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An Overview of pQTLtools

Source: vignettes/pQTLtools.Rmd
pQTLtools.Rmd

The examples here are based on the SCALLOP work1.

pkgs <- c("GenomeInfoDb", "GenomicRanges", "TwoSampleMR", "biomaRt",
          "coloc", "dplyr", "gap", "ggplot2", "gwasvcf", "httr",
          "ieugwasr", "karyoploteR", "circlize", "knitr", "meta", "plotly", "pQTLdata", "pQTLtools",
          "rGREAT", "readr", "regioneR", "seqminer")
for (p in pkgs) if (length(grep(paste("^package:", p, "$", sep=""), search())) == 0) {
    if (!requireNamespace(p)) warning(paste0("This vignette needs package `", p, "'; please install"))
}
invisible(suppressMessages(lapply(pkgs, require, character.only=TRUE)))

1 Forest plots

We start with results on osteoprotegerin (OPG)2,

data(OPG,package="gap.datasets")
meta::settings.meta(method.tau="DL")
gap::METAL_forestplot(OPGtbl,OPGall,OPGrsid,width=6.75,height=5,digits.TE=2,digits.se=2,
                 col.diamond="black",col.inside="black",col.square="black")
#> Joining with `by = join_by(MarkerName)`
#> Joining with `by = join_by(MarkerName)`
Forest plots

Figure 1.1: Forest plots

Forest plots

Figure 1.2: Forest plots 

gap::METAL_forestplot(OPGtbl,OPGall,OPGrsid,package="metafor",method="FE",xlab="Effect",
                 showweights=TRUE)
#> Joining with `by = join_by(MarkerName)`
#> Joining with `by = join_by(MarkerName)`
Forest plots

Figure 1.3: Forest plots

Forest plots

Figure 1.4: Forest plots

involving both a cis and a trans pQTLs. As meta inherently includes random effects, we use a fixed effects (FE) model from metafor.

2 cis/trans classification

2.1 pQTL signals and classification table 

options(width=200)
f <- file.path(find.package("pQTLtools"),"tests","INF1.merge")
merged <- read.delim(f,as.is=TRUE)
hits <- merge(merged[c("CHR","POS","MarkerName","prot","log10p")],
              pQTLdata::inf1[c("prot","uniprot")],by="prot") %>%
        dplyr::mutate(log10p=-log10p)
names(hits) <- c("prot","Chr","bp","SNP","log10p","uniprot")
cistrans <- gap::cis.vs.trans.classification(hits,pQTLdata::inf1,"uniprot")
cis.vs.trans <- with(cistrans,data)
knitr::kable(with(cistrans,table),caption="cis/trans classification")
Table 2.1: cis/trans classification
cis trans total
ADA 1 0 1
CASP8 1 0 1
CCL11 1 4 5
CCL13 1 3 4
CCL19 1 3 4
CCL2 0 3 3
CCL20 1 1 2
CCL23 1 0 1
CCL25 1 3 4
CCL3 1 1 2
CCL4 1 1 2
CCL7 1 2 3
CCL8 1 1 2
CD244 1 2 3
CD274 1 0 1
CD40 1 0 1
CD5 1 2 3
CD6 1 1 2
CDCP1 1 2 3
CSF1 1 0 1
CST5 1 3 4
CX3CL1 1 2 3
CXCL1 1 0 1
CXCL10 1 1 2
CXCL11 1 3 4
CXCL5 1 3 4
CXCL6 1 1 2
CXCL9 1 2 3
DNER 1 0 1
EIF4EBP1 0 1 1
FGF19 0 3 3
FGF21 1 2 3
FGF23 0 2 2
FGF5 1 0 1
FLT3LG 0 6 6
GDNF 1 0 1
HGF 1 1 2
IL10 1 2 3
IL10RB 1 1 2
IL12B 1 7 8
IL15RA 1 0 1
IL17C 1 0 1
IL18 1 1 2
IL18R1 1 1 2
IL1A 0 1 1
IL6 0 1 1
IL7 1 0 1
IL8 1 0 1
KITLG 0 7 7
LIFR 0 1 1
LTA 1 2 3
MMP1 1 2 3
MMP10 1 1 2
NGF 1 1 2
NTF3 0 1 1
OSM 0 2 2
PLAU 1 5 6
S100A12 1 0 1
SIRT2 1 0 1
SLAMF1 1 4 5
SULT1A1 1 1 2
TGFA 1 0 1
TGFB1 1 0 1
TNFRSF11B 1 1 2
TNFRSF9 1 1 2
TNFSF10 1 7 8
TNFSF11 1 5 6
TNFSF12 1 4 5
TNFSF14 1 0 1
VEGFA 1 3 4
total 59 121 180 
with(cistrans,total)
#> [1] 180
T <- with(cistrans,table)
H <- T[rownames(T)!="total","total"]
merge <- merged[c("Chrom","Start","End","prot","MarkerName")]
merge_cvt <- merge(merge,cis.vs.trans,by.x=c("prot","MarkerName"),by.y=c("prot","SNP"))
ord <- with(merge_cvt,order(Chr,bp))
merge_cvt <- merge_cvt[ord,]

2.2 Genomic associations

This is visualised via a circos plot, highlighting the likely causal genes for pQTLs,

pQTLs <- transmute(merge_cvt,chr=paste0("chr",Chr),start=bp,end=bp,log10p)
cis.pQTLs <- subset(merge_cvt,cis) %>%
             dplyr::transmute(chr=paste0("chr",p.chr),start=p.start,end=p.end,gene=p.gene,cols="red")
pQTL_genes <- read.table(file.path(find.package("pQTLtools"),"tests","pQTL_genes.txt"),
                         col.names=c("chr","start","end","gene")) %>%
              dplyr::mutate(chr=gsub("hs","chr",chr)) %>%
              dplyr::left_join(cis.pQTLs) %>%
              dplyr::mutate(cols=ifelse(is.na(cols),"blue",cols))
#> Joining with `by = join_by(chr, start, end, gene)`
par(cex=0.7)
gap::circos.mhtplot2(pQTLs,pQTL_genes,ticks=0:3*10,ymax=30)
#> Warning: Some of the regions have end position values larger than the end of the chromosomes.
#> Note: 7 points are out of plotting region in sector 'chr1', track '5'.
#> Note: 3 points are out of plotting region in sector 'chr2', track '5'.
#> Note: 10 points are out of plotting region in sector 'chr3', track '5'.
#> Note: 7 points are out of plotting region in sector 'chr4', track '5'.
#> Note: 2 points are out of plotting region in sector 'chr5', track '5'.
#> Note: 4 points are out of plotting region in sector 'chr6', track '5'.
#> Note: 2 points are out of plotting region in sector 'chr8', track '5'.
#> Note: 2 points are out of plotting region in sector 'chr9', track '5'.
#> Note: 2 points are out of plotting region in sector 'chr10', track '5'.
#> Note: 4 points are out of plotting region in sector 'chr11', track '5'.
#> Note: 1 point is out of plotting region in sector 'chr12', track '5'.
#> Note: 1 point is out of plotting region in sector 'chr13', track '5'.
#> Note: 1 point is out of plotting region in sector 'chr14', track '5'.
#> Note: 2 points are out of plotting region in sector 'chr16', track '5'.
#> Note: 8 points are out of plotting region in sector 'chr17', track '5'.
#> Note: 8 points are out of plotting region in sector 'chr19', track '5'.
#> Note: 4 points are out of plotting region in sector 'chr20', track '5'.
#> Note: 1 point is out of plotting region in sector 'chr21', track '5'.
Genomic associations

Figure 2.1: Genomic associations 

par(cex=1)

where the red and blue colours indicate cis/trans classifications.

