# ldblock package: linkage disequilibrium data structures

Vincent J. Carey, stvjc at channing.harvard.edu

#### February 2015

# Contents

# 1 Introduction

There is a nice vignette in snpStats concerning linkage disequilibrium (LD) analysis as supported by software in that package. The purpose of this package is to simplify handling of existing population-level data on LD for the purpose of flexibly defining LD blocks.

# 2 Import of HapMap LD data

The `hmld` function imports gzipped tabular data from hapmap’s repository .

    suppressPackageStartupMessages({
     library(ldblock)
     library(GenomeInfoDb)
    })
    path = dir(system.file("hapmap", package="ldblock"), full=TRUE)
    ceu17 = hmld(path, poptag="CEU", chrom="chr17")

    ## importing F:/biocbuild/bbs-3.19-bioc/tmpdir/Rtmp0sEow1/Rinst3f1867a22221/ldblock/hapmap/ld_chr17_CEU.txt.gz

    ## done.

    ceu17

    ## ldstruct for population CEU, chrom chr17
    ## dimensions: 36621 x 36621 ; statistic is Dprime
    ## object structure:
    ##       ldmat       chrom      genome      allpos      poptag   statInUse
    ## "dsCMatrix" "character" "character"   "numeric" "character" "character"
    ##       allrs
    ## "character"

# 3 A view of the block structure

For some reason knitr/render will not display this image nicely.

    library(Matrix)

    ##
    ## Attaching package: 'Matrix'

    ## The following object is masked from 'package:S4Vectors':
    ##
    ##     expand

    image(ceu17@ldmat[1:400,1:400],
       col.reg=heat.colors(120), colorkey=TRUE, useRaster=TRUE)

This ignores physical distance and MAF. The bright stripes are probably due to SNP with low MAF.

# 4 Collecting SNPs exhibiting linkage to selected SNP

We’ll use `ceu17` and the `gwascat` package to enumerate SNP that are in LD with GWAS hits.

    library(gwascat)

    ## gwascat loaded.  Use makeCurrentGwascat() to extract current image.

    ##  from EBI.  The data folder of this package has some legacy extracts.

    load(system.file("legacy/ebicat37.rda", package="gwascat"))
    #seqlevelsStyle(ebicat37) = "NCBI"  # noop?
    seqlevels(ebicat37) = gsub("chr", "", seqlevels(ebicat37))
    e17 = ebicat37[ which(as.character(seqnames(ebicat37)) == "17") ]

Some dbSNP names for GWAS hits on chr17 are

    rsh17 = unique(e17$SNPS)
    head(rsh17)

    ## [1] "rs11078895" "rs11891"    "rs7501939"  "rs9905704"  "rs4796793"
    ## [6] "rs78378222"

We will use `expandSnpSet` to obtain names for SNP that were found in HapMap CEU to have which \(D' > .9\) with any of these hits. These names are added to the input set.

    length(rsh17)

    ## [1] 530

    exset = ldblock::expandSnpSet( rsh17, ldstruct= ceu17, lb=.9 )

    ## Warning in ldblock::expandSnpSet(rsh17, ldstruct = ceu17, lb = 0.9): dropping
    ## 149 rsn not matched in ld matrix

    length(exset)

    ## [1] 13209

    all(rsh17 %in% exset)

    ## [1] TRUE

Not all GWAS SNP are in the HapMap LD resource. You can use your own LD data as long as the format agrees with that of the HapMap distribution.