gap (version 1.6)

sentinels: Sentinel identification from GWAS summary statistics

Description

Sentinel identification from GWAS summary statistics

Usage

sentinels(
  p,
  pid,
  st,
  debug = FALSE,
  flanking = 1e+06,
  chr = "Chrom",
  pos = "End",
  b = "Effect",
  se = "StdErr",
  log_p = NULL,
  snp = "MarkerName",
  sep = ","
)

Value

The function give screen output.

Arguments

p

an object containing GWAS summary statistics.

pid

a phenotype (e.g., protein) name in pGWAS.

st

row number as in p.

debug

a flag to show the actual data.

flanking

the width of flanking region.

chr

Chromosome name.

pos

Position.

b

Effect size.

se

Standard error.

log_p

log(P).

snp

Marker name.

sep

field delimitor.

Details

This function accepts an object containing GWAS summary statistics for signal identification as defined by flanking regions. As the associate P value could be extremely small, the effect size and its standard error are used.

A distance-based approach was consequently used and reframed as an algorithm here. It takes as input signals multiple correlated variants in particular region(s) which reach genomewide significance and output three types of sentinels in a region-based manner. For a given protein and a chromosome, the algorithm proceeds as follows:

Algorithm sentinels

Step 1. for a particular collection of genomewide significant variants on a chromosome, the width of the region is calculated according to the start and end chromosomal positions and if it is smaller than the flanking distance, the variant with the smallest P value is taken as sentinel (I) otherwise goes to step 2.

Step 2. The variant at step 1 is only a candidate and a flanking region is generated. If such a region contains no variant the candidate is recorded as sentinel (II) and a new iteration starts from the variant next to the flanking region.

Step 3. When the flanking is possible at step 2 but the P value is still larger than the candidate at step 2, the candidate is again recorded as sentinel (III) but next iteration starts from the variant just after the variant at the end position; otherwise the variant is updated as a new candidate where the next iteration starts.

Note Type I signals are often seen from variants in strong LD at a cis region, type II results seen when a chromosome contains two trans signals, type III results seen if there are multiple trans signals.

Typically, input to the function are variants reaching certain level of significance and the functtion identifies minimum p value at the flanking interval; in the case of another variant in the flanking window has smaller p value it will be used instead.

For now key variables in p are "MarkerName", "End", "Effect", "StdErr", "P.value", where "End" is as in a bed file indicating marker position, and the function is set up such that row names are numbered as 1:nrow(p); see example below. When log_p is specified, log(P) is used instead, which is appropriate with output from METAL with LOGPVALUE ON. In this case, the column named log(P) in the output is actually log10(P).

Examples

if (FALSE) {
 ## OPG as a positive control in our pGWAS
 require(gap.datasets)
 data(OPG)
 p <- reshape::rename(OPGtbl, c(Chromosome="Chrom", Position="End"))
 chrs <- with(p, unique(Chrom))
 for(chr in chrs)
 {
   ps <- subset(p[c("Chrom","End","MarkerName","Effect","StdErr")], Chrom==chr)
   row.names(ps) <- 1:nrow(ps)
   sentinels(ps, "OPG", 1)
 }
 subset(OPGrsid,MarkerName=="chr8:120081031_C_T")
 subset(OPGrsid,MarkerName=="chr17:26694861_A_G")
 ## log(P)
 p <- within(p, {logp <- log(P.value)})
 for(chr in chrs)
 {
   ps <- subset(p[c("Chrom","End","MarkerName","logp")], Chrom==chr)
   row.names(ps) <- 1:nrow(ps)
   sentinels(ps, "OPG", 1, log_p="logp")
 }
 ## to obtain variance explained
 tbl <- within(OPGtbl, chi2n <- (Effect/StdErr)^2/N)
 s <- with(tbl, aggregate(chi2n,list(prot),sum))
 names(s) <- c("prot", "h2")
 sd <- with(tbl, aggregate(chi2n,list(prot),sd))
 names(sd) <- c("p1","sd")
 m <- with(tbl, aggregate(chi2n,list(prot),length))
 names(m) <- c("p2","m")
 h2 <- cbind(s,sd,m)
 ord <- with(h2, order(h2))
 sink("h2.dat")
 print(h2[ord, c("prot","h2","sd","m")], row.names=FALSE)
 sink()
 png("h2.png", res=300, units="in", width=12, height=8)
 np <- nrow(h2)
 with(h2[ord,], {
     plot(h2, cex=0.4, pch=16, xaxt="n", xlab="protein", ylab=expression(h^2))
     xtick <- seq(1, np, by=1)
     axis(side=1, at=xtick, labels = FALSE)
     text(x=xtick, par("usr")[3],labels = prot, srt = 75, pos = 1, xpd = TRUE, cex=0.5)
 })
 dev.off()
 write.csv(tbl,file="INF1.csv",quote=FALSE,row.names=FALSE)
}