RareMETALS

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 [1]

What is new

• 07/24/2013 Updated to version 2.6, updates include support for conditional analysis for gene-level and single variant association test. Fixed a few bugs on function for performing single variant and gene-level association test.

Where to download

The R package can be downloaded from rareMETALS_2.6.tar.gz. It will be eventually released on the Comprehensive R-archive Network. To perform gene-level association test, you will also need refFlat_hg19.txt.gz, which is the gene definition provided by refFlat.

How to install

To install the package, please use "R CMD INSTALL rareMETALS_2.4.tar.gz" command.

Supported Functionalities

• Marginal analysis of single variant or gene-level association test
• Conditional analysis of single variant or gene-level association, for variants (gene) where there are covariance information available between candidate variants and known variants.
• Estimates of genetic effects and locus genetic variance

Preparing Input Files for rareMETALS

1. Generate summary level statistic files: Summary statistics files can be generated by rvtests [2] or rare-metal-worker [3]
2. 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 [4]
3. 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:

 rareMETALS.single(score.stat.file, cov.file, range, alternative = c("two.sided", "greater", "less"), ix.gold = 1, callrate.cutoff = 0, hwe.cutoff = 0)

• Input parameters are described below:
  • 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
  • range is a tabix [5]-like range (e.g. 1:12345-23456). All variants in the specified region will be analyzed
  • 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
  • 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.
• Output is a dataframe that consist of the following fields:
  • pos: Variant physical position
  • ref: Reference allele
  • alt: Alternative allele
  • no.sample: Number of samples
  • p.value: P-values
  • statistic: Test statistic (score statistic)
  • maf: Minor allele frequency
  • beta1.est: Beta estimates
  • beta1.sd: The standard deviation for beta estimates
  • hsq.est: Locus genetic variance estimates
  • direction.by.study: Direction of genetic effects by study
  • anno: Annotation for variants

Performing gene level association test

• 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",
 "less"), alpha = 0.05, ix.gold = 1, out.digits = 4, callrate.cutoff = 0, hwe.cutoff = 0, gene.file = "refFlat_hg19.txt.gz")

• 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"
  • 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
  • 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
  • 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
  • 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.
  • 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

• Output: The output res.out consist of the following fields:
  • gene.name.out: gene names
  • p.value.out: P-value
  • statistic.out: Score statistics for meta-analysis
  • no.site.out: Number of variant sites in the gene.
  • beta1.est.out: Estimates for beta.
  • beta1.sd.out: Standard deviation for the beta estimates
  • 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
  • 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