contains standard error information using the <code>STDERRLABEL</code> command (or <code>STDERR</code> for short). While standard error based weights are more common in the biostatistical literature, if you decide to use this approach, it is very important to ensure that effect size estimates (''beta'' coefficients) and standard errors use the same units in all studies (i.e. make sure that the exact same trait was examined in each study and that the same transformations were applied). Inconsistent use of measurement units across studies is the most common cause of discrepancies between these two analysis strategies.

	=== Genomic Control Correction ===		=== Genomic Control Correction ===

JoshBis: clarified FILTER section, I hope.

2010-04-15T23:06:41Z

clarified FILTER section, I hope.

← Older revision		Revision as of 19:06, 15 April 2010
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	Together, these two filters would only consider entries where the value in the N column is greater than 1000 and the value in the MAF column is also greater than 0.01.		Together, these two filters would only consider entries where the value in the N column is greater than 1000 and the value in the MAF column is also greater than 0.01.

	Filters can be defined using the <, >, <=, >=, =, != and IN operators. The IS operator tests membership in a set. For example to restrict analysis to three interesting SNPs, use:		Filters can be defined using the <, >, <=, >=, =, != and IN operators. The IS operator tests membership in a set. For example to restrict analysis to three interesting SNPs, use (''note absence of spaces in list of SNPs''):

	ADDFILTER MARKER_ID IN (rs1234,rs123456,rs123)		ADDFILTER MARKER_ID IN (rs1234,rs123456,rs123)