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Find cis SNPs around a set of Variable Methylated Loci (VML)

Source: R/findCisSNPs.R
findCisSNPs.Rd

Identification of genotyped Single Nucleotide Polymorphisms (SNPs) close to each VML using a distance threshold.

Usage

findCisSNPs(VML_df, genotype_information, distance = 1e+06)

Arguments

VML_df

A GRanges-like data frame (i.e. the same columns as a GRanges object converted to a data frame). Must contain the following columns: "seqnames", "start", "end". These columns are present automatically when doing the object conversion and correspond to the chromosome number, and range of the region.

genotype_information

A data frame with information about genotyped sites of interest. It must contain the following columns: "CHROM" (chromosome number), "POS" (Genomic basepair position of the SNP (must be an integer), and "ID" (SNP ID). The nomenclature of CHROM must match with the one used in the VML_df seqnames column (i.e., if VML_df$seqnames uses 1, 2, 3, X, Y or Chr1, Chr2, Chr3, ChrX, ChrY, etc. as chromosome number, the genotype_information$CHROM values must be encoded in the same way).

distance

The distance threshold in basepairs to be used to identify cis SNPs. Default is 1 Mb.

Value

The same VML data frame (a data frame compatible with GRanges conversion) with the following new columns:

  • The cis SNPs identified for each VML and the number of SNPs surrounding each VML in the specified window

Details

Important: please make sure that the positions of the VML data frame and the ones in the genotype information are from the same genome build.

Examples

## Find VML in test data
VML <- RAMEN::findVML(
   methylation_data = RAMEN::test_methylation_data,
   array_manifest = "IlluminaHumanMethylationEPICv1",
   cor_threshold = 0,
   var_method = "variance",
   var_distribution = "ultrastable",
   var_threshold_percentile = 0.99,
   max_distance = 1000
   )
#> Identifying Highly Variable Probes...
#> Setting options('download.file.method.GEOquery'='auto')
#> Setting options('GEOquery.inmemory.gpl'=FALSE)
#> Identifying sparse Variable Methylated Probes
#> Identifying Variable Methylated Regions...
#> Applying correlation filter to Variable Methylated Regions...
#> Warning: executing %dopar% sequentially: no parallel backend registered
## Find cis SNPs around VML
VML_with_cis_snps <- RAMEN::findCisSNPs(
  VML_df = VML$VML,
  genotype_information = RAMEN::test_genotype_information,
  distance = 1e6
  )
#> Reminder: please make sure that the positions of the VML data frame and the ones in the genotype information are from the same genome build.