gap (version 1.1-16)

asplot: Regional association plot

Description

This function obtains regional association plot for a particular locus, based on the information about recombinatino rates, linkage disequilibria between the SNP of interest and neighbouring ones, and single-point association tests p values.

Note that the best p value is not necessarily within locus in the original design.

Usage

asplot(locus, map, genes, flanking=1e3, best.pval=NULL, sf=c(4,4), logpmax=10, pch=21)

Arguments

locus

Data frame with columns c("CHR", "POS", "NAME", "PVAL", "RSQR") containing association results

map

Genetic map, i.e, c("POS","THETA","DIST")

genes

Gene annotation with columns c("START", "STOP", "STRAND", "GENE")

flanking

Flanking length

best.pval

Best p value for the locus of interest

sf

scale factors for p values and recombination rates, smaller values are necessary for gene dense regions

logpmax

Maximum value for -log10(p)

pch

Plotting character for the SNPs to be highlighted, e.g., 21 and 23 refer to circle and diamond

References

DGI. Whole-genome association analysis identifies novel loci for type 2 diabetes and triglyceride levels. Science 2007;316(5829):1331-6

Examples

## Not run: 
# asplot(CDKNlocus, CDKNmap, CDKNgenes)
# title("CDKN2A/CDKN2B Region")
# asplot(CDKNlocus, CDKNmap, CDKNgenes, best.pval=5.4e-8, sf=c(3,6))
# 
# ## NCBI2R
# 
# options(stringsAsFactors=FALSE)
# p <- with(CDKNlocus,data.frame(SNP=NAME,PVAL))
# hit <- subset(p,PVAL==min(PVAL,na.rm=TRUE))$SNP
# 
# library(NCBI2R)
# # LD under build 36
# pos <- apply(as.data.frame(p$SNP),1,GetSNPPosHapmap)
# chr_pos <- do.call("rbind",pos)
# l <- with(chr_pos,min(as.numeric(chrpos),na.rm=TRUE))
# u <- with(chr_pos,max(as.numeric(chrpos),na.rm=TRUE))
# LD <- with(chr_pos,GetLDInfo(unique(chr),l,u))
# hit_LD <- subset(LD,SNPA==hit)
# hit_LD <- within(hit_LD,{RSQR=r2})
# info <- GetSNPInfo(p$SNP)
# haldane <- function(x) 0.5*(1-exp(-2*x))
# locus <- with(info, data.frame(CHR=chr,POS=chrpos,NAME=marker,
#                     DIST=(chrpos-min(chrpos))/1000000,
#                     THETA=haldane((chrpos-min(chrpos))/100000000)))
# locus <- merge.data.frame(locus,hit_LD,by.x="NAME",by.y="SNPB",all=TRUE)
# locus <- merge.data.frame(locus,p,by.x="NAME",by.y="SNP",all=TRUE)
# locus <- subset(locus,!is.na(POS))
# ann <- AnnotateSNPList(p$SNP)
# genes <- with(ann,data.frame(ID=locusID,CLASS=fxn_class,PATH=pathways,
#                              START=GeneLowPoint,STOP=GeneHighPoint,
#                              STRAND=ori,GENE=genesymbol,BUILD=build,CYTO=cyto))
# attach(genes)
# ugenes <- unique(GENE)
# ustart <- as.vector(as.table(by(START,GENE,min))[ugenes])
# ustop <- as.vector(as.table(by(STOP,GENE,max))[ugenes])
# ustrand <- as.vector(as.table(by(as.character(STRAND),GENE,max))[ugenes])
# detach(genes)
# genes <- data.frame(START=ustart,STOP=ustop,STRAND=ustrand,GENE=ugenes)
# genes <- subset(genes,START!=0)
# rm(l,u,ugenes,ustart,ustop,ustrand)
# # Assume we have the latest map as in CDKNmap
# asplot(locus,CDKNmap,genes)
# ## End(Not run)