3. Case study: colocalization analysis in prostate cancer

Before we start.

All we need to prepare include three parts:

GWAS summary statistics dataset.
Tissue of interest.
Following three installed packages.

library(xQTLbiolinks)
library(coloc)
library(hyprcoloc)
library(data.table)
library(stringr)
library(R.utils)
library(TxDb.Hsapiens.UCSC.hg38.knownGene)
library(VariantAnnotation)

Prostate cancer is one of the most common cancers in men. Prostate cancer pathogenesis involves both heritable and environmental factors. The molecular events involved in the development or progression of prostate cancer are still unclear. In this example, we aim to figure out the causal variants and genes assocaited with prostate cancer, and to uncover potential molecular mechanisms of regulation.

For data preparation, we download summary statistics dataset of a GWAS study (GCST006085) of prostate cancer from GWAS category and load the dataset in R with data.table package. Correspondingly, we chose tissue Prostate for study. We retain the variants with dbSNP id (start with rs), and a data.table object named gwasDF of 13,498,990 (rows) x 7 (cols) is loaded.

gwasDF <- fread("29892016-GCST006085-EFO_0001663-build37.f.tsv.gz")
# extract columns.
gwasDF<- gwasDF[str_detect(variant_id, "^rs"),.(rsid=variant_id, chrom=chromosome, position= base_pair_location, pValue=p_value, AF=effect_allele_frequency, beta, se= standard_error)]
# tissue:
tissueSiteDetail="Prostate"

head(gwasDF)

#>           rsid chrom position  pValue     AF    beta     se
#> 1: rs145072688     1    10352 0.46280 0.5753 -0.0094 0.0128
#> 2: rs376342519     1    10616 0.41070 0.0066  0.0572 0.0696
#> 3: rs201725126     1    13116 0.84470 0.8255 -0.0035 0.0178
#> 4: rs200579949     1    13118 0.84470 0.8255 -0.0035 0.0178
#> 5:  rs75454623     1    14930 0.61880 0.4772 -0.0065 0.0131
#> 6: rs199856693     1    14933 0.02721 0.0407 -0.0794 0.0360

Now we can perform quality control for this GWAS dataset by estimating the genomic inflation factor, validating QQ-plot and PZ-plot using xQTLbiolinks.

xQTLvisual_qqPlot(gwasDF[,.(pValue)])
xQTLvisual_PZPlot(gwasDF[,.(pValue, beta, se)])

Left is the QQ-plot. The x-axis of the QQ-plot represents the expected p-values under the null hypothesis, while the y-axis represents the observed p-values from the GWAS summary statistics data. There is a significant deviation from the diagonal line, which indicates potential variations from the null hypothesis that may result from true associations or LD. The genomic inflation factor (lambda) is labeled in the top left of the QQ-plot, and a lambda value of 1.138 indicating no strong population stratification. Right is the PZ plot, the observed p-values on the y-axis and the corresponding p-values derived from z-scores that estimated by beta and se on the x-axis. A strong concordance can be observed between the observed p-values and those calculated from Z-scores.

Genomic annotation of the significant signals can be conducted using xQTLanno_genomic:

snpHits <- xQTLanno_genomic(gwasDF[,.(chrom=paste0("chr",chrom), position, pValue)])
xQTLvisual_anno(snpHits)

Step 1: Identify sentinel SNPs:

Sentinel SNP is the most prominent signal within a given genome range, and is usually in high LD with causal variants. By default, xQTLbiolinks detect sentinel snps that with the p-value < 5e-8 and SNP-to-SNP distance > 10e6 bp. Note: For in this example, due to the inconsistent genome version between the GWAS dataset (GRCh37) and eQTL associations (GRCh38) from eQTL category, conversion of genome version is required, and can be conducted using xQTLanalyze_getSentinelSnp with genomeVersion="grch37" and grch37To38=TRUE (package rtracklayeris required):

sentinelSnpDF <- xQTLanalyze_getSentinelSnp(gwasDF, centerRange=1e6,
                                            genomeVersion="grch37", grch37To38=TRUE)

A total of 94 sentinel SNPs are detected.

head(sentinelSnpDF)