2.3 Bar chart and circos plot 

barplot(table(H),xlab="No. of pQTL regions",ylab="No. of proteins",
        ylim=c(0,25),col="darkgrey",border="black",cex=0.8,cex.axis=2,cex.names=2,las=1)
Bar chart

Figure 2.2: Bar chart 

gap::circos.cis.vs.trans.plot(hits=f,pQTLdata::inf1,"uniprot")
circos plot

Figure 2.3: circos plot

The circos plot is based on target genes (those encoding proteins) and somewhat too busy.

2.4 SH2B3

Here we focus on SH2B3.

  HOTSPOT <- "chr12:111884608_C_T"
  a <- data.frame(chr="chr12",start=111884607,end=111884608,gene="SH2B3")
  b <- dplyr::filter(cis.vs.trans,SNP==HOTSPOT) %>%
       dplyr::mutate(p.chr=paste0("chr",p.chr)) %>%
       dplyr::rename(chr=p.chr,start=p.start,end=p.end,gene=p.gene,cistrans=cis.trans)
  cols <- rep(12,nrow(b))
  cols[b[["cis"]]] <- 10
  labels <- dplyr::bind_rows(b[c("chr","start","end","gene")],a)
  circlize::circos.clear()
  circlize::circos.par(start.degree=90, track.height=0.1, cell.padding=c(0,0,0,0))
  circlize::circos.initializeWithIdeogram(species="hg19", track.height=0.05, ideogram.height=0.06)
  circlize::circos.genomicLabels(labels, labels.column=4, cex=1.1, font=3, side="inside")
  circlize::circos.genomicLink(bind_rows(a,a,a,a,a,a), b[c("chr","start","end")], col=cols,
                     directional=1, border=10, lwd=2)
*SH2B3* hotspot

Figure 2.4: SH2B3 hotspot

A more recent implementation is the qtlClassifier function. For this example we have,

options(width=200)
geneSNP <- merge(merged[c("prot","MarkerName")],pQTLdata::inf1[c("prot","gene")],by="prot")[c("gene","MarkerName","prot")]
SNPPos <- merged[c("MarkerName","CHR","POS")]
genePos <- pQTLdata::inf1[c("gene","chr","start","end")]
cvt <- gap::qtlClassifier(geneSNP,SNPPos,genePos,1e6)
knitr::kable(head(cvt))
gene MarkerName prot geneChrom geneStart geneEnd SNPChrom SNPPos Type
2 EIF4EBP1 chr4:187158034_A_G 4E.BP1 8 37887859 37917883 4 187158034 trans
3 ADA chr20:43255220_C_T ADA 20 43248163 43280874 20 43255220 cis
4 NGF chr1:115829943_A_C Beta.NGF 1 115828539 115880857 1 115829943 cis
5 NGF chr9:90362040_C_T Beta.NGF 1 115828539 115880857 9 90362040 trans
6 CASP8 chr2:202164805_C_G CASP.8 2 202098166 202152434 2 202164805 cis
7 CCL11 chr1:159175354_A_G CCL11 17 32612687 32615353 1 159175354 trans 
cistrans.check <- merge(cvt[c("gene","MarkerName","Type")],cis.vs.trans[c("p.gene","SNP","cis.trans")],
                        by.x=c("gene","MarkerName"),by.y=c("p.gene","SNP"))
with(cistrans.check,table(Type,cis.trans))
#>        cis.trans
#> Type    cis trans
#>   cis    59     0
#>   trans   0   121

2.5 pQTL-gene plot 

t2d <- qtl2dplot(cis.vs.trans,xlab="pQTL position",ylab="Gene position")
pQTL-gene plot

Figure 2.5: pQTL-gene plot

2.6 pQTL-gene plotly

The pQTL-gene plot above can be also viewed in a 2-d plotly style, fig2d.html,

fig2d <- gap::qtl2dplotly(cis.vs.trans,xlab="pQTL position",ylab="Gene position")
htmlwidgets::saveWidget(fig2d,file="fig2d.html")
print(fig2d)

and 3-d counterpart, fig3d.html,

fig3d <- gap::qtl3dplotly(cis.vs.trans,zmax=300,qtl.prefix="pQTL:",xlab="pQTL position",ylab="Gene position")
htmlwidgets::saveWidget(fig3d,file="fig3d.html")
print(fig3d)

Both plots are responsive.

2.7 Karyoplot

As biomaRt is not always on, we keep a copy of hgnc.

set_config(config(ssl_verifypeer = 0L))
mart <- biomaRt::useMart(biomart = "ensembl", dataset = "hsapiens_gene_ensembl")
attrs <- c("hgnc_symbol", "chromosome_name", "start_position", "end_position", "band")
hgnc <- vector("character",180)
for(i in 1:180)
{
  v <- with(merge_cvt[i,],paste0(Chr,":",bp,":",bp))
  g <- subset(getBM(attributes = attrs, filters="chromosomal_region", values=v, mart=mart),!is.na(hgnc_symbol))
  hgnc[i] <- paste(g[["hgnc_symbol"]],collapse=";")
  cat(i,g[["hgnc_symbol"]],hgnc[i],"\n")
}
save(hgnc,file="hgnc.rda",compress="xz")

