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* Estimates of genetic effects and locus genetic variance
 
* Estimates of genetic effects and locus genetic variance
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== Workflow for using rareMETALS ==
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== Preparing Input Files for rareMETALS ==
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* Generate summary level statistic files: Summary statistics files can be generated by rvtests [https://github.com/zhanxw/rvtests] or rare-metal-worker [http://genome.sph.umich.edu/wiki/Rare-Metal-Worker]
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# Generate summary level statistic files: Summary statistics files can be generated by rvtests [https://github.com/zhanxw/rvtests] or rare-metal-worker [http://genome.sph.umich.edu/wiki/Rare-Metal-Worker]
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* Annotate your summary level statistics: In order to perform gene-level association test, summary level statistics file have to be annotated first. The default program for performing annotations is ANNO (by Xiaowei Zhan). The usage of the program can be found at [https://github.com/zhanxw/anno]
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# Annotate your summary level statistics: In order to perform gene-level association test, summary level statistics file have to be annotated first. The default program for performing annotations is ANNO (by Xiaowei Zhan). The usage of the program can be found at [https://github.com/zhanxw/anno]
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* Preparing input files for rareMETALS: rareMETALS R-package takes compressed and tabix-indexed files as input for performing meta-analysis  
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# Compress and Index Summary Statistics: Files rareMETALS R-package takes compressed and tabix-indexed files as input for performing meta-analysis  
 
    
 
    
* Performing single variant association analysis: Single variant association analysis statistics can be calculated using the following function in the package:
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== Performing single variant association analysis ==
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* Single variant association analysis statistics can be calculated using the following function in the package:
 
   rareMETALS.single(score.stat.file, cov.file, range, alternative = c("two.sided", "greater", "less"), ix.gold = 1, callrate.cutoff = 0, hwe.cutoff = 0)
 
   rareMETALS.single(score.stat.file, cov.file, range, alternative = c("two.sided", "greater", "less"), ix.gold = 1, callrate.cutoff = 0, hwe.cutoff = 0)
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* Input parameters are described below:
 
** score.stat.file is the vector of file names for single variant score statistics.
 
** score.stat.file is the vector of file names for single variant score statistics.
 
** cov.file is the vector of files of covariance matrices for single variant score statistics
 
** cov.file is the vector of files of covariance matrices for single variant score statistics
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** hwe.cutoff specifies the cutoffs for call rate, All sites with call rate lower than the cutoff will be labeled as missing.     
 
** hwe.cutoff specifies the cutoffs for call rate, All sites with call rate lower than the cutoff will be labeled as missing.     
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* Performing gene level association test  
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== Performing gene level association test ==
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* Gene-level association test can be performed using the following function:
 
   rareMETALS.gene(ANNO, score.stat.file, cov.file, gene, test = "GRANVIL",maf.cutoff, no.boot = 10000, alternative = c("two.sided", "greater",
 
   rareMETALS.gene(ANNO, score.stat.file, cov.file, gene, test = "GRANVIL",maf.cutoff, no.boot = 10000, alternative = c("two.sided", "greater",
 
   "less"), alpha = 0.05, ix.gold = 1, out.digits = 4, callrate.cutoff = 0, hwe.cutoff = 0, gene.file = "refFlat_hg19.txt.gz")
 
   "less"), alpha = 0.05, ix.gold = 1, out.digits = 4, callrate.cutoff = 0, hwe.cutoff = 0, gene.file = "refFlat_hg19.txt.gz")
** Input parameters:
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* Input parameters are described below:
*** ANNO is the annotation information for variants. Possible choices include Nonsynonymous, Stop_Gain, Stop_Loss, Synonymous, Essential_Splice_Site, or any logical combination of them, such as "Nonsynonymous|Stop_Gain|Stop_Loss"  
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** ANNO is the annotation information for variants. Possible choices include Nonsynonymous, Stop_Gain, Stop_Loss, Synonymous, Essential_Splice_Site, or any logical combination of them, such as "Nonsynonymous|Stop_Gain|Stop_Loss"  
*** score.stat.file is the vector of file names for single variant score statistics.
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** score.stat.file is the vector of file names for single variant score statistics.
*** cov.file is the vector of files of covariance matrices for single variant score statistics
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** cov.file is the vector of files of covariance matrices for single variant score statistics
*** gene is the gene name such as PCSK9
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** gene is the gene name such as PCSK9
*** no.boot is the number of bootstraps performed for evaluating significance, such as 10,000. If you choose to use analytic evaluation, please specify no.boot=0
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** no.boot is the number of bootstraps performed for evaluating significance, such as 10,000. If you choose to use analytic evaluation, please specify no.boot=0
*** alternative specifies alternative hypothesis to be tested. The default is two.sided.  
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** alternative specifies alternative hypothesis to be tested. The default is two.sided.  
*** ix.gold is the index to be used for choosing a "gold standard" population, in case flips of alleles are observed, and the gold standard population can be used to correct for the flips
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** ix.gold is the index to be used for choosing a "gold standard" population, in case flips of alleles are observed, and the gold standard population can be used to correct for the flips
*** out.digits is the number of digits in the output, which is used to prettify output.  
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** out.digits is the number of digits in the output, which is used to prettify output.  
*** callrate.cutoff specifies the call rate cutoffs that will be used. All sites with call rates lower than the cutoff will be labelled as missing.  
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** callrate.cutoff specifies the call rate cutoffs that will be used. All sites with call rates lower than the cutoff will be labelled as missing.  
*** hwe.cutoff specifies the cutoffs for call rate, All sites with call rate lower than the cutoff will be labeled as missing.     
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** hwe.cutoff specifies the cutoffs for call rate, All sites with call rate lower than the cutoff will be labeled as missing.     
*** gene.file is a resource to locate gene region
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** gene.file is a resource to locate gene region
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** Output: The output res.out consist of the following fields:  
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* Output: The output res.out consist of the following fields:  
*** gene.name.out: gene names
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** gene.name.out: gene names
*** p.value.out: P-value
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** p.value.out: P-value
*** statistic.out: Score statistics for meta-analysis
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** statistic.out: Score statistics for meta-analysis
*** no.site.out: Number of variant sites in the gene.  
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** no.site.out: Number of variant sites in the gene.  
*** beta1.est.out: Estimates for beta.  
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** beta1.est.out: Estimates for beta.  
*** beta1.sd.out: Standard deviation for the beta estimates
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** beta1.sd.out: Standard deviation for the beta estimates
*** maf.cutoff.out: The minor allele frequency cutoffs used to analyze the data
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** maf.cutoff.out: The minor allele frequency cutoffs used to analyze the data
*** direction.burden.by.study.out: Direction of meta-analysis burden statistics across different studies   
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** direction.burden.by.study.out: Direction of meta-analysis burden statistics across different studies   
*** direction.meta.single.var.out: Direction of meta-analysis statistics for single variant test. It may be useful for inspecting if any of the variant in the gene have opposite effects etc.   
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** direction.meta.single.var.out: Direction of meta-analysis statistics for single variant test. It may be useful for inspecting if any of the variant in the gene have opposite effects etc.   
*** pos.ref.alt.out: Position, reference and alternative alleles for each variant position in the gene
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** pos.ref.alt.out: Position, reference and alternative alleles for each variant position in the gene
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* Performing conditional analysis
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== Performing conditional analysis ==
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