#>           rsid  chr  position    pValue    maf    beta     se
#> 1:  rs55664108 chr1 204587862 8.572e-25 0.7157  0.0912 0.0089
#> 2:  rs35296356 chr1 150601662 3.355e-14 0.3384 -0.0690 0.0091
#> 3:  rs34579442 chr1 153927424 4.478e-14 0.6637 -0.0655 0.0087
#> 4: rs146564277 chr1 155051619 1.577e-12 0.0301 -0.1850 0.0262
#> 5:  rs34848415 chr1 205762038 2.893e-09 0.5231 -0.0501 0.0084
#> 6:  rs56391074 chr1  87745032 1.659e-08 0.6298 -0.0466 0.0082

Step 2: Identify trait genes for each sentinel SNPs:

Trait genes are genes that located in the range of 1Mb (default, can be changed with parameter detectRange) of sentinel SNPs. In order to reduce the number of trait genes and thus reduce the running time, we take the overlap of eGenes and trait genes as the final output of the function xQTLanalyze_getTraits:

traitsAll <- xQTLanalyze_getTraits(sentinelSnpDF, detectRange=1e6, tissueSiteDetail=tissueSiteDetail)

Totally, 898 associations between 835 traits genes and 92 sentinel SNPs are detected

head(traitsAll)

#>    chromosome geneStart   geneEnd geneStrand geneSymbol          gencodeId
#> 1:       chr1 205086142 205122015          -      RBBP5 ENSG00000117222.13
#> 2:       chr1 205142505 205211566          -      DSTYK ENSG00000133059.16
#> 3:       chr1 205336065 205357090          -    KLHDC8A ENSG00000162873.14
#> 4:       chr1 204828651 205022822          +      NFASC ENSG00000163531.15
#> 5:       chr1 204190341 204196486          -      KISS1  ENSG00000170498.8
#> 6:       chr1 204198160 204214092          -     GOLT1A  ENSG00000174567.7
#>          rsid  position    pValue    maf
#> 1: rs55664108 204587862 8.572e-25 0.7157
#> 2: rs55664108 204587862 8.572e-25 0.7157
#> 3: rs55664108 204587862 8.572e-25 0.7157
#> 4: rs55664108 204587862 8.572e-25 0.7157
#> 5: rs55664108 204587862 8.572e-25 0.7157
#> 6: rs55664108 204587862 8.572e-25 0.7157

Step 3: Conduct colocalization analysis with eQTL:

Following three steps of colocalization analysis are encapsulated in one function xQTLanalyze_coloc:

Retrieved all associations from EBI eQTL catalogue for a specified gene.
Merge the data.frame of GWAS and eQTL by rsid.
Perform colocalization analysis using coloc and hyprcoloc methods.

For above 835 trait genes, a for loop can be used to get these genes’ outputs of colocalization analysis (this may take several hours):

# All genes:
genesAll<- unique(traitsAll$gencodeId)
# A data.table to save results:
colocResultAll <- data.table()
for(i in 1:length(genesAll)){
  colocResult <- xQTLanalyze_coloc(gwasDF, 
                                   genomeVersion = "grch37", method="Both",
                                   genesAll[i], tissueSiteDetail=tissueSiteDetail)
  if(!is.null(colocResult)){ colocResultAll <- rbind(colocResultAll, colocResult$colocOut)}
  message(format(Sys.time(), "== %Y-%b-%d %H:%M:%S ")," == Id:",i,"/",length(genesAll)," == Gene:",genesAll[i])
}

In this case, we invoke two methods to conduct colocalization analysis, (1). coloc that estimates the posterior support of variants for each hypothesis: H0,H1,H2,H3,H4:

H0 : neither trait has a genetic association in the region
H1: only trait 1 has a genetic association in the region
H2: only trait 2 has a genetic association in the region
H3: both traits are associated, but with different causal variants
H4: both traits are associated and share a single causal variant

and (2). hyprcoloc that calculates the posterior probability that all traits share a causal variant.

Output is a data.table object that combined all results of colocOut of all genes.

head(colocResultAll)

