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RareMETALS (view source)

Revision as of 10:18, 18 May 2017

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→‎Where to download
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rareMETALS is an R-package for performing single or gene-level tests for detecting rare variant associations. For questions regarding the use of this package, please contact Dajiang Liu (dajiang at umich dot edu) or Gonçalo Abecasis (goncalo at umich dot edu). The same methodology is also implemented in command line tools. Please see [http://genome.sph.umich.edu/wiki/Rare-Metal]
+
rareMETALS is an R-package for performing single or gene-level tests for detecting rare variant associations. For questions regarding the use of this package, please contact Dajiang Liu (dajiang.liu at outlook dot com) or Gonçalo Abecasis (goncalo at umich dot edu). The same methodology is also implemented in command line tools. Please see [http://genome.sph.umich.edu/wiki/Rare-Metal]
    
== Change Log ==
 
== Change Log ==
 +
* 05/18/2017 Version 6.8 incorporates a number of new features and bug fixes. We included support for multi-allelic variants, the support for a new conditional analysis method, the support for cohort level genomic controls, and the bug fixes for calculating heterogeneity statistics such as Q and I2.
 +
* 04/09/2016 Version 6.3 is released. Minor bug fix: Due to different level of missingness of variants in the gene, the single variant association statistics calculated using the covariance matrices of score statistics can be different than single variant association statistics calculated using vstat. This has lead to confusions. It has been fixed in version 6.3. The primary results from version 6.2 should be correct.
 +
* 09/25/2015 Version 6.2 is released. Minor bug fix: Removed the incorrect warning information in version 6.1 when quantitative traits are meta-analyzed. The software incorrectly consider it as binary trait and suggested the use of rareMETALS2. 
 +
* 07/23/2015 Version 6.1 is released. Minor feature changes include output for VT the sites where the statistics are maximized; fixed a bug for determining monomorphic sites. Issue warnings when rareMETALS is used to analyze binary trait for meta-analysis. 
 +
* 05/19/2015 Version 6.0 is released. Minor feature addition: rareMETALS can now output of the set of variants that are analyzed in VT (i.e. the set of variants with MAF < the threshold where the VT statistic is maximized).
 +
* 04/01/2015 Version 5.9 is released (not a April's fool joke)! A bug in calculating Cochran-Q statistic is fixed. A bug in conditional.rareMETALS.range.group is also fixed. No other analyses are affected.
 
* 01/24/2015 Version 5.8 is released, which fixed a serious bug for single variant unconditional association tests with group file. If you happen to run the analyses using rareMETALS.single.group() in version 5.7, the results are likely to be incorrect. Please rerun using version 5.8. Please note only rareMETALS.single.group function is affected. All other functions should not be affected by this error.  
 
* 01/24/2015 Version 5.8 is released, which fixed a serious bug for single variant unconditional association tests with group file. If you happen to run the analyses using rareMETALS.single.group() in version 5.7, the results are likely to be incorrect. Please rerun using version 5.8. Please note only rareMETALS.single.group function is affected. All other functions should not be affected by this error.  
 
* 01/04/2015 Version 5.7 is released, which added metrics for heterogeneity of genetic effects, including I2 and Q for single variant association statistics
 
* 01/04/2015 Version 5.7 is released, which added metrics for heterogeneity of genetic effects, including I2 and Q for single variant association statistics
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== Where to download ==
 
== Where to download ==
   −
The R package can be downloaded from [[Media:rareMETALS_5.8.tar.gz | rareMETALS_5.8.tar.gz]]. It will be eventually released on the Comprehensive R-archive Network. If you want to perform gene-level association test using automatically generated annotations, you will also need [[Media:refFlat_hg19.txt.gz | refFlat_hg19.txt.gz]], which is the gene definition modified from refFlat.
+
The R package can be downloaded from [[Media:rareMETALS_6.8.tar.gz | rareMETALS_6.8.tar.gz]]. It will be eventually released on the Comprehensive R-archive Network. If you want to perform gene-level association test using automatically generated annotations, you will also need [[Media:refFlat_hg19.txt.gz | refFlat_hg19.txt.gz]], which is the gene definition modified from refFlat.
 +
 
 +
== Documentation ==
 +
 
 +
An R automatically generated documentation is available here: [[Media:rareMETALS-manual.pdf | rareMETALS-manual.pdf]]. Please note that it is still rough in places. Please let us know if you see any problems. Thanks!
 +
 
 +
== Forum ==
 +
 
 +
I have created a google group for discussion on the usage and for bug reports etc. As you can see, there are numerous updates to the package since its release, thanks to the valuable suggestions from many users. We are committed to continue to update the package and improve its functionalities. If you find any issues to the package and think that the discussions may also benefit others, please post them to the user group. Here is the link to the discussion group https://groups.google.com/forum/#!forum/raremetals
    
== How to install ==
 
== How to install ==
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* Estimates of genetic effects and locus genetic variance
 
* Estimates of genetic effects and locus genetic variance
 
* Estimate measures of genetic effect heterogeneities between studies  
 
* Estimate measures of genetic effect heterogeneities between studies  
 +
 +
== Exemplar Dataset==
 +
 +
Four datasets are useful to get you started on how to use rareMETALS R package for meta-analyses of gene-level association test
 +
 +
[[Media:study1.MetaScore.assoc.gz]] [[Media:study2.MetaScore.assoc.gz]] [[Media:study1.MetaCov.assoc.gz]] [[Media:study2.MetaCov.assoc.gz]]
    
== How to Generate Summary Association Statistics and Prepare Them for Meta-analysis ==
 
== How to Generate Summary Association Statistics and Prepare Them for Meta-analysis ==
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Once you have generated summary association statistics, you need to compress them with bgzip, and index them with tabix. If you use RAREMETALWORKER, the command should be like  
 
Once you have generated summary association statistics, you need to compress them with bgzip, and index them with tabix. If you use RAREMETALWORKER, the command should be like  
 +
 +
'''NOTE: Tabix 1.X does not seem to support the indexing for generic tab-delimited files. To index the file, please use tabix 0.2.5 or earlier versions.
    
If you use RVTESTS, your command should be
 
If you use RVTESTS, your command should be
   −
bgzip study1.MetaScore.assoc
+
  bgzip study1.MetaScore.assoc
   −
tabix -s 1 -b 2 -e 2 -S 1 study1.MetaScore.assoc.gz
+
  tabix -s 1 -b 2 -e 2 -S 1 study1.MetaScore.assoc.gz
   −
tabix -s 1 -b 2 -e 2 -S 1 study1.MetaCov.assoc.gz
+
  tabix -s 1 -b 2 -e 2 -S 1 study1.MetaCov.assoc.gz
    
== A Simple Tutorial for Using the rareMETALS.single function ==
 
== A Simple Tutorial for Using the rareMETALS.single function ==
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rareMETALS.single function allow you to perform meta-analyses for single variant association tests. The summary association statistics are combined using Mantel Haenszel test statistic. The details are described in our method paper Liu et al, Nat Genet, 2014.  
 
rareMETALS.single function allow you to perform meta-analyses for single variant association tests. The summary association statistics are combined using Mantel Haenszel test statistic. The details are described in our method paper Liu et al, Nat Genet, 2014.  
   −
Assume that you have a set of single variant score statistics and their covariance matrices. The examplar command can be:
+
Assume that you have a set of single variant score statistics and their covariance matrices.  
 +
 
 +
Example:
 +
 
 +
  cov.file <- c("study1.MetaCov.assoc.gz","study2.MetaCov.assoc.gz")
 +
  score.stat.file <- c("study1.MetaScore.assoc.gz","study2.MetaScore.assoc.gz")
 +
 
 +
  library(rareMETALS)
 +
  res <- rareMETALS.single(score.stat.file,cov.file=NULL,range="19:11200093-11201275",alternative="two.sided",ix.gold=1,callrate.cutoff=0,hwe.cutoff=0)
 +
 +
###result can be explored as below###
 +
  > names(res)
 +
  [1] "p.value"            "ref"                "alt"                "integratedData"    "raw.data"         
 +
  [6] "clean.data"        "statistic"          "direction.by.study" "anno"              "maf"             
 +
  [11] "QC.by.study"        "no.sample"          "beta1.est"          "beta1.sd"          "hsq.est"         
 +
  [16] "nearby"            "pos"   
 +
  > print(res$pos)
 +
  [1] "19:11200093" "19:11200213" "19:11200235" "19:11200272" "19:11200282" "19:11200309" "19:11200412" "19:11200419"
 +
  [9] "19:11200431" "19:11200442" "19:11200475" "19:11200508" "19:11200514" "19:11200557" "19:11200579" "19:11200728"
 +
  [17] "19:11200753" "19:11200754" "19:11200806" "19:11200839" "19:11200840" "19:11200896" "19:11201124" "19:11201259"
 +
  [25] "19:11201274" "19:11201275"
 +
  > print(res$p.value)
 +
  [1] 0.551263675 0.056308558 0.172481571 0.734935815 0.922326732 0.053804524 0.886985353 0.903835162 0.005280228 0.266575301
 +
  [11] 0.196122312 0.157114376 0.951477852 0.840523624 0.759482777 0.112743041 0.414147263 0.825877149 0.006090142 0.096474975
 +
[21] 0.096474975 0.956407850 0.038234190 0.253512486 0.550935361 0.482315038
 +
 
 +
== A Simple Tutorial for Using the rareMETALS.single.group function ==
 +
 
 +
Dataset used to get the refaltList  [[Media:groupFile.txt.gz]]
 +
 
 +
  res.site<-read.table("groupFile.txt",header=T)
 +
  refaltList <- list(pos=paste(res.site[,1],res.site[,2],sep=":"),ref=res.site$AF,alt=res.site$ALT,af=res.site$AF,af.diff.max=0.5,checkAF=T)
 +
  res31<-rareMETALS.single.group(score.stat.file,cov.file=NULL, range="19:11200093-11201275", refaltList,
 +
                        alternative = c("two.sided"), callrate.cutoff = 0,
 +
                        hwe.cutoff = 0, correctFlip = TRUE, analyzeRefAltListOnly = TRUE)
 +
 
 +
  ###result can be explored as below###
 +
  > names(res31)
 +
  [1] "p.value"            "ref"                "alt"                "integratedData"    "raw.data"         
 +
  [6] "clean.data"        "statistic"          "direction.by.study" "anno"              "maf"             
 +
  [11] "maf.byStudy"        "maf.maxdiff.vec"    "ix.maf.maxdiff.vec" "maf.sd.vec"        "no.sample.mat"   
 +
  [16] "no.sample"          "beta1.est"          "beta1.sd"          "QC.by.study"        "hsq.est"         
 +
  [21] "nearby"            "cochranQ.stat"      "cochranQ.df"        "cochranQ.pVal"      "I2"               
 +
  [26] "log.mat"            "pos"             
 +
  > print(res31$pos)
 +
  [1] "19:11200093" "19:11200213" "19:11200235" "19:11200272" "19:11200282" "19:11200309" "19:11200412" "19:11200419"
 +
  [9] "19:11200431" "19:11200442" "19:11200475" "19:11200508" "19:11200514" "19:11200557" "19:11200579" "19:11200728"
 +
  [17] "19:11200753" "19:11200754" "19:11200806" "19:11200839" "19:11200840" "19:11200896" "19:11201124" "19:11201259"
 +
  [25] "19:11201274" "19:11201275"
 +
  > print(res31$p.value)
 +
  [1]        NA        NA        NA        NA 0.9223267        NA        NA        NA        NA        NA        NA        NA
 +
  [13]        NA        NA        NA        NA        NA        NA        NA        NA        NA        NA        NA        NA
 +
  [25]        NA        NA
 +
 
 +
== A Simple Tutorial for Using the rareMETALS.range function ==
 +
 
 +
res <- rareMETALS.range(score.stat.file,cov.file,range="19:11200093-11201275",range.name="LDLR",test = "GRANVIL",maf.cutoff = 0.05,alternative = c("two.sided"),ix.gold = 1,out.digits = 4,callrate.cutoff = 0,hwe.cutoff = 0,max.VT = NULL)
 +
print(res$res.out)
 +
 
 +
<pre>
 +
  gene.name.out p.value.out statistic.out no.site.out beta1.est.out
 +
[1,] "LDLR"        "0.6064"    "0.2654"      "25"        "-0.01729"
 +
    beta1.sd.out maf.cutoff.out direction.burden.by.study.out
 +
[1,] "0.03357"    "0.05"        "--"
 +
    direction.meta.single.var.out top.singlevar.pos top.singlevar.refalt
 +
[1,] "---++-+--+-+++++--+++++-+"  "19:11200431"    "C/T"
 +
    top.singlevar.pval top.singlevar.af
 +
[1,] "0.004709"        "0.01038"
 +
    pos.ref.alt.out     
 +
 
 +
 
 +
 
 +
[1,] "19:11200093/T/C,19:11200213/G/A,19:11200235/G/A,19:11200272/C/A,19:11200282/G/A,19:11200309/C/A,19:11200412/C/T,19:11200419/C/T,19:11200431/C/T,19:1120\
 +
0442/G/A,19:11200475/C/G,19:11200508/G/A,19:11200514/C/T,19:11200557/G/A,19:11200579/C/T,19:11200728/C/T,19:11200753/T/C,19:11200754/G/A,19:11200806/C/T,19:1\
 +
1200839/T/A,19:11200840/C/A,19:11200896/C/T,19:11201259/G/C,19:11201274/C/T,19:11201275/A/T"
 +
</pre>
 +
 
 +
</pre>
 +
gene.name.out p.value.out statistic.out no.site.out beta1.est.out beta1.sd.out maf.cutoff.out direction.burden.by.study.out direction.meta.single.var.out top.singlevar.pos
 +
[1,] "LDLR"        "0.01916"  "5.487"      "25"        "-0.3575"    "0.1526"    "0.05"        "--"                          "---++-+--+-+++++--+++++-+"  "19:11200309"   
 +
    top.singlevar.refalt top.singlevar.pval top.singlevar.af
 +
[1,] "C/A"                "0.01047"          "0.01538"     
 +
    pos.ref.alt.out   
 +
                                                                                                                                                                                                                                                                                                                                                                                           
 +
[1,]"19:11200093/T/C,19:11200213/G/A,19:11200235/G/A,19:11200272/C/A,19:11200282/G/A,19:11200309/C/A,19:11200412/C/T,19:11200419/C/T,19:11200431/C/T,19:11200442/G/A,19:11200475/C/G,
 +
  19:11200508/G/A,19:11200514/C/T,19:11200557/G/A,19:11200579/C/T,19:11200728/C/T,19:11200753/T/C,19:11200754/G/A,19:11200806/C/T,19:11200839/T/A,19:11200840/C/A,19:11200896/C/T,
 +
  19:11201259/G/C,19:11201274/C/T,19:11201275/A/T"
 +
                                                                                                                                                                                                                                                                                                                                                                                 
 +
</pre>
 +
 
 +
 
 +
More detailed results can be found in a list res$res.list
 +
 
 +
== A Simple Tutorial for Using the rareMETALS.range.group function ==
 +
 
 +
res32<-rareMETALS.range.group(score.stat.file, cov.file, range="19:11200093-11201275", range.name="LDLR",
 +
                      test = "GRANVIL", refaltList, maf.cutoff = 1,
 +
                      alternative = c("two.sided"), out.digits = 4,
 +
                      callrate.cutoff = 0, hwe.cutoff = 0, max.VT = NULL,
 +
                      correctFlip = TRUE, analyzeRefAltListOnly = TRUE)
 +
print(res32$res.out)
 +
 
 +
    gene.name.out N.out  p.value.out statistic.out no.site.out beta1.est.out beta1.sd.out maf.cutoff.out
 +
[1,] "LDLR"        "2504" "0.8629"    "0.0298"      "1"        "0.1764"      "1.044"      "1"         
 +
    direction.burden.by.study.out direction.meta.single.var.out top.singlevar.pos top.singlevar.refalt top.singlevar.pval
 +
[1,] "+-"                          "+"                          "19:11200282"    "3/1"                "0.8629"         
 +
    top.singlevar.af pos.ref.alt.out 
 +
[1,] "0.000599"      "19:11200282/G/A"
 +
 
 +
== A Simple Tutorial for Using the conditional.rareMETALS.single==
 +
It is well known that, owing to linkage disequilibrium, one or more common causal variants can result in shadow association signals at other nearby common variants, use RareMETALS to perform conditional analysis for single variant tests
 +
 
 +
example:
 +
 
 +
  res<-conditional.rareMETALS.single(candidate.variant.vec=c("19:11200282","19:11200309"), score.stat.file, cov.file,
 +
                              known.variant.vec=c("19:11200754","19:11200806","19:11200839"), maf.cutoff=0.05, no.boot =1000,
 +
                              alternative = c("two.sided"), ix.gold = 1,
 +
                              out.digits = 4, callrate.cutoff = 0, hwe.cutoff = 0,
 +
                              p.value.known.variant.vec = NA, anno.known.variant.vec = NA,
 +
                              anno.candidate.variant.vec = NA)
 +
  print(res$res.out)
 +
 
 +
 
 +
          POS          REF ALT PVALUE    AF      BETA_EST  BETA_SD  DIRECTION_BY_STUDY ANNO  POS_REF_ALT_ANNO_KNOWN                                   
 +
[1,] "19:11200282" "G" "A" "0.5825"  "0.000599" "0.5616"  "1.044"    "-="              "N/A" "19:11200754/G/A/NA,19:11200806/C/T/NA,19:11200839/T/A/NA"
 +
[2,] "19:11200309" "C" "A" "0.01484" "0.01538"  "-0.3615" "0.02201"  "+="              "N/A" "19:11200754/G/A/NA,19:11200806/C/T/NA,19:11200839/T/A/NA"
 +
 
 +
 
 +
</pre>
 +
 
 +
== A Simple Tutorial for Using the conditional.rareMETALS.range==
   −
score.stat.file<- c('study1.singlevar.assoc.gz','study1.singlevar.assoc.gz');
+
example:
−
cov.file<- c('study1.singlevar.cov.gz','study1.singlevar.cov.gz');
+
 
 +
  res<-conditional.rareMETALS.range(range.name = "LDLR", score.stat.file, cov.file,
 +
                            candidate.variant.vec=c("19:11200282","19:11200309"), known.variant.vec=c("19:11200754","19:11200806","19:11200839"), test = "GRANVIL", maf.cutoff=0.05,
 +
                            alternative = c("two.sided"), ix.gold = 1,
 +
                            out.digits = 4, callrate.cutoff = 0, hwe.cutoff = 0, max.VT = NULL)
 +
  print(res$res.out)
    +
  gene.name.out p.value.out statistic.out no.site.out beta1.est.out beta1.sd.out maf.cutoff.out direction.burden.by.study.out direction.meta.single.var.out
 +
  [1,] "LDLR"        "0.01961"  "5.446"      "2"        "-0.3429"    "0.1469"    "0.05"        "-?"                          "+-"                       
 +
    top.singlevar.pos top.singlevar.refalt top.singlevar.pval top.singlevar.af pos.ref.alt.out                  pos.ref.alt.known.out                           
 +
  [1,] "19:11200309"    "C/A"                "0.01484"          "0.01538"        "19:11200282/G/A,19:11200309/C/A" "19:11200754/G/A,19:11200806/C/T,19:11200839/T/A"
   −
== A Simple Tutorial for Using the rareMETALS.gene function ==
+
More detailed results can be found in a list res$res.list
Dajiang Liu
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