asplot: Regional association plot

In gap: Genetic Analysis Package

Regional association plot

Description

Regional association plot

Usage

asplot(
  locus,
  map,
  genes,
  flanking = 1000,
  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.

Details

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.

Author(s)

Paul de Bakker, Jing Hua Zhao, Shengxu Li

References

\insertRef

saxena07gap

Examples

## Not run: 
require(gap.datasets)
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
chr_pos <- GetSNPInfo(with(p,SNP))[c("chr","chrpos")]
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))
# We have complaints; a possibility is to get around with 
# https://ftp.ncbi.nlm.nih.gov/hapmap/
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)

gap documentation built on Aug. 26, 2023, 5:07 p.m.