#>             traitGene nsnps    PP.H0.abf    PP.H1.abf    PP.H2.abf  PP.H3.abf
#> 1: ENSG00000001167.14  6205 2.072318e-15 0.2592617382 5.785196e-15 0.72375223
#> 2: ENSG00000002919.14  4883 2.252628e-42 0.0004579085 4.916915e-39 0.99949789
#> 3: ENSG00000003393.14  3901 4.134900e-03 0.5826462177 2.588394e-03 0.36468313
#> 4: ENSG00000003400.14  4549 7.800693e-05 0.0111580704 6.692117e-04 0.09483042
#> 5: ENSG00000005020.12  5271 1.551832e-32 0.4907383003 1.308029e-32 0.41354432
#> 6: ENSG00000005379.15  4413 9.273629e-03 0.4357011132 1.079004e-02 0.50690919
#>       PP.H4.abf candidate_snp SNP.PP.H4 hypr_posterior hypr_regional_prob
#> 1: 1.698603e-02     rs9369290 0.2487894         0.0000             0.0079
#> 2: 4.420283e-05   rs138467395 1.0000000         0.0000             0.0174
#> 3: 4.594735e-02    rs59308963 0.1405084         0.0003             0.0126
#> 4: 8.932643e-01    rs12620010 0.2315255         0.7922             0.9931
#> 5: 9.571738e-02    rs17156041 0.6731519         0.0005             0.0373
#> 6: 3.732603e-02     rs2189681 0.3030656         0.0002             0.0129
#>    hypr_candidate_snp hypr_posterior_explainedBySnp
#> 1:          rs9369290                        0.2752
#> 2:        rs138467395                        1.0000
#> 3:         rs59308963                        0.1434
#> 4:         rs12620010                        0.3882
#> 5:         rs17156041                        0.6855
#> 6:          rs2189681                        0.1829

We can see that the two methodologies’ outcomes are quite consistent. With a threshold of 0.75 for coloc and 0.5 for hyprcoloc, a total of 24 colocalized genes are shared.

library(VennDiagram)
T <- venn.diagram( list(colocOut = colocResultAll[PP.H4.abf>=0.75]$traitGene, 
                   hyprcolocOut = colocResultAll[hypr_posterior>=0.5]$traitGene),
                   filename = NULL,lwd=1,lty=1, width = 8000, height = 10000,
                   fill=c('#ff7a45','#597ef7'), col=c('red','blue'), cat.col=c('red','blue'),
                   main.cex = 0.45, sub.cex= 0.1,
                   disable.logging = TRUE)
grid.draw(T)

To save time and go through this case as soon as possible, you can get the above result directly with:

colocResultAll <- fread("http://bioinfo.szbl.ac.cn/xQTL_biolinks/xqtl_data/colocResultAll.txt")

Step 4: Visualization of the results:

We considered colocalization tests with a posterior probability of hypothesis 4 (PPH4.ABF) > 0.75 & hypr_posterior > 0.5 as having strong or moderate evidence for colocalization.

colocResultsig <- colocResultAll[PP.H4.abf>0.75 & hypr_posterior>0.5][order(-PP.H4.abf)]

There are 24 trait genes that are associated and share a single causal variant:

head(colocResultsig)

#>             traitGene nsnps    PP.H0.abf    PP.H1.abf    PP.H2.abf   PP.H3.abf
#> 1:  ENSG00000137673.8  7108 3.722120e-16 7.637674e-04 1.159909e-15 0.001382244
#> 2: ENSG00000167641.10  5034 2.405778e-32 1.349362e-05 8.271165e-30 0.003642818
#> 3: ENSG00000184058.12  6045 1.551367e-07 1.220850e-05 1.078421e-04 0.007494261
#> 4: ENSG00000115486.11  5389 1.594413e-24 5.835862e-07 2.875803e-20 0.009535535
#> 5: ENSG00000179409.10  6154 4.387457e-18 6.631626e-03 6.233624e-18 0.008437170
#> 6:  ENSG00000277744.1  4616 1.043225e-19 1.214959e-02 6.298351e-20 0.006353677
#>    PP.H4.abf candidate_snp SNP.PP.H4 hypr_posterior hypr_regional_prob
#> 1: 0.9978540    rs11568818 0.9999999         0.9993             0.9998
#> 2: 0.9963437     rs7247241 0.2491879         0.9911             1.0000
#> 3: 0.9923855     rs2238775 0.3010829         0.9778             0.9995
#> 4: 0.9904639    rs10175792 0.2836228         0.9830             1.0000
#> 5: 0.9849312     rs2740358 0.9401973         0.9869             0.9960
#> 6: 0.9814967     rs2079811 0.9999034         0.9924             0.9943
#>    hypr_candidate_snp hypr_posterior_explainedBySnp
#> 1:         rs11568818                        1.0000
#> 2:          rs4802297                        0.2738
#> 3:          rs2238775                        0.3230
#> 4:         rs10175792                        0.3449
#> 5:          rs2740358                        0.8927
#> 6:          rs2079811                        1.0000

All these genes’ details can be fetched with xQTLquery_gene:

outGenes <- xQTLquery_gene(colocResultsig$traitGene)

