# A quick application of coloclization analysis

#### 2022-06-08

A disease target gene discovery framework is provided to detect susceptible genes and causal variants, which consists of two modules, including colocalization analysis and tissue propensity analysis.

## Before we start

Colocalization analysis can be performed with `xQTLbiolinks` by providing their own GWAS summary statistics data. 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)

## Step 1. data preparation.

Download and load an example file of summary statistics dataset (GRCh38). We perform colocalization analysis in `Brain - Cerebellum`.

    gwasDF <- fread("http://raw.githubusercontent.com/dingruofan/exampleData/master/gwas/AD/gwasDFsub.txt")
    tissueSiteDetail="Brain - Cerebellum"

In this example, a data.table object of 16538 (rows) x 5 (cols) is loaded. Five columns are required (arbitrary column names is supported, but columns must be as the following order):

`Col 1`. “variants” (character), , using an rsID (e.g. “rs11966562”);

`Col 2`. “chromosome” (character), one of the chromosome from chr1-chr22;

`Col 3`. “position” (integer), genome position of snp;

`Col 4`. “P-value” (numeric);

`Col 5`. “MAF” (numeric). Allel frequency;

`Col 6`. “beta” (numeric). effect size.

`Col 7`. “se” (numeric). standard error.

    head(gwasDF)

    #>          rsid chrom position   pValue     AF     beta      se
    #> 1: rs13120565     4 10702513 5.66e-10 0.6429  0.01825 0.00294
    #> 2:  rs4697781     4 10703243 8.94e-10 0.6446  0.01803 0.00294
    #> 3:  rs4697779     4 10701187 5.74e-09 0.3231 -0.01747 0.00300
    #> 4:  rs4697780     4 10701381 5.95e-09 0.3231 -0.01746 0.00300
    #> 5:  rs4547789     4 10702891 1.46e-08 0.3197 -0.01703 0.00300
    #> 6: rs11726285     4 10700944 1.47e-08 0.3214 -0.01702 0.00301

## Step 2. Seek sentinel snps.

Sentinel SNPs can be detected using `xQTLanalyze_getSentinelSnp` with the arguments `p-value < 5e-8` and `SNP-to-SNP distance > 10e6 bp`. We recommend converting the genome version of the GWAS file to GRCh38 if that is GRCh37 (run with parameter: `genomeVersion="grch37"`; `grch37To38=TRUE`, and package `rtracklayer`is required).

    sentinelSnpDF <- xQTLanalyze_getSentinelSnp(gwasDF, pValueThreshold = 5e-08)

After filtering, a sentinel SNP with P-value<5e-8 is detected in this example:

    sentinelSnpDF

    #>          rsid  chr position   pValue    maf    beta      se
    #> 1: rs13120565 chr4 10702513 5.66e-10 0.6429 0.01825 0.00294

## Step 3. 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. Every gene within 1Mb of sentinel SNPs is searched by fuction `xQTLanalyze_getTraits`. Besides, 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, 3 associations between 3 traits genes and 1 SNPs are detected

    traitsAll

    #>    chromosome geneStart  geneEnd geneStrand    geneSymbol          gencodeId
    #> 1:       chr4  11393150 11429765          -        HS3ST1  ENSG00000002587.9
    #> 2:       chr4  10486395 10684865          -          CLNK ENSG00000109684.14
    #> 3:       chr4  10068089 10073019          - RP11-448G15.3  ENSG00000261490.1
    #>          rsid position   pValue    maf
    #> 1: rs13120565 10702513 5.66e-10 0.6429
    #> 2: rs13120565 10702513 5.66e-10 0.6429
    #> 3: rs13120565 10702513 5.66e-10 0.6429

## Step 4. Conduct colocalization analysis.

For a single trait gene, like CLNK in above table, colocalization analysis (using `coloc` method) can be performed with:

    output <- xQTLanalyze_coloc(gwasDF, "CLNK", tissueSiteDetail=tissueSiteDetail) # using gene symbol

output is a list, including three parts: coloc_Out_summary, gwasEqtlInfo, and gwasEqtlInfo.

    output$coloc_Out_summary

    #>    nsnps    PP.H0.abf   PP.H1.abf    PP.H2.abf  PP.H3.abf PP.H4.abf traitGene
    #> 1:  7107 7.097893e-11 1.32221e-07 3.890211e-06 0.00625302  0.993743      CLNK
    #>    candidate_snp SNP.PP.H4
    #> 1:    rs13120565 0.5328849

For multiple trait genes, a for loop or lapply function can be used to get all genes’ outputs (using both `coloc` and `hyprcoloc` methods).

    outputs <- rbindlist(lapply( unique(traitsAll$gencodeId), function(x){ # using gencode ID.
               xQTLanalyze_coloc(gwasDF, x, tissueSiteDetail=tissueSiteDetail, method = "Both")$colocOut }))

outputs is a data.table that combined all results of coloc_Out_summary of all genes.

    outputs

    #>             traitGene nsnps    PP.H0.abf    PP.H1.abf    PP.H2.abf  PP.H3.abf
    #> 1:  ENSG00000002587.9  6452 1.730175e-05 3.218430e-02 6.603361e-05 0.12198838
    #> 2: ENSG00000109684.14  7107 7.097893e-11 1.322210e-07 3.890211e-06 0.00625302
    #> 3:  ENSG00000261490.1  6601 5.287051e-05 9.848309e-02 4.801374e-04 0.89435622
    #>     PP.H4.abf candidate_snp SNP.PP.H4 hypr_posterior hypr_regional_prob
    #> 1: 0.84574398    rs13120565 0.4140146         0.5685             0.9694
    #> 2: 0.99374296    rs13120565 0.5328849         0.9793             0.9999
    #> 3: 0.00662768    rs13120565 0.4219650         0.0000             0.0101
    #>    hypr_candidate_snp hypr_posterior_explainedBySnp
    #> 1:         rs13120565                        0.2726
    #> 2:         rs13120565                        0.4747
    #> 3:         rs13120565                        0.4118

## Step 5. Visualization of the results.

For the potential casual gene ENSG00000109684.14 (PP4=0.9937 & hypr_posterior=0.9793) with candidate SNP rs13120565 (SNP.PP.H4=0.5328849 & hypr_posterior_explainedBySnp=0.4747), We merge the variants of GWAS and eQTL by rsid.

    eqtlAsso <- xQTLdownload_eqtlAllAsso(gene="ENSG00000109684.14",
                                         tissueLabel = tissueSiteDetail)
    gwasEqtldata <- merge(gwasDF, eqtlAsso[,.(rsid=snpId, position=pos, maf, pValue)],
                          by=c("rsid", "position"), suffixes = c(".gwas",".eqtl"))

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)],
                         highlightSnp = "rs13120565", legend=FALSE)

Violin plot of normalized exprssion of eQTL (rs13120565-ENSG00000187323.11):

    xQTLvisual_eqtlExp("rs13120565", "ENSG00000109684.14", tissueSiteDetail = tissueSiteDetail)

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

    xQTLvisual_locusCombine(gwasEqtldata[,c("rsid","chrom", "position", "pValue.gwas", "pValue.eqtl")],
                            highlightSnp="rs13120565")

Colocalized loci should show a general pattern where SNPs in high LD will show strong associations with expression levels of the colocalized gene, and the eQTL associations will weaken for SNPs in lower LD. This pattern of the eQTL varies among different tissues / cell-types, the strength of which indicates the power of the regulatory effect of the variant. We can easily distinguish this patten using Tissue Propensity Analysis, and uncover the tissue / cell-type that the variant can play the most significant regulatory role in.

    propensityRes <- xQTLanalyze_propensity( gene="ENSG00000109684.14", variantName="rs13120565",
                                             tissueLabels = c("Brain - Cerebellar Hemisphere",
                                             "Brain - Cerebellum", "Treg memory", "Colon - Transverse",
                                             "CD4+ T cell", "Kidney - Cortex", "Uterus", "Esophagus - Mucosa", "LCL") )

A p-value<0.05 that calculated via permutation test indicates there exists a significant correlation between the correlation coefficient of LD and eQTL significance.

    propensityRes$tissuePropensity

    #>                     tissue_label study_accession       tissue pValue_eQTL
    #> 1: Brain - Cerebellar Hemisphere         GTEx_V8 UBER_0002037 2.34596e-06
    #> 2:                   Treg memory  Schmiedel_2018   CL_0002678 2.25235e-05
    #> 3:               Kidney - Cortex         GTEx_V8 UBER_0001225 1.36117e-01
    #> 4:                           LCL         GENCORD  EFO_0005292 5.85962e-01
    #> 5:            Esophagus - Mucosa         GTEx_V8 UBER_0006920 7.95494e-01
    #> 6:                        Uterus         GTEx_V8 UBER_0000995 9.78559e-01
    #> 7:                   CD4+ T cell       BLUEPRINT   CL_0000624 8.51989e-01
    #> 8:            Colon - Transverse         GTEx_V8 UBER_0001157 7.02369e-01
    #> 9:            Brain - Cerebellum         GTEx_V8 UBER_0002037 1.37779e-13
    #>    pValue_propensity
    #> 1:       0.000999001
    #> 2:       0.027972028
    #> 3:       0.606393606
    #> 4:       1.000000000
    #> 5:       0.950049950
    #> 6:       0.716283716
    #> 7:       0.381618382
    #> 8:       0.244755245
    #> 9:       0.000999001

using a heatmap plot to visualize the result:

    xQTLvisual_qtlPropensity(propensityRes)

For applying `xQTLbiolinks` to a whole case study, please find this Document.