# Case study: colocalization analysis in prostate cancer

#### 2022-06-08

## Before we start.

All we need to prepare include three parts:

1. GWAS summary statistics dataset.
2. Name of tissue of interest.
3. Following three installed packages.

    library(data.table)
    library(xQTLbiolinks)
    library(stringr)

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

## Step 1: Filter 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 `rtracklayer`is 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(example_Coloc_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.

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

1. Retrieved all associations from EBI eQTL catalogue for a specified gene.
2. Merge the data.frame of GWAS and eQTL by rsid.
3. 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):

    genesAll<- unique(traitsAll$gencodeId)
    colocResultAll <- data.table()
    # 162 575 error
    for(i in 1:length(genesAll)){
      colocResult <- xQTLanalyze_coloc(gwasDF,
                                       genomeVersion = "grch37", method="Both",
                                       genesAll[i], tissueSiteDetail=tissueSiteDetail)
      if(!is.null(colocResult)){ colocResultAll <- rbind(colocResultAll, colocResult$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("https://raw.githubusercontent.com/dingruofan/exampleData/master/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 (“RefSeq,” n.d.). Prostate cancer can be promoted via MMP7-induced epithelial-to-mesenchymal transition by Interleukin-17 (Zhang et al. 2017). Resent literature has shown that serum MMP7 levels could guide metastatic therapy for prostate cancer (Tregunna 2020).

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:

    library(clusterProfiler)
    library(org.Hs.eg.db)
    ego <- enrichGO(gene = as.character(outGenes$entrezGeneId),
                    OrgDb = org.Hs.eg.db,
                    ont= "BP",
                    pAdjustMethod ="none",
                    readable = TRUE)
    dotplot(ego, showCategory=15)

Viral-related GO terms are enriched in above analysis, including `"viral life cycle"`, `"positive regulation of viral life cycle"` and `"positive regulation of viral process"`. Previous studies have highlighted the role of viral infections in initiation or progression of prostate cancer. The presence of viruses such as human papillomavirus (HPV), herpesviruses including cytomegalovirus (CMV), human herpes simplex virus type 2 (HSV2), human herpesvirus type 8 (HHV8) and Epstein-Barr virus (EBV) can infect the prostate (Abidi et al. 2018). However, the causal variants’ genetic effects on the phenotype and whether the trait genes has a direct association with prostate carcinogenesis has not yet been established.

## References

Abidi, Syed Hani, Fareena Bilwani, Kulsoom Ghias, and Farhat Abbas. 2018. “Viral etiology of prostate cancer: Genetic alterations and immune response. A literature review.” International Journal of Surgery (London, England) 52 (April): 136–40. https://doi.org/10.1016/j.ijsu.2018.02.050.
“RefSeq.” n.d. https://www.ncbi.nlm.nih.gov/gene/4316#summary.
Tregunna, Rebecca. 2020. “Serum MMP7 levels could guide metastatic therapy for prostate cancer.” Nature Reviews. Urology 17 (12): 658. https://doi.org/10.1038/s41585-020-00396-3.
Zhang, Q, S Liu, K R Parajuli, W Zhang, K Zhang, Z Mo, J Liu, et al. 2017. “Interleukin-17 Promotes Prostate Cancer via Mmp7-Induced Epithelial-to-Mesenchymal Transition.” Oncogene 36 (5): 687–99. https://doi.org/10.1038/onc.2016.240.