Add the value of PPH4 for each gene, and remove non-protein-coding genes:

outGenes <- merge(colocResultsig[,.(gencodeId= traitGene, PP.H4.abf, candidate_snp, SNP.PP.H4, hypr_posterior)], 
                  outGenes[,.(geneSymbol, gencodeId, entrezGeneId, geneType)], by="gencodeId", sort=FALSE)
outGenes <- outGenes[geneType =="protein coding"]

outGenes

#>              gencodeId PP.H4.abf candidate_snp  SNP.PP.H4 hypr_posterior
#>  1:  ENSG00000137673.8 0.9978540    rs11568818 0.99999989         0.9993
#>  2: ENSG00000167641.10 0.9963437     rs7247241 0.24918793         0.9911
#>  3: ENSG00000184058.12 0.9923855     rs2238775 0.30108289         0.9778
#>  4: ENSG00000115486.11 0.9904639    rs10175792 0.28362278         0.9830
#>  5: ENSG00000179409.10 0.9849312     rs2740358 0.94019727         0.9869
#>  6: ENSG00000117280.12 0.9701717      rs708723 0.31910829         0.9428
#>  7: ENSG00000184012.11 0.9652282     rs6517673 0.37410718         0.9127
#>  8: ENSG00000069275.12 0.9625282      rs823154 0.29904947         0.9364
#>  9: ENSG00000099331.13 0.9607259    rs11666569 0.24161844         0.9293
#> 10: ENSG00000155749.12 0.9538274     rs7560328 0.20927651         0.8966
#> 11: ENSG00000118961.14 0.9294589     rs9306895 0.48924035         0.7300
#> 12: ENSG00000101751.10 0.9271138     rs3730783 0.08842167         0.8321
#> 13:  ENSG00000172613.7 0.9099755    rs35826789 0.69464894         0.9038
#> 14:  ENSG00000180535.3 0.8941134     rs6465657 0.15229260         0.8643
#> 15: ENSG00000003400.14 0.8932643    rs12620010 0.23152548         0.7922
#> 16: ENSG00000115648.13 0.8889021    rs72620824 0.35924147         0.7378
#> 17: ENSG00000162877.12 0.8849219     rs1772159 0.28436834         0.5931
#> 18: ENSG00000204536.13 0.8733027     rs2517985 0.12887177         0.7333
#> 19: ENSG00000136819.15 0.8365468    rs13300897 0.12522776         0.7313
#> 20: ENSG00000065060.16 0.7937810   rs112283401 0.02533245         0.6114
#> 21: ENSG00000198625.12 0.7695907     rs2169137 0.12300108         0.6086
#>              gencodeId PP.H4.abf candidate_snp  SNP.PP.H4 hypr_posterior
#>     geneSymbol entrezGeneId       geneType
#>  1:       MMP7         4316 protein coding
#>  2:   PPP1R14A        94274 protein coding
#>  3:       TBX1         6899 protein coding
#>  4:       GGCX         2677 protein coding
#>  5:     GEMIN4        50628 protein coding
#>  6:      RAB29         8934 protein coding
#>  7:    TMPRSS2         7113 protein coding
#>  8:     NUCKS1        64710 protein coding
#>  9:      MYO9B         4650 protein coding
#> 10:   ALS2CR12       130540 protein coding
#> 11:       LDAH        60526 protein coding
#> 12:       POLI        11201 protein coding
#> 13:      RAD9A         5883 protein coding
#> 14:    BHLHA15       168620 protein coding
#> 15:     CASP10          843 protein coding
#> 16:       MLPH        79083 protein coding
#> 17:     PM20D1       148811 protein coding
#> 18:     CCHCR1        54535 protein coding
#> 19:    C9orf78        51759 protein coding
#> 20:   UHRF1BP1        54887 protein coding
#> 21:       MDM4         4194 protein coding
#>     geneSymbol entrezGeneId       geneType

Ridgeline plot can be used to compare the expressions among these genes:

xQTLvisual_genesExp(outGenes$geneSymbol, tissueSiteDetail=tissueSiteDetail)

Trait gene MMP7 that with the highest PPH4.ABF=0.9978 & hypr_posterior=0.9993 encodes a member of the peptidase M10 family of matrix metalloproteinases, which is involved in the breakdown of extracellular matrix in normal physiological processes, such as embryonic development, reproduction, and tissue remodeling, as well as in disease processes, such as arthritis and metastasis. Prostate cancer can be promoted via MMP7-induced epithelial-to-mesenchymal transition by Interleukin-17. Resent literature has shown that serum MMP7 levels could guide metastatic therapy for prostate cancer.

Expression of MMP7 in multiple tissues can be plotted with xQTLvisual_geneExpTissues:

geneExpTissues <- xQTLvisual_geneExpTissues("MMP7", log10y = TRUE)

The number and significance of eQTLs in distinguished tissues are capable of showing a tissue-specific effect or a ubiquitous effect. The function xQTLvisual_eqtl can be used to indicate whether the gene is widely regulation in various tissues.

xQTLvisual_eqtl("MMP7")

Besides, we provide functions xQTLvisual_locusCompare and xQTLvisual_locusZoom to visualize the colocalization between the GWAS and the eQTL dataset for a specified gene, we take the gene MMP7 as an example:

# Download all eQTL associations of gene  MMP7 in prostate:
eqtlAsso <- xQTLdownload_eqtlAllAsso(gene="MMP7",tissueLabel = tissueSiteDetail)
# Merge the variants of GWAS and eQTL dataset by rsid:
gwasEqtldata <- merge(gwasDF[,-c("AF")], eqtlAsso[,.(rsid=snpId, pValue)],
                      by=c("rsid"), suffixes = c(".gwas",".eqtl"))

Five retained fields are required:

gwasEqtldata

#>             rsid chrom position pValue.gwas pValue.eqtl
#>    1: rs10000104  chr4 10190142      0.2830   0.4986640
#>    2: rs10000248  chr4 10720872      0.0359   0.6089460
#>    3: rs10000318  chr4 11613397      0.5810   0.3734920
#>    4: rs10000369  chr4 11234631      0.0453   0.6642180
#>    5: rs10000399  chr4 11595842      0.2370   0.0948472
#>   ---                                                  
#> 8007:  rs9999345  chr4 10895379      0.0861   0.0100352
#> 8008:  rs9999470  chr4 10315399      0.2300   0.0700527
#> 8009:  rs9999523  chr4 11410463      0.9930   0.2258890
#> 8010:  rs9999669  chr4 10852509      0.2470   0.0441530
#> 8011:  rs9999767  chr4 10127270      0.8730   0.1902930

Visualization of p-value distribution and comparison of the signals of GWAS and eQTL:

xQTLvisual_locusCompare(gwasEqtldata[,.(rsid, pValue.eqtl)], 
                        gwasEqtldata[,.(rsid, pValue.gwas)], legend_position = "bottomright")

Locuszoom plot of GWAS signals:

xQTLvisual_locusZoom(gwasEqtldata[,.(rsid, chrom, position, pValue.gwas)], legend=FALSE)

Locuszoom plot of eQTL signals:

xQTLvisual_locusZoom(gwasEqtldata[,.(rsid, chrom, position, pValue.eqtl)], legend=FALSE)

We can also combine locuscompare and locuszoom plot using function xQTLvisual_locusCombine:

xQTLvisual_locusCombine(gwasEqtldata[,c("rsid","chrom", "position", "pValue.gwas", "pValue.eqtl")])

From the above figures, we can see that the SNP rs11568818 is potential causal variant, and we can use a violin plot to show the normalized effect size of it:

xQTLvisual_eqtlExp("rs11568818", "MMP7", tissueSiteDetail = tissueSiteDetail)

Extension: Integrative analysis with external packages or tools

To gain insight into the function of trait genes with higher PPH4, and explore the potential regulatory mechanism of the prostate cancer, we can conduct exploratory analysis, like co-expression analysis, and gene ontology enrichment analyses.

First we download expression profiles of the genes with higher value of PPH4 (>0.75) & hypr_posterior (>0.5) in prostate.

expMat <- xQTLdownload_exp(outGenes$gencodeId, tissueSiteDetail=tissueSiteDetail, toSummarizedExperiment =FALSE)

Pearson coefficient can be calculated with the expression matrix for each gene:

corDT <- cor(t(expMat[,-1:-7]))
colnames(corDT) <- outGenes$geneSymbol
rownames(corDT) <- outGenes$geneSymbol

R package corrplot is used to display this correlation matrix:

library(corrplot) 
corrplot(corDT, method="color",  
         type="upper",    
         order = "hclust",
         addCoef.col = "#ff0099", 
         number.cex = 0.7)

R package clusterProfiler is used for gene functional annotation: