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The R package rareMETALS2 was an extension of the R package rareMETALS. It was designed to meta-analyze gene-level association tests for binary trait . While rareMETALS offers a near-complete solution for meta-analysis of gene-level tests for quantitative trait, it does not offer the optimal solution for binary trait. The package rareMETALS2 offers improved features for analyzing gene-level association tests in meta-analyses for binary trait. If you have any questions for using rareMETALS2 or rvtests, please post your questions to our google group https://groups.google.com/forum/#!forum/raremetals

The package rareMETALS2 is under development. It takes summary association statistics generated by rvtests as input. It offers the following unique features

  • 1.) It allows the meta-analysis of samples with related individuals and samples with unrelated individuals, and allows locally efficient estimate of genetic effects.
  • 2.) It allows the adjustment of covariates in meta-analysis.
  • 3.) It allows conditional meta-analysis of single variant and gene-level associations.

Change Log

June, 14, 2015 0.1 Version released


The R package can be downloaded from rareMETALS2_0.1.tar.gz. It will be eventually released on the Comprehensive R-archive Network.

How to install

To install the package, please use "R CMD INSTALL rareMETALS2_XXX.tar.gz" command, where XXX is the version number for rareMETALS2

Forum to Ask Questions

I have created a google group for discussion on the usage and for bug reports etc. 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

Meta-analysis of Single Variant Associations

 rareMETALS2.single <- function(score.stat.file,range,alternative=c('two.sided','greater','less'),ix.gold=1,callrate.cutoff=0,hwe.cutoff=0,hwe.ctrl.cutoff=0)
 * score.stat.file files of score statistics                                                                                                          
 * range tabix range of variants to be analyzed                                                                                                       
 * alternative alternative hypothesis to be specified                                                                                                 
 * ix.gold Gold standard population to align reference allele to.                                                                                     
 * callrate.cutoff Cutoffs of call rate, lower than which will NOT be analyzed (labelled as missing)                                                  
 * hwe.cutoff Cutoffs of HWE p-values; Variants with HWE p-value smaller than the cutoffs are removed from subsequent analysis and labelled as missing;                                                                                                                                                           
 * hwe.ctrl.cutoff Cutoffs of HWE p-values using controls; Variants with HWE p-value smaller than the cutoffs are removed from subsequent analysis and labelled as missing; In case control studies, it is recommended to use hwe.ctrl.cutoff, since large effect variants may violate HWE.