We now proceed with

load(file.path(find.package("pQTLtools"),"tests","hgnc.rda"))
merge_cvt <- within(merge_cvt,{
  hgnc <- hgnc
  hgnc[cis] <- p.gene[cis]
})

with(merge_cvt, {
  sentinels <- regioneR::toGRanges(Chr,bp-1,bp,labels=hgnc)
  cis.regions <- regioneR::toGRanges(Chr,cis.start,cis.end)
  loci <- toGRanges(Chr,Start,End)
  colors <- c("red","blue")
  GenomeInfoDb::seqlevelsStyle(sentinels) <- "UCSC"
  kp <- karyoploteR::plotKaryotype(genome="hg19",chromosomes=levels(seqnames(sentinels)))
  karyoploteR::kpAddBaseNumbers(kp)
  karyoploteR::kpPlotRegions(kp, data=loci,r0=0.05,r1=0.15,border="black")
  karyoploteR::kpPlotMarkers(kp, data=sentinels, labels=hgnc, text.orientation="vertical",
                cex=0.5, y=0.3*seq_along(hgnc)/length(hgnc), srt=30,
                ignore.chromosome.ends=TRUE,
                adjust.label.position=TRUE, label.color=colors[2-cis], label.dist=0.002,
                cex.axis=3, cex.lab=3)
  legend("bottomright", bty="n", pch=c(19,19), col=colors, pt.cex=0.4,
         legend=c("cis", "trans"), text.col=colors, cex=0.8, horiz=FALSE)
# panel <- toGRanges(p.chr,p.start,p.end,labels=p.gene)
# kpPlotLinks(kp, data=loci, data2=panel, col=colors[2-cis])
})
#> Chromosome name styles in data ("1") and genome ("chr1") do not match.
#> They must match exactly for karyoploteR to plot anything. It seems it may be a problem with 'chr' in the names?
Karyoplot of cis/trans pQTLs

Figure 2.6: Karyoplot of cis/trans pQTLs

3 Genomic regions enrichment analysis

It is now considerably easier with Genomic Regions Enrichment of Annotations Tool (GREAT).

post <- function(regions)
{
  job <- rGREAT::submitGreatJob(get(regions), species="hg19", version="3.0.0")
  et <- rGREAT::getEnrichmentTables(job,download_by = 'tsv')
  tb <- do.call('rbind',et)
  write.table(tb,file=paste0(regions,".tsv"),quote=FALSE,row.names=FALSE,sep="\t")
  invisible(list(job=job,tb=tb))
}

M <- 1e+6
merge <- merged %>%
         dplyr::mutate(chr=Chrom, start=POS-M, end=POS+M) %>%
         dplyr::mutate(start=if_else(start<1,1,start)) %>%
         dplyr::select(prot,MarkerName,chr,start,end)
cistrans <- dplyr::select(merge, chr,start,end) %>%
            dplyr::arrange(chr,start,end) %>%
            dplyr::distinct()
# All regions
cistrans.post <- post("cistrans")
job <- with(cistrans.post,job)
rGREAT::plotRegionGeneAssociationGraphs(job)
GREAT plots

Figure 3.1: GREAT plots 

rGREAT::availableOntologies(job)
# plot of the top term
par(mfcol=c(3,1))
rGREAT::plotRegionGeneAssociationGraphs(job, ontology="GO Molecular Function")
rGREAT::plotRegionGeneAssociationGraphs(job, ontology="GO Biological Process")
GREAT plots

Figure 3.2: GREAT plots 

rGREAT::plotRegionGeneAssociationGraphs(job, ontology="GO Cellular Component")
# Specific regions
IL12B <- dplyr::filter(merge,prot=="IL.12B") %>% dplyr::select(chr,start,end)
KITLG <- dplyr::filter(merge,prot=="SCF") %>% dplyr::select(chr,start,end)
TNFSF10 <- dplyr::filter(merge,prot=="TRAIL") %>% dplyr::select(chr,start,end)
tb_all <- data.frame()
for (r in c("IL12B","KITLG","TNFSF10"))
{
  r.post <- post(r)
  tb_all <- rbind(tb_all,data.frame(gene=r,with(r.post,tb)))
}
#> Don't make too frequent requests. The time break is 60s.
#> Please wait for 55s for the next request.
#> The time break can be set by `request_interval` argument.
#> Don't make too frequent requests. The time break is 60s.
#> Please wait for 57s for the next request.
#> The time break can be set by `request_interval` argument.
#> 
#> Don't make too frequent requests. The time break is 60s.
#> Please wait for 57s for the next request.
#> The time break can be set by `request_interval` argument.

The top terms at Binomial p=1e-5 could be extracted as follows,

Table 3.1: GREAT IL12B-KITLG-TNFSF10 results
gene Ontology ID Desc BinomRank BinomP BinomBonfP BinomFdrQ RegionFoldEnrich ExpRegions ObsRegions GenomeFrac SetCov HyperRank HyperP HyperBonfP HyperFdrQ GeneFoldEnrich ExpGenes ObsGenes TotalGenes GeneSetCov TermCov Regions Genes
GO Biological Process.1100 KITLG GO Biological Process GO: 0010874 regulation of cholesterol efflux 1 0 0.001 0.001 272.460 0.011 3 0.002 0.429 1 0 0.002 0.002 265.309 0.011 3 17 0.250 0.176 /chr16:55993160-57993161,/chr7:93953894-95953895,/chr9:106661741-108661742 ABCA1,CETP,PON1
GO Biological Process.2100 KITLG GO Biological Process GO: 0032374 regulation of cholesterol transport 2 0 0.004 0.002 185.189 0.016 3 0.002 0.429 2 0 0.012 0.006 140.945 0.021 3 32 0.250 0.094 /chr16:55993160-57993161,/chr7:93953894-95953895,/chr9:106661741-108661742 ABCA1,CETP,PON1
GO Biological Process.3100 KITLG GO Biological Process GO: 0032368 regulation of lipid transport 3 0 0.094 0.031 67.046 0.045 3 0.006 0.429 3 0 0.115 0.038 66.327 0.045 3 68 0.250 0.044 /chr16:55993160-57993161,/chr7:93953894-95953895,/chr9:106661741-108661742 ABCA1,CETP,PON1
GO Biological Process.1102 TNFSF10 GO Biological Process GO: 0030195 negative regulation of blood coagulation 1 0 0.006 0.006 170.502 0.018 3 0.002 0.375 1 0 0.034 0.034 100.228 0.030 3 36 0.200 0.083 /chr17:63224774-65224775,/chr19:43153099-45153100,/chr3:185449121-187449122 APOH,KNG1,PLAUR
GO Biological Process.2102 TNFSF10 GO Biological Process GO: 0050819 negative regulation of coagulation 2 0 0.014 0.007 129.538 0.023 3 0.003 0.375 2 0 0.047 0.024 90.205 0.033 3 40 0.200 0.075 /chr17:63224774-65224775,/chr19:43153099-45153100,/chr3:185449121-187449122 APOH,KNG1,PLAUR
GO Biological Process.3102 TNFSF10 GO Biological Process GO: 0072376 protein activation cascade 3 0 0.046 0.015 87.207 0.034 3 0.004 0.375 3 0 0.196 0.065 56.378 0.053 3 64 0.200 0.047 /chr17:63224774-65224775,/chr1:195710915-197710916,/chr3:185449121-187449122 APOH,CFH,KNG1
GO Cellular Component.119 TNFSF10 GO Cellular Component GO: 0005615 extracellular space 1 0 0.008 0.008 9.342 0.642 6 0.080 0.750 1 0 0.000 0.000 10.934 0.732 8 880 0.533 0.009 /chr14:93844946-95844947,/chr17:63224774-65224775,/chr18:28804862-30804863,/chr1:195710915-197710916,/chr3:171274231-173274232,/chr3:185449121-187449122 APOH,CFH,CFHR3,KNG1,MEP1B,SERPINA1,SERPINA6,TNFSF10

4 eQTL Catalog for colocalization analysis

See example associated with import_eQTLCatalogue(). A related function is import_OpenGWAS() used to fetch data from OpenGWAS. The cis-pQTLs and 1e+6 flanking regions were considered and data are actually fetched from files stored locally. Only the first sentinel was used (r=1).

liftRegion <- function(x,chain,flanking=1e6)
{
  require(GenomicRanges)
  gr <- with(x,GenomicRanges::GRanges(seqnames=chr,IRanges::IRanges(start,end))+flanking)
  GenomeInfoDb::seqlevelsStyle(gr) <- "UCSC"
  gr38 <- rtracklayer::liftOver(gr, chain)
  chr <- gsub("chr","",colnames(table(seqnames(gr38))))
  start <- min(unlist(start(gr38)))
  end <- max(unlist(end(gr38)))
  invisible(list(chr=chr[1],start=start,end=end,region=paste0(chr[1],":",start,"-",end)))
}

sumstats <- function(prot,chr,region37)
{
  cat("GWAS sumstats\n")
  vcf <- file.path(INF,"METAL/gwas2vcf",paste0(prot,".vcf.gz"))
  gwas_stats <- gwasvcf::query_gwas(vcf, chrompos = region37) %>%
                gwasvcf::vcf_to_granges() %>%
                GenomeInfoDb::keepSeqlevels(chr) %>%
                GenomeInfoDb::renameSeqlevels(paste0("chr",chr))
  gwas_stats_hg38 <- rtracklayer::liftOver(gwas_stats, chain) %>%
    unlist() %>%
    dplyr::as_tibble() %>%
    dplyr::transmute(chromosome = seqnames,
                     position = start, REF, ALT, AF, ES, SE, LP, SS) %>%
    dplyr::mutate(id = paste(chromosome, position, sep = ":")) %>%
    dplyr::mutate(MAF = pmin(AF, 1-AF)) %>%
    dplyr::group_by(id) %>%
    dplyr::mutate(row_count = n()) %>%
    dplyr::ungroup() %>%
    dplyr::filter(row_count == 1) %>%
    dplyr::mutate(chromosome=gsub("chr","",chromosome))
  s <- ggplot2::ggplot(gwas_stats_hg38, aes(x = position, y = LP)) +
       ggplot2::theme_bw() +
       ggplot2::geom_point() +
       ggplot2::ggtitle(with(sentinel,paste0(prot,"-",SNP," association plot")))
  s
  gwas_stats_hg38
}

gtex <- function(gwas_stats_hg38,ensGene,region38)
{
  cat("c. GTEx_v8 imported eQTL datasets\n")
  fp <- file.path(find.package("pQTLtools"),"eQTL-Catalogue","tabix_ftp_paths_gtex.tsv")
  web <- read.delim(fp, stringsAsFactors = FALSE) %>% dplyr::as_tibble()
  local <- within(web %>% dplyr::as_tibble(),
        {
          f <- lapply(strsplit(ftp_path,"/imported/|/ge/"),"[",3);
          ftp_path <- paste0("~/rds/public_databases/GTEx/csv/",f)
        })
  gtex_df <- dplyr::filter(local, quant_method == "ge") %>%
             dplyr::mutate(qtl_id = paste(study, qtl_group, sep = "_"))
  ftp_path_list <- setNames(as.list(gtex_df$ftp_path), gtex_df$qtl_id)
  hdr <- file.path(find.package("pQTLtools"),"eQTL-Catalogue","column_names.GTEx")
  column_names <- names(read.delim(hdr))
  safe_import <- purrr::safely(import_eQTLCatalogue)
  summary_list <- purrr::map(ftp_path_list,
                             ~safe_import(., region38, selected_gene_id = ensGene, column_names))
  result_list <- purrr::map(summary_list, ~.$result)
  result_list <- result_list[!unlist(purrr::map(result_list, is.null))]
  result_filtered <- purrr::map(result_list[lapply(result_list,nrow)!=0],
                                ~dplyr::filter(., !is.na(se)))
  purrr::map_df(result_filtered, ~run_coloc(., gwas_stats_hg38), .id = "qtl_id")
}

gtex_coloc <- function(prot,chr,ensGene,chain,region37,region38,out)
{
  gwas_stats_hg38 <- sumstats(prot,chr,region37)
  df_gtex <- gtex(gwas_stats_hg38,ensGene,region38)
  if (!exists("df_gtex")) return
  saveRDS(df_gtex,file=paste0(out,".RDS"))
  dplyr::arrange(df_gtex, -PP.H4.abf)
  p <- ggplot2::ggplot(df_gtex, aes(x = PP.H4.abf)) +
       ggplot2::theme_bw() +
       geom_histogram() +
       ggtitle(with(sentinel,paste0(prot,"-",SNP," PP4 histogram"))) +
       xlab("PP4") + ylab("Frequency")
  p
}

single_run <- function()
{
  chr <- with(sentinel,Chr)
  ss <- subset(inf1,prot==sentinel[["prot"]])
  ensRegion37 <- with(ss,
                      {
                        start <- start-M
                        if (start<0) start <- 0
                        end <- end+M
                        paste0(chr,":",start,"-",end)
                      })
  ensGene <- ss[["ensembl_gene_id"]]
  ensRegion38 <- with(liftRegion(ss,chain),region)
  cat(chr,ensGene,ensRegion37,ensRegion38,"\n")
  f <- with(sentinel,paste0(prot,"-",SNP))
  gtex_coloc(sentinel[["prot"]],chr,ensGene,chain,ensRegion37,ensRegion38,f)
}

options(width=200)
HOME <- Sys.getenv("HOME")
HPC_WORK <- Sys.getenv("HPC_WORK")
INF <- Sys.getenv("INF")
M <- 1e6
sentinels <- subset(cis.vs.trans,cis)
f <- file.path(find.package("pQTLtools"),"eQTL-Catalogue","hg19ToHg38.over.chain")
chain <- rtracklayer::import.chain(f)
gwasvcf::set_bcftools(file.path(HPC_WORK,"bin","bcftools"))

r <- 1
sentinel <- sentinels[r,]
single_run()
ktitle <- with(sentinel,paste0("Colocalization results for ",prot,"-",SNP))

The results are also loadable as follows.

coloc_df <- readRDS(file.path(find.package("pQTLtools"),"tests","OPG-rs2247769.RDS")) %>%
            dplyr::rename(Tissue=qtl_id, H0=PP.H0.abf,H1=PP.H1.abf,
                          H2=PP.H2.abf,H3=PP.H3.abf,H4=PP.H4.abf) %>%
            mutate(Tissue=gsub("GTEx_V8_","",Tissue),
                   H0=round(H0,2),H1=round(H1,2),H2=round(H2,2),H3=round(H3,2),H4=round(H4,2)) %>%
                   dplyr::arrange(-H4)
knitr::kable(coloc_df,caption="Colocalization results for OPG-chr8:120081031_C_T")
Table 4.1: Colocalization results for OPG-chr8:120081031_C_T
Tissue nsnps H0 H1 H2 H3 H4
Adrenal_Gland 6741 0 0 0.46 0.37 0.18
Brain_Hippocampus 6733 0 0 0.54 0.36 0.10
Brain_Amygdala 6701 0 0 0.55 0.37 0.08
Brain_Cerebellum 6738 0 0 0.53 0.39 0.08
Small_Intestine_Terminal_Ileum 6738 0 0 0.56 0.36 0.08
Artery_Tibial 6742 0 0 0.59 0.34 0.07
Brain_Nucleus_accumbens_basal_ganglia 6741 0 0 0.55 0.39 0.06
Liver 6742 0 0 0.46 0.48 0.06
Minor_Salivary_Gland 6721 0 0 0.57 0.38 0.06
Brain_Cortex 6741 0 0 0.47 0.47 0.05
Brain_Frontal_Cortex_BA9 6736 0 0 0.47 0.48 0.05
Brain_Hypothalamus 6737 0 0 0.50 0.46 0.05
Brain_Spinal_cord_cervical_c-1 6725 0 0 0.56 0.39 0.05
Spleen 6740 0 0 0.53 0.42 0.05
Artery_Coronary 6740 0 0 0.48 0.49 0.04
Brain_Anterior_cingulate_cortex_BA24 6728 0 0 0.52 0.44 0.04
Brain_Cerebellar_Hemisphere 6738 0 0 0.53 0.42 0.04
Brain_Putamen_basal_ganglia 6732 0 0 0.57 0.39 0.04
Brain_Substantia_nigra 6706 0 0 0.57 0.39 0.04
Colon_Sigmoid 6742 0 0 0.52 0.44 0.04
Kidney_Cortex 6625 0 0 0.53 0.43 0.04
Ovary 6739 0 0 0.54 0.42 0.04
Pituitary 6742 0 0 0.60 0.36 0.04
Stomach 6742 0 0 0.57 0.39 0.04
Uterus 6735 0 0 0.56 0.40 0.04
Vagina 6719 0 0 0.59 0.37 0.04
Artery_Aorta 6742 0 0 0.59 0.39 0.03
Brain_Caudate_basal_ganglia 6741 0 0 0.56 0.41 0.03
Breast_Mammary_Tissue 6742 0 0 0.54 0.43 0.03
Cells_EBV-transformed_lymphocytes 6730 0 0 0.47 0.50 0.03
Esophagus_Gastroesophageal_Junction 6742 0 0 0.57 0.40 0.03
Prostate 6742 0 0 0.51 0.46 0.03
Testis 6742 0 0 0.61 0.36 0.03
Esophagus_Mucosa 6742 0 0 0.36 0.62 0.02
Muscle_Skeletal 6742 0 0 0.60 0.37 0.02
Skin_Not_Sun_Exposed_Suprapubic 6742 0 0 0.55 0.43 0.02
Skin_Sun_Exposed_Lower_leg 6742 0 0 0.42 0.56 0.02
Esophagus_Muscularis 6742 0 0 0.01 0.98 0.01
Nerve_Tibial 6742 0 0 0.19 0.81 0.01
Thyroid 6742 0 0 0.15 0.84 0.01
Adipose_Subcutaneous 6742 0 0 0.00 1.00 0.00
Adipose_Visceral_Omentum 6742 0 0 0.09 0.91 0.00
Cells_Cultured_fibroblasts 6742 0 0 0.00 1.00 0.00
Colon_Transverse 6742 0 0 0.01 0.99 0.00
Heart_Atrial_Appendage 6742 0 0 0.00 1.00 0.00
Heart_Left_Ventricle 6742 0 0 0.02 0.97 0.00
Lung 6742 0 0 0.01 0.99 0.00
Pancreas 6742 0 0 0.02 0.98 0.00

The function sumstats() obtained meta-analysis summary statistics (in build 37 and therefore lifted over to build 38) to be used in colocalization analysis. The output are saved in the .RDS files. Note that ftp_path changes from eQTL Catalog to local files.

5 Mendelian Randomisation (MR)

5.1 pQTL-based MR

The function pqtlMR() has an attractive feature that multiple pQTLs can be used together for conducting MR with a list of outcomes from MR-Base, e.g., outcome <- extract_outcome_data(snps=with(exposure,SNP),outcomes=c("ieu-a-7","ebi-a-GCST007432")). For generic applications, the run_TwoSampleMR() function can be used.

f <- file.path(system.file(package="pQTLtools"),"tests","Ins.csv")
exposure <- TwoSampleMR::format_data(read.csv(f))
caption4 <- "IL6R variant and diseases"
knitr::kable(exposure, caption=paste(caption4,"(instruments)"),digits=3)
Table 5.1: IL6R variant and diseases (instruments)
SNP effect_allele.exposure other_allele.exposure eaf.exposure beta.exposure se.exposure pval.exposure exposure mr_keep.exposure pval_origin.exposure id.exposure
rs2228145 A C 0.613 -0.168 0.012 0 IL.6 TRUE reported 68ekAE 
f <- file.path(system.file(package="pQTLtools"),"tests","Out.csv")
outcome <- TwoSampleMR::format_data(read.csv(f),type="outcome")
pqtlMR(exposure, outcome, prefix="IL6R-")
#> Harmonising IL.6 (68ekAE) and Atopic dermatitis (oJAAxF)
#> Harmonising IL.6 (68ekAE) and Rheumatoid arthritis (y59FBK)
#> Harmonising IL.6 (68ekAE) and Coronary artery disease (ZoHewj)
#> Analysing '68ekAE' on 'oJAAxF'
#> Analysing '68ekAE' on 'y59FBK'
#> Analysing '68ekAE' on 'ZoHewj'
result <- read.delim("IL6R-result.txt") %>%
          dplyr::select(-id.exposure,-id.outcome)
knitr::kable(result,caption=paste(caption4, "(result)"),digits=3)
Table 5.1: IL6R variant and diseases (result)
outcome exposure method nsnp b se pval
Atopic dermatitis IL.6 Wald ratio 1 -0.454 0.102 0
Rheumatoid arthritis IL.6 Wald ratio 1 -0.457 0.084 0
Coronary artery disease IL.6 Wald ratio 1 -0.231 0.031 0 
single <- read.delim("IL6R-single.txt") %>%
          dplyr::select(-id.exposure,-id.outcome,-samplesize)
knitr::kable(subset(single,!grepl("All",SNP)), caption=paste(caption4, "(single)"),digits=3)
Table 5.1: IL6R variant and diseases (single)
exposure outcome SNP b se p
1 IL.6 Atopic dermatitis rs2228145 -0.454 0.1019 8.57e-06
4 IL.6 Rheumatoid arthritis rs2228145 -0.457 0.0842 5.72e-08
7 IL.6 Coronary artery disease rs2228145 -0.231 0.0309 7.39e-14

We carry on producing a forest plot.

IL6R <- single %>%
        dplyr::filter(grepl("^rs222",SNP)) %>%
        dplyr::select(outcome,b,se) %>%
        setNames(c("outcome","Effect","StdErr")) %>%
        dplyr::mutate(outcome=gsub("\\b(^[a-z])","\\U\\1",outcome,perl=TRUE),
               Effect=as.numeric(Effect),StdErr=as.numeric(StdErr))
gap::mr_forestplot(IL6R,colgap.forest.left="0.05cm", fontsize=14,
                   leftcols=c("studlab"), leftlabs=c("Outcome"),
                   plotwidth="3.5inch", sm="OR",
                   rightcols=c("effect","ci","pval"), rightlabs=c("OR","95%CI","P"),
                   digits=2, digits.pval=2, scientific.pval=TRUE,
                   common=FALSE, random=FALSE, print.I2=FALSE, print.pval.Q=FALSE, print.tau2=FALSE,
                   addrow=TRUE, backtransf=TRUE, at=c(1:3)*0.5, spacing=1.5, xlim=c(0.5,1.5))
pQTL-MR

Figure 5.1: pQTL-MR 

invisible(sapply(c("harmonise","result","single"),
                 function(x) unlink(paste0("IL6R-",x,".txt"))))

5.2 Two-sample MR

The documentation example is quoted here,

outcomes <- "ebi-a-GCST007432"
prot <- "MMP.10"
type <- "cis"
f <- paste0(prot,"-",type,".mrx")
d <- read.table(file.path(system.file(package="pQTLtools"),"tests",f),
                header=TRUE)
exposure <- TwoSampleMR::format_data(within(d,{P=10^logP}), phenotype_col="prot", snp_col="rsid",
                        chr_col="Chromosome", pos_col="Posistion",
                        effect_allele_col="Allele1", other_allele_col="Allele2",
                        eaf_col="Freq1", beta_col="Effect", se_col="StdErr",
                        pval_col="P", log_pval=FALSE,
                        samplesize_col="N")
clump <- exposure[sample(1:nrow(exposure),nrow(exposure)/80),] # TwoSampleMR::clump_data(exposure)
outcome <- TwoSampleMR::extract_outcome_data(snps=clump$SNP,outcomes=outcomes)
#> Extracting data for 13 SNP(s) from 1 GWAS(s)
harmonise <- TwoSampleMR::harmonise_data(clump,outcome)
#> Harmonising MMP.10 (jrjVHU) and FEV1 || id:ebi-a-GCST007432 (ebi-a-GCST007432)
prefix <- paste(outcomes,prot,type,sep="-")
run_TwoSampleMR(harmonise, mr_plot="pQTLtools", prefix=prefix)
#> Analysing 'jrjVHU' on 'ebi-a-GCST007432'
Two-sample MR

Figure 5.2: Two-sample MR

Two-sample MR

Figure 5.3: Two-sample MR

Two-sample MR

Figure 5.4: Two-sample MR

Two-sample MR

Figure 5.5: Two-sample MR

At time of running (12/4/2024), clump_data fails to run so 1.25% variants is used for illustrative purpose.

The output is contained in individual .txt files, together with the scatter, forest, funnel and leave-one-out plots.

Table 5.2: MMP.10 variants and FEV1 (result)
id.exposure id.outcome outcome exposure method nsnp b se pval
jrjVHU ebi-a-GCST007432 FEV1 || id:ebi-a-GCST007432 MMP.10 MR Egger 13 0.018 0.014 0.210
jrjVHU ebi-a-GCST007432 FEV1 || id:ebi-a-GCST007432 MMP.10 Weighted median 13 0.001 0.007 0.940
jrjVHU ebi-a-GCST007432 FEV1 || id:ebi-a-GCST007432 MMP.10 Inverse variance weighted 13 -0.004 0.005 0.497
jrjVHU ebi-a-GCST007432 FEV1 || id:ebi-a-GCST007432 MMP.10 Simple mode 13 -0.001 0.013 0.952
jrjVHU ebi-a-GCST007432 FEV1 || id:ebi-a-GCST007432 MMP.10 Weighted mode 13 0.000 0.009 0.984
Table 5.2: MMP.10 variants and FEV1 (heterogeneity)
id.exposure id.outcome outcome exposure method Q Q_df Q_pval
jrjVHU ebi-a-GCST007432 FEV1 || id:ebi-a-GCST007432 MMP.10 MR Egger 8.8 11 0.641
jrjVHU ebi-a-GCST007432 FEV1 || id:ebi-a-GCST007432 MMP.10 Inverse variance weighted 11.8 12 0.463
Table 5.2: MMP.10 variants and FEV1 (pleiotropy)
id.exposure id.outcome outcome exposure egger_intercept se pval
jrjVHU ebi-a-GCST007432 FEV1 || id:ebi-a-GCST007432 MMP.10 -0.004 0.002 0.112
Table 5.2: MMP.10 variants and FEV1 (single)
exposure outcome id.exposure id.outcome samplesize SNP b se p
MMP.10 FEV1 || id:ebi-a-GCST007432 jrjVHU ebi-a-GCST007432 321047 rs11605152 -0.002 0.012 0.836
MMP.10 FEV1 || id:ebi-a-GCST007432 jrjVHU ebi-a-GCST007432 321047 rs12365082 0.005 0.011 0.617
MMP.10 FEV1 || id:ebi-a-GCST007432 jrjVHU ebi-a-GCST007432 321047 rs1276270 -0.019 0.018 0.287
MMP.10 FEV1 || id:ebi-a-GCST007432 jrjVHU ebi-a-GCST007432 321047 rs17099555 0.020 0.033 0.548
MMP.10 FEV1 || id:ebi-a-GCST007432 jrjVHU ebi-a-GCST007432 321047 rs17880553 0.003 0.032 0.929
MMP.10 FEV1 || id:ebi-a-GCST007432 jrjVHU ebi-a-GCST007432 321047 rs1835493 -0.038 0.034 0.262
MMP.10 FEV1 || id:ebi-a-GCST007432 jrjVHU ebi-a-GCST007432 321047 rs2846341 -0.013 0.016 0.407
MMP.10 FEV1 || id:ebi-a-GCST007432 jrjVHU ebi-a-GCST007432 321047 rs41380244 0.006 0.036 0.864
MMP.10 FEV1 || id:ebi-a-GCST007432 jrjVHU ebi-a-GCST007432 321047 rs510347 -0.061 0.033 0.068
MMP.10 FEV1 || id:ebi-a-GCST007432 jrjVHU ebi-a-GCST007432 321047 rs61895694 0.021 0.014 0.143
MMP.10 FEV1 || id:ebi-a-GCST007432 jrjVHU ebi-a-GCST007432 321047 rs72977504 -0.013 0.037 0.732
MMP.10 FEV1 || id:ebi-a-GCST007432 jrjVHU ebi-a-GCST007432 321047 rs75093003 -0.039 0.033 0.230
MMP.10 FEV1 || id:ebi-a-GCST007432 jrjVHU ebi-a-GCST007432 321047 rs79991976 -0.040 0.033 0.230
MMP.10 FEV1 || id:ebi-a-GCST007432 jrjVHU ebi-a-GCST007432 321047 All - Inverse variance weighted -0.004 0.005 0.497
MMP.10 FEV1 || id:ebi-a-GCST007432 jrjVHU ebi-a-GCST007432 321047 All - MR Egger 0.018 0.014 0.210
Table 5.2: MMP.10 variants and FEV1 (loo)
exposure outcome id.exposure id.outcome samplesize SNP b se p
MMP.10 FEV1 || id:ebi-a-GCST007432 jrjVHU ebi-a-GCST007432 321047 rs11605152 -0.004 0.006 0.525
MMP.10 FEV1 || id:ebi-a-GCST007432 jrjVHU ebi-a-GCST007432 321047 rs12365082 -0.007 0.006 0.286
MMP.10 FEV1 || id:ebi-a-GCST007432 jrjVHU ebi-a-GCST007432 321047 rs1276270 -0.002 0.006 0.707
MMP.10 FEV1 || id:ebi-a-GCST007432 jrjVHU ebi-a-GCST007432 321047 rs17099555 -0.004 0.005 0.437
MMP.10 FEV1 || id:ebi-a-GCST007432 jrjVHU ebi-a-GCST007432 321047 rs17880553 -0.004 0.006 0.496
MMP.10 FEV1 || id:ebi-a-GCST007432 jrjVHU ebi-a-GCST007432 321047 rs1835493 -0.003 0.005 0.610
MMP.10 FEV1 || id:ebi-a-GCST007432 jrjVHU ebi-a-GCST007432 321047 rs2846341 -0.002 0.006 0.680
MMP.10 FEV1 || id:ebi-a-GCST007432 jrjVHU ebi-a-GCST007432 321047 rs41380244 -0.004 0.006 0.490
MMP.10 FEV1 || id:ebi-a-GCST007432 jrjVHU ebi-a-GCST007432 321047 rs510347 -0.002 0.005 0.696
MMP.10 FEV1 || id:ebi-a-GCST007432 jrjVHU ebi-a-GCST007432 321047 rs61895694 -0.008 0.006 0.187
MMP.10 FEV1 || id:ebi-a-GCST007432 jrjVHU ebi-a-GCST007432 321047 rs72977504 -0.003 0.006 0.538
MMP.10 FEV1 || id:ebi-a-GCST007432 jrjVHU ebi-a-GCST007432 321047 rs75093003 -0.003 0.005 0.624
MMP.10 FEV1 || id:ebi-a-GCST007432 jrjVHU ebi-a-GCST007432 321047 rs79991976 -0.003 0.005 0.623
MMP.10 FEV1 || id:ebi-a-GCST007432 jrjVHU ebi-a-GCST007432 321047 All -0.004 0.005 0.497

5.3 MR using cis, trans and cis+trans (pan) instruments

This is illustrated with IL-12B.

efo <- read.delim(file.path(find.package("pQTLtools"),"tests","efo.txt"))
d3 <- read.delim(file.path(find.package("pQTLtools"),"tests","IL.12B.txt")) %>%
      dplyr::mutate(MRBASEID=unlist(lapply(strsplit(outcome,"id:"),"[",2)),y=b) %>%
      dplyr::left_join(efo) %>%
      dplyr::mutate(trait=gsub("\\b(^[a-z])","\\U\\1",trait,perl=TRUE)) %>%
      dplyr::select(-outcome,-method) %>%
      dplyr::arrange(cistrans,desc(trait))
#> Joining with `by = join_by(MRBASEID)`
knitr::kable(dplyr::select(d3,MRBASEID,trait,cistrans,nsnp,b,se,pval) %>%
             dplyr::group_by(cistrans),
             caption="MR with IL-12B variants",digits=3)
Table 5.3: MR with IL-12B variants
MRBASEID trait cistrans nsnp b se pval
ukb-a-115 Vitiligo cis 54 0.000 0.000 0.410
ukb-b-8814 Urinary tract infection cis 18 -0.001 0.000 0.279
ukb-b-19386 Ulcerative colitis cis 7 0.001 0.000 0.039
ukb-b-15622 Tuberculosis cis 7 0.000 0.000 0.668
ebi-a-GCST003156 Systemic lupus erythematosus cis 39 -0.096 0.068 0.157
finn-a-M13_SJOGREN Sjogren syndrome cis 29 -0.248 0.141 0.080
ieu-b-69 Sepsis cis 56 -0.033 0.027 0.209
ieu-a-1112 Sclerosing cholangitis cis 32 0.052 0.068 0.446
finn-a-D3_SARCOIDOSIS Sarcoidosis cis 29 -0.060 0.115 0.601
ukb-b-9125 Rheumatoid arthritis cis 17 0.000 0.001 0.845
ukb-b-10537 Psoriasis cis 17 -0.004 0.001 0.000
ebi-a-GCST005581 Primary biliary cirrhosis cis 2 0.006 0.163 0.972
ukb-b-15606 Pneumonia cis 7 0.000 0.000 0.376
ieu-b-18 Multiple sclerosis cis 26 -0.026 0.043 0.548
finn-a-MENINGITIS Meningitis infection cis 29 -0.217 0.191 0.257
ebi-a-GCST005528 Juvenile idiopathic arthritis cis 1 -0.167 0.114 0.140
ieu-a-31 Inflammatory bowel disease cis 56 0.390 0.035 0.000
ieu-a-1081 IGA glomerulonephritis cis 3 0.210 0.177 0.237
ukb-b-19732 Hypothyroidism cis 37 0.000 0.001 0.952
finn-a-AB1_HIV HIV infection cis 29 -0.129 0.280 0.645
bbj-a-123 Graves disease cis 21 0.008 0.101 0.938
ukb-b-13251 Gout cis 20 0.000 0.000 0.764
ukb-a-552 Crohn’s disease cis 54 0.001 0.000 0.000
ieu-a-276 Celiac disease cis 3 -0.017 0.121 0.887
ukb-b-20141 Atopic eczema cis 26 0.000 0.001 0.988
ukb-b-20208 Asthma cis 7 0.000 0.000 0.756
ukb-b-18194 Ankylosing spondylitis cis 5 0.000 0.000 0.710
ukb-b-16702 Allergy cis 7 0.000 0.000 0.265
ukb-b-16499 Allergic rhinitis cis 40 0.000 0.001 0.788
ukb-a-115 Vitiligo pan 15 0.000 0.000 0.214
ukb-b-8814 Urinary tract infection pan 12 0.000 0.000 0.474
ukb-b-19386 Ulcerative colitis pan 10 0.000 0.000 0.266
ukb-b-15622 Tuberculosis pan 11 0.000 0.000 0.481
ebi-a-GCST003156 Systemic lupus erythematosus pan 13 0.146 0.206 0.480
finn-a-M13_SJOGREN Sjogren syndrome pan 8 -0.010 0.245 0.969
ieu-b-69 Sepsis pan 16 -0.012 0.029 0.694
ieu-a-1112 Sclerosing cholangitis pan 10 0.406 0.454 0.371
finn-a-D3_SARCOIDOSIS Sarcoidosis pan 8 0.088 0.195 0.653
ukb-b-9125 Rheumatoid arthritis pan 12 0.000 0.001 0.818
ukb-b-10537 Psoriasis pan 12 0.000 0.003 0.900
ebi-a-GCST005581 Primary biliary cirrhosis pan 5 0.015 0.161 0.927
ukb-b-15606 Pneumonia pan 10 0.000 0.000 0.677
ieu-b-18 Multiple sclerosis pan 11 0.019 0.069 0.789
finn-a-MENINGITIS Meningitis infection pan 8 -0.233 0.205 0.254
ebi-a-GCST005528 Juvenile idiopathic arthritis pan 4 -0.068 0.304 0.822
ieu-a-31 Inflammatory bowel disease pan 15 0.274 0.067 0.000
ieu-a-1081 IGA glomerulonephritis pan 4 0.228 0.190 0.230
ukb-b-19732 Hypothyroidism pan 14 0.001 0.006 0.806
finn-a-AB1_HIV HIV infection pan 8 -0.060 0.270 0.825
bbj-a-123 Graves disease pan 13 -0.293 0.154 0.057
ukb-b-13251 Gout pan 13 0.000 0.001 0.787
ukb-a-552 Crohn’s disease pan 15 0.000 0.000 0.219
ieu-a-276 Celiac disease pan 4 0.100 0.232 0.665
ukb-b-20141 Atopic eczema pan 13 -0.001 0.001 0.341
ukb-b-20208 Asthma pan 10 0.000 0.000 0.226
ukb-b-18194 Ankylosing spondylitis pan 8 0.000 0.001 0.916
ukb-b-16702 Allergy pan 10 0.000 0.000 0.032
ukb-b-16499 Allergic rhinitis pan 14 0.000 0.002 0.962
ukb-a-115 Vitiligo trans 11 0.000 0.000 0.306
ukb-b-8814 Urinary tract infection trans 8 0.000 0.001 0.925
ukb-b-19386 Ulcerative colitis trans 7 0.000 0.001 0.861
ukb-b-15622 Tuberculosis trans 8 0.000 0.000 0.366
ebi-a-GCST003156 Systemic lupus erythematosus trans 10 0.839 0.368 0.023
finn-a-M13_SJOGREN Sjogren syndrome trans 7 0.707 0.436 0.104
ieu-b-69 Sepsis trans 12 0.000 0.052 0.997
ieu-a-1112 Sclerosing cholangitis trans 8 1.374 0.903 0.128
finn-a-D3_SARCOIDOSIS Sarcoidosis trans 7 0.512 0.392 0.191
ukb-b-9125 Rheumatoid arthritis trans 8 0.001 0.001 0.600
ukb-b-10537 Psoriasis trans 8 0.006 0.006 0.347
ebi-a-GCST005581 Primary biliary cirrhosis trans 4 0.085 0.326 0.795
ukb-b-15606 Pneumonia trans 7 0.000 0.000 0.649
ieu-b-18 Multiple sclerosis trans 7 0.130 0.117 0.265
finn-a-MENINGITIS Meningitis infection trans 7 -0.500 0.446 0.262
ebi-a-GCST005528 Juvenile idiopathic arthritis trans 3 0.636 0.918 0.488
ieu-a-31 Inflammatory bowel disease trans 11 0.061 0.075 0.419
ieu-a-1081 IGA glomerulonephritis trans 2 0.451 1.010 0.656
ukb-b-19732 Hypothyroidism trans 10 0.004 0.012 0.716
finn-a-AB1_HIV HIV infection trans 7 0.001 0.608 0.998
bbj-a-123 Graves disease trans 10 -0.628 0.224 0.005
ukb-b-13251 Gout trans 9 0.001 0.001 0.557
ukb-a-552 Crohn’s disease trans 11 0.000 0.000 0.316
ieu-a-276 Celiac disease trans 2 1.210 0.469 0.010
ukb-b-20141 Atopic eczema trans 9 -0.002 0.002 0.222
ukb-b-20208 Asthma trans 7 0.001 0.001 0.136
ukb-b-18194 Ankylosing spondylitis trans 6 0.000 0.001 0.650
ukb-b-16702 Allergy trans 7 0.000 0.000 0.276
ukb-b-16499 Allergic rhinitis trans 10 0.000 0.004 0.923 
p <- ggplot2::ggplot(d3,aes(y = trait, x = y))+
     ggplot2::theme_bw()+
     ggplot2::geom_point()+
     ggplot2::facet_wrap(~cistrans,ncol=3,scales="free_x")+
     ggplot2::geom_segment(ggplot2::aes(x = b-1.96*se, xend = b+1.96*se, yend = trait))+
     ggplot2::geom_vline(lty=2, ggplot2::aes(xintercept=0), colour = 'red')+
     ggplot2::xlab("Effect size")+
     ggplot2::ylab("")
p
MR with cis, trans and cis+trans variants of IL-12B

Figure 5.6: MR with cis, trans and cis+trans variants of IL-12B

6 Literature on pQTLs

References3 4 are included as EndNote libraries which is now part of pQTLdata package.

7 UniProt IDs

The function uniprot2ids converts UniProt IDs to others.

References

1.
The SCALLOP consortium. Mapping pQTLs of circulating inflammatory proteins identifies drivers of immune-related disease risk and novel therapeutic targets. medRxiv 2023.03.24.23287680 (2023) doi:10.1101/2023.03.24.23287680.
2.
Kwan, J. S. H. et al. Meta-analysis of genome-wide association studies identifies two loci associated with circulating osteoprotegerin levels. Human Molecular Genetics 23, 6684–6693 (2014).
3.
Sun, B. B. et al. Genomic atlas of the human plasma proteome. Nature 558, 73–79 (2018).
4.
Suhre, K., McCarthy, M. I. & Schwenk, J. M. Genetics meets proteomics: Perspectives for large population-based studies. Nat Rev Genet (2020) doi:10.1038/s41576-020-0268-2.