eQTpLot is an intuitive and user-friendly R package developed for the visualization of colocalization between eQTL and GWAS data. eQTpLot takes as input standard GWAS and eQTL summary statistics, and optional pairwise LD information, to generate a series of plots visualizing colocalization, correlation, and enrichment between eQTL and GWAS signals for a given gene-trait pair. With eQTpLot, investigators can easily generate a series of customizable plots clearly illustrating, for a given gene-trait pair:
These clear and comprehensive plots provide a unique view of eQTL-GWAS colocalization, allowing for a more complete understanding of the interaction between gene expression and trait associations. eQTpLot was developed in R version
4.0.0 and depends on a number of packages for various aspects of its implementation
c("biomaRt", "dplyr", "GenomicRanges", "ggnewscale", "ggplot2", "ggplotify", "ggpubr", "gridExtra", "Gviz", "LDheatmap", "patchwork")
eQTpLot can be install using
devtools, either directly from GitHub,
or by downloading the repository to your computer, unzipping, and installing the
*Note: For issues installing dependencies, try running the following code prior to installation.
At a minimum, eQTpLot requires two input data frames:
Two optional data frames may also be supplied:
The formatting parameters of all both required and both optional input files are summarized below.
GWAS.df is a data frame of GWAS summary data with one row per SNP, ex. PLINK
.assoc.linear, .assoc.logistic format, containing the following columns:
CHR|Chromosome for SNP (sex chromosomes coded numerically). Data type: integer
POS|Chromosomal position for each SNP, in base pairs. Data type: integer
SNP|Variant ID (such as dbSNP ID "rs...". Note: Must be the same naming scheme as used in
eQTL.df to ensure proper matching). Data type: character
P|p-value for the SNP from GWAS analysis. Data type: numeric
BETA|beta for the SNP from GWAS analysis. Data type: numeric
PHE|OPTIONAL Name of the phenotype for which the GWAS data refers, useful if your
GWAS.df contains data for multiple phenotypes, i.e. PheWAS. If not provided, eQTpLot will assume the GWAS data is for a single phenotype, specified with the
trait argument. Data type: character
> data(GWAS.df.example) > head(GWAS.df.example) CHR BP SNP P BETA PHE 1 11 66078129 rs1625595 0.06646 -7.925e-05 Creatinine 2 11 66078252 rs565374903 0.17350 -1.915e-02 Creatinine 3 11 66078296 rs750544051 0.03073 -4.299e-02 Creatinine 4 11 66078347 11:66078347_C_G 0.64030 -9.298e-03 Creatinine 5 11 66078368 rs541384459 0.93890 5.763e-04 Creatinine 6 11 66078385 rs138591375 0.34690 1.647e-03 Creatinine
eQTL.df is a data frame of eQTL data, one row per SNP, ex. downloaded directly from the GTEx Portal in .csv format, containing the following columns:
SNP.Id|Variant ID Note: naming scheme must be the same as what is used in the
GWAS.df to ensure proper matching. Data type: character
Gene.Symbol|Gene symbol to which the eQTL expression data refers Note: gene symbol must match entries in
Genes.df to ensure proper matching. Data type: character
P.value|P-value for the SNP from eQTL analysis Data type: numeric
NES|Normalized effect size for the SNP from eQTL analysis (Per GTEx, defined as the slope of the linear regression, and is computed as the effect of the alternative allele relative to the reference allele in the human genome reference. Data type: numeric
Tissue|Tissue type to which the eQTL p-value/NES refer Note: eQTL.df can contain multiple tissue types. Data type: character
N|OPTIONAL Number of samples used to calculate the p-value and NES for the eQTL data, used if performing a MultiTissue or PanTissue analysis with the option CollapseMethod set to "meta" for a simple sample size weighted meta-analysis. Data type: character
> data(eQTL.df.example) > head(eQTL.df.example) Gene.Symbol SNP.Id P.Value NES Tissue 1 PELI3 rs138677235 0.0377103 -0.139874 Adipose_Subcutaneous 2 PELI3 rs111472085 0.0131649 0.257579 Adipose_Subcutaneous 3 PELI3 rs75325358 0.0442168 -0.147111 Adipose_Subcutaneous 4 PELI3 rs113298476 0.0442168 -0.147111 Adipose_Subcutaneous 5 PELI3 rs73490435 0.0134318 0.256645 Adipose_Subcutaneous 6 PELI3 rs112219657 0.0387010 0.214056 Adipose_Subcutaneous
Genes.df is an optional data frame, one row per gene, which should contain the following columns:
Note: eQTpLot automatically loads a default
Genes.df containing information for most protein-coding genes for genome builds hg19 and hg38, but you may wish to specify our own
Genes.df data frame if your gene of interest is not included in the default data frame, or if your eQTL data uses a different gene naming scheme (for example, Gencode ID instead of gene symbol)
Gene|Gene symbol/name for which the Coordinate data refers to Note: gene symbol/name must match entries in
eQTL.df to ensure proper matching. Data type: character
CHR|Chromosome the gene is on Note: do not include a "car" prefix, and sex chromosomes should be coded numerically. Data type: integer
Start|Chromosomal coordinate of start position (in basepairs) to use for gene Note: this should be the smaller of the two values between
Stop. Data type: integer
Stop|Chromosomal coordinate of end position (in basepairs) to use for gene Note: this should be the larger of the two values between
Stop. Data type: integer
Build|The genome build for the coordinate data -- the default
Genes.df dataframe contains entries for both genome builds for each gene, and the script will select the appropriate entry based on the specified
gbuild (default is
"hg19")). Data type: character,
> data(Genes.df.example) > head(Genes.df.example) CHR Start Stop Gene Build 1 19 58858171 58864865 A1BG hg19 2 10 52559168 52645435 A1CF hg19 3 12 9220303 9268825 A2M hg19 4 12 8975067 9039798 A2ML1 hg19 5 1 33772366 33786699 A3GALT2 hg19 6 22 43088117 43117307 A4GALT hg19
LD.df is an optional data frame of SNP linkage data, one row per SNP pair, compatible with PLINK .ld (--r/--r2) file format https://www.cog-genomics.org/plink/1.9/formats#ld
Note: If no
LD.df is supplied, eQTpLot will plot data without LD information
BP_A|Base pair position of the first variant in the LD pair. Data type: integer
SNP_A|Variant ID of the first variant in the LD pair. Data type: character
BP_B|Base pair position of the second variant in the LD pair. Data type:integer
SNP_B|Variant ID of the second variant in the LD pair. Data type: character
R2|Squared correlation measure of linkage between the two variants. Data type: numeric
> data(LD.df.example) > head(LD.df.example) CHR_A BP_A SNP_A CHR_B BP_B SNP_B R2 1 11 66078129 rs1625595 11 66079275 11:66079275_GA_G 0.299550 2 11 66078129 rs1625595 11 66079361 rs33981819 0.686453 3 11 66078129 rs1625595 11 66079786 rs490972 0.991748 4 11 66078129 rs1625595 11 66079787 rs565972 0.991756 5 11 66078129 rs1625595 11 66079818 rs61891388 0.706614 6 11 66078129 rs1625595 11 66080770 rs7924580 0.309860
Note: variants in SNP_A and SNP_B must also appear in the
GWAS.df SNP column to be used for LD analysis.
eQTL.df|a data frame of eQTL summary statistic data, as defined above
GWAS.df|a data frame of GWAS summary statistic data, as defined above
gbuild|Default: “hg19”. The genome build to use for fetching genomic information for the genome track (panel B). This build should match the genome build used for chromosomal positions in
GWAS.df. Currently compatibile with hg19 and hg38.
gene| gene name or symbol Note: gene name must match an entry in
Genes.df for the specified gbuild
sigpvalue_eQTL|Default: 0.05. Significance threshold for eQTL data (variants with eQTL p-value >
sigpvalue_eQTL are excluded from analysis)
sigpvalue_GWAS|Default: 5e-8. Significance threshold for GWAS data (corresponds to the horizontal line in plot A and GWAS significance thresholds for the eQTL enrichment plot).
tissue|Default: “all”. Single tissue or list of tissue names to filter
eQTL.df by. If this parameter is set to “all”, eQTpLot will pick the smallest eQTL p-value for each SNP across all tissues for a PanTissue analysis (described in more detail below). Note: the tissue name must match at least one entry in the
trait|name of the GWAS phenotype to analyze. If no
PHE column is present in
GWAS.df, this argument will be used as the name for the analyzed phenotype. If
GWAS.df contains multiple phenotypes in the
PHE column, this parameter will be used to filter in
GWAS.df for only this phenotype.
Genes.df|A data frame of gene coordinates, as defined above
LD.df|A data frame of pairwise linkage data, as defined above
congruence|Default: FALSE. If TRUE, variants with congruent and incongruent effects will be plotted separately, as described below.
genometrackheight|Default: 2. Relative height of the genome track panel (B). Gene-dense regions may require more plotting space, whereas gene-sparse regions may look better with less plotting space.
getplot|Default: TRUE. Displays the generated plot in the viewport.
LDcolor|Default: “color”. Determines the color palette used in the LDheatmap panel if
LD.df is supplied. Options are
c("color", "black") for viridis and grayscales, respectively.
LDmin|Default: 10. Only variants in LD, i.e. R2 >
R2min, with at least this many other variants will be plotted in the LDheatmap panel if
LD.df is supplied. This parameter can be used to thin the number of variants being plotted in the LDheatmap. Warning: setting
R2min too low when dealing with high density data may cause performance issues; eQTpLot will give a warning if attempting to plot > 1,000 SNPs
leadSNP|Name of the lead SNP to use for plotting LD information in the P-P plots, if
LD.df is supplied. The name of the variant must be present in both
NESeQTLRange| a list of maximum and minimum limits
c(min,max), to display for the
NES value in
eQTL.df. The default setting will adjust the size scale automatically to fit the displayed data, whereas specifying the limits will keep them consistent between plots.
R2min|Only used if
LD.df is supplied. Default: 0.1. The threshold for R2 to use when selecting LD data from
LD.df. Variant pairs with R2 <
R2min will not be included in the analysis. Warning: setting
R2min too low when dealing with high density data may cause performance issues; eQTpLot will give a warning if attempting to plot > 1,000 SNPs
range|Default: 200. The range, in kB, to extend the analysis window on either side of the gene of interest, as defined by the
Stop points for the specified
res|Default: 300. The resolution, in dpi, for the output plot image
saveplot|Default: TRUE. Saves the generated plot in the working directory with the name "
tissue.Congreunce_Info.LD_Info.eQTpLot.png", using the provided arguments
wi|Default: 12 if
LD.df is not supplied, 14 if
LD.df is supplied. The width of the output plot image, in inches. The height of the plot is calculated from this argument as well to maintain the appropriate aspect ratio.
xlimd|sets the x-axis upper limit for the P-P plot
ylima|sets the y-axis upper limit in plot A
ylimd|sets the y-axis upper limit for the P-P plot
CollapsMethod|Default: “min”. the method used to collapse eQTL p-values and NES across tissues if a MultiTissue or PanTissue analysis is specified. If set to "min”, the p-value and NES from the tissue with the smallest p-value for each variant will be selected. If set to "median" or "mean" the median or mean p-value and NES for each variant, across all specified tissues, will be selected. If set to “meta” eQTpLot will perform a simple sample-size-weighted meta-analysis of the p-values across all specified tissues. If "meta" is specified,
eQTL.df should include a column with header "N" indicating the number of samples used to derive the given eQTL data. If no column N is present, eQTpLot will give the user the option to complete a meta-analysis assuming equal sample sizes for all tissues, which may lead to inaccurate results. Additionally. if "meta" is specified, no meta-analyzed NES will be computed and all variants will be displayed as the same size in the main eQTpLot figure.
Gene.List| Default: FALSE. If set to TRUE, outputs the Pearson correlation between eQTL and GWAS p-values for a given tissue across a user-supplied list of genes, ordered by significance. No plots will be generated. If the user sets the parameter tissue to “all,” or to a list of tissues, eQTpLot will collapse the eQTL data for these tissues by variant, using the method specified by the parameter
CollapseMethod. This may be a useful parameter to obtain a very simple bird’s-eye view of the genes at a locus whose expression is most closely correlated to a relevant GWAS signal for a given trait.
Tissue.List|Default: FALSE. If TRUE, this parameter will output the Pearson correlation between eQTL and GWAS p-values for a given gene across a user-supplied list of tissues, ordered by significance. No plots will be generated. If the user sets the parameter tissue to “all,” eQTpLot will consider each tissue included in
eQTL.df. This may be a useful parameter to obtain a very simple bird’s-eye view of the tissues in which a given gene’s expression is most closely tied to a relevant GWAS signal for a given trait.
In its simplest implementation, eQTplot takes as input two data frames, one of GWAS summary data and the other of eQTL summary data, with the user specifying the name of the gene to be analyzed, the GWAS trait to be analyzed (useful if the GWAS data contains information on multiple associations, as one might obtain from a Phenome-wide Association Study (PheWAS)), and the tissue type to use for the eQTL analysis. Using these inputs, eQTpLot generates a series of plots intuitively illustrating the colocalization of GWAS and eQTL signals in chromosomal space, and the enrichment of and correlation between the candidate gene eQTLs and trait-significant variants. Additional parameters and data can be supplied, such as pairwise variant LD information, allowing for an even more comprehensive visualization of the interaction between eQTL and GWAS data within a given genomic locus.
One major implementation feature of eQTpLot is the option to divide eQTL/GWAS variants into two groups based on their directions of effect. If the argument
congruence is set to TRUE, all variants are divided into two groups: congruous, or those with the same direction of effect on gene expression and the GWAS trait (e.g., a variant that is associated with increased expression of the candidate gene and an increase in the GWAS trait), and incongruous, or those with opposite directions of effect on gene expression and the GWAS trait (e.g., a variant that is associated with increased expression of the candidate gene but a decrease in the GWAS trait). The division between congruous and incongruous variants provides a more nuanced view of the relationship between gene expression level and GWAS associations – a variant associated with increased expression of a candidate gene and an increase in a given GWAS trait would seem to be operating through different mechanisms that a variant that is similarly associated with increased expression of the same candidate gene, but a decrease in the same GWAS trait. eQTpLot intuitively visualizes these differences as described below. This distinction also serves to illuminate important underlying biologic difference between different gene-trait pairs, discriminating between genes that appear to suppress a particular phenotype and those that appear to promote it.
In some instances, it may be of interest to visualize a variant’s effect on candidate gene expression across multiple tissue types, or even across all tissues. Such analyses can be accomplished by setting the argument
tissue to a list of tissues contained within
eQTL.df (e.g. c(“Adipose_Subcutaneous”, “Adipose_Visceral”)) for a MultiTissue analysis, or by setting the argument
tissue to “all” for a PanTissue analysis. In a PanTissue analysis, eQTL data across all tissues contained in
eQTL.df will be collapsed, by variant, into a single pan-tissue eQTL; a similar approach is used in a MultiTissue analysis, but in this case eQTL data will be collapsed, by variant, across only the specified tissues. The method by which eQTpLot collapses eQTL data can be specified with the argument
CollapseMethod, which accepts as input one of four options – “min,” “median,” “mean,” or “meta.” By setting
CollapseMethod to “min” (the default), for each variant the tissue with the smallest eQTL p-value will be selected, such that each variant’s most significant eQTL effect, agnostic of tissue, can be visualized. Setting the parameter to “median” or “mean” will visualize the median or mean p-value and NES value for each SNP across all specified tissues. Lastly, setting
CollapseMethod to “meta” will perform a simple sample-size-weighted meta-analysis (i.e. a weighted Z-test) for each variant across all specified tissues, visualizing the resultant p-value for each variant. It should be noted that this meta-analysis method requires a sample size for each eQTL entry in
eQTL.df, which should be supplied in an optional column “N.” If sample size numbers are not readily available (as may be the case if directly downloading cis-eQTL data from the GTEx portal), eQTpLot gives the user the option to presume that all eQTL data is derived from identical sample sizes across all tissues – this approach may of course yield inaccurate estimates of a variant’s effect in meta-analysis, but may be useful to the user.
To generate the main eQTL-GWAS Colocalization Plot (Figures 1A, 2A, 3A, 4A), a locus of interest (LOI) is defined to include the target gene’s chromosomal coordinates (as listed in
Genes.df, for the indicated
gbuild, for the user-specified
gene), along with a range of flanking genome (specified with the argument
range, with a default value of 200 kilobases on either side of the gene). GWAS summary statistics from
GWAS.df are filtered to include only variants that fall within the LOI. The variants are then plotted in chromosomal space along the horizontal axis, with the inverse log of the p-value of association with the specified GWAS trait (PGWAS) plotted along the vertical axis, as one would plot a standard GWAS Manhattan plot. The GWAS significance threshold,
sigpvalue_GWAS (default value 5e-8), is depicted with a red horizontal line.
Within this plot, variants that lack eQTL data for the target gene in
eQTL.df (or for which the eQTL p-value (PeQTL) does not meet the specified significance threshold,
sigpvalue_eQTL (default value 0.05)) are plotted as grey squares. On the other hand, variants that act as eQTLs for the target gene (withPeQTL <
sigpvalue_eQTL) are plotted as colored triangles, with a color gradient corresponding to the inverse magnitude ofPeQTL. As noted above, an analysis can be specified to differentiate between variants with congruous versus incongruous effects on the GWAS trait and candidate gene expression levels – if this is the case, variants with congruous effects will be plotted using a blue color scale, while variants with incongruous effects will be plotted using a red color scale (as seen in Figure 4A).The size of each triangle corresponds to the eQTL normalized effect size (NES) for each variant, while the directionality of each triangle is set to correspond to the direction of effect for the variant on the GWAS trait.
A depiction of the genomic positions of all genes within the LOI is generated below the plot using the package Gviz (Figures 1B, 2B, 3B, 4B). If LD data is supplied, in the form of
LD.df, a third panel illustrating the LD landscape of eQTL variants within the LOI is generated using the package LDheatmap (Figure 3C, 4C). To generate this panel,
LD.df is filtered to contain only eQTL variants that appear in the plotted LOI, and to include only variant pairs that are in LD with each other with R2 >
R2min (default value of 0.1). This dataset is further filtered to include only variants that are in LD (with R2 >
R2min) with at least a certain number of other variants (user-defined with the argument
LDmin, default value of 10). These filtering steps are useful in paring down the number of variants to be plotted in the LDheatmap, keeping the most informative variants and reducing the time needed to generate the eQTpLot. A heatmap illustrating the pairwise linkage disequilibrium of the final filtered variant set is subsequently generated below the main eQTL-GWAS Colocalization Plot, with a fill scale corresponding to R2 for each variant pair. The location of each variant in chromosomal space is indicated at the top of the heatmap, using the same chromosomal coordinates as displayed in panels A and B.
For variants within the LOI with PGWAS less than the specified GWAS significance threshold,
sigpvalue_GWAS, the proportion that are also eQTLs for the gene of interest (with PeQTL <
sigpvalue_eQTL) are calculated and plotted, and the same is done for variants withPGWAS >
sigpvalue_GWAS, (Figure 1C, 2C, 3D, 4D). Enrichment of candidate gene eQTLs among GWAS-significant variants is determined by Fisher’s exact test. If an analysis differentiating between congruous and incongruous variants is specified, these are considered separately in the analysis (as seen in figure 4D).
To visualize correlation between PGWAS and PeQTL, each variant within the LOI is plotted withPeQTL along the horizontal axis, and PGWAS along the vertical axis. Correlation between the two probabilities is visualized by plotting a best-fit linear regression over the points. The Pearson correlation coefficient and p-value of correlation are computed and displayed on the plot as well (Figure 1D, 2D). If an analysis differentiating between congruous and incongruous variants is specified, separate plots are made for each set of variants and superimposed over each other as a single plot, with linear regression lines/Pearson coefficients displayed for both sets.
If LD data is supplied in the form of
LD.df, a similar plot is generated, but the fill color of each point is set to correspond to the LD R2 value for each variant with a specified lead variant, plotted as a green diamond (Figure 3E). This lead variant can be user-specified with the argument
leadSNP or is otherwise automatically defined as the upper-right-most variant in the P-P plot. This same lead variant is also labelled in the main eQTpLot panel A (Figure 3A). In the case where LD data is provided and an analysis differentiating between congruous and incongruous variants is specified, two separate plots are generated: one for congruous and one for incongruous variants (Figure 4E-F). In each plot, the fill color of each point is set to correspond to the LD R2 value for each variant with the lead variant for that specific plot (again defined as the upper-right most variant of the P-P plot), with both the congruous and incongruous lead variants labelled in the main eQTpLot panel A (Figure 4A).
In this example, a GWAS study of LDL cholesterol levels has identified a significant association with a genomic locus at chr11:66,196,265-66,338,300 (build hg19), which contains a number of plausible candidate genes, including the genes BBS1 and ACTN3.
GeneList function of eQTpLot, the user supplies both the BBS1 and ACTN3 genes to eQTpLot, along with all required input data, to obtain a crude estimation of which gene’s eQTL data most closely correlates with the GWAS signal observed at this locus.
Calling eQTpLot as follows generates Pearson correlation statistics between PGWAS and PeQTL for both genes and the LDL trait, using a PanTissue approach and collapsing by method “min” as described above.
> eQTpLot(GWAS.df = GWAS.df.example, eQTL.df = eQTL.df.example, gene = c("BBS1", "ACTN3"), gbuild = "hg19", trait = "LDL", tissue = "all", CollapseMethod = "min", GeneList = T)  "Checking input data..."  "PHE column found in GWAS.df. Analyzing data for phenotype LDL"  "PanTissue eQTL analysis, collapsing by method min will be completed across all tissues in eQTL.df"  "For genes:"  "'BBS1', 'ACTN3'"  "eQTL analysis for gene BBS1: Pearson correlation: 0.823, p-value: 1.62e-127"  "eQTL analysis for gene ACTN3: Pearson correlation: 0.245, p-value: 1.52e-07"  "Complete"
Demonstrating that there is significantly stronger correlation between the GWAS signal at this locus and eQTLs for the gene BBS1, compared to the gene ACTN3. To visualize these differences, starting with the gene BBS1, eQTpLot can be called as follows:
``` eQTpLot(GWAS.df = GWAS.df.example, eQTL.df = eQTL.df.example, gene = "BBS1", gbuild = "hg19", trait = "LDL", tissue = "all", CollapseMethod = "min")
This command will analyze the GWAS data in `GWAS.df.example` within a default 200kb range surrounding the *BBS1* gene, using the preloaded `Genes.df` to define the genomic boundaries of *BBS1* based on genome build hg19. eQTL data from `eQTL.df.example` will be filtered to contain only data pertaining to *BBS1*. Since `tissue` is set to “all”, eQTpLot will perform a PanTissue analysis, as described above. This generates the following plot: #### Figure 1 !(man/figures/Figure1.jpeg)<!-- --> Figure 1 illustrates clear evidence of colocalization between the LDL-significant locus and *BBS1* eQTLs. In Figure 1A, it is easy to see that all variants significantly associated with LDL cholesterol (those plotted above the horizontal red line) are also very significantly associated with *BBS1* expression levels, as indicated by their coloration in bright orange. Figure 1C shows that there is a significant enrichment (p = 9.5e-46 by Fisher’s exact test) for *BBS1* eQTLs among GWAS-significant variants (as shown on the plot). Lastly, Figure 1D illustrates strong evidence for correlation between P<sub>GWAS</sub> and P<sub>eQTL</sub> for the analyzed variants, with a Pearson correlation coefficient of 0.823 and a p-value of correlation of 1.62e-127 (as displayed on the plot). Taken together, this analysis provides strong evidence for colocalization between variants associated with LDL cholesterol levels, and variants associated with *BBS1* expression levels. To investigate the possibility that the LDL association signal might also be acting through modulation of the expression of other genes at this locus, the same analysis can be performed, substituting the gene *ACTN3* for the gene *BBS1*, as in the following command:
eQTpLot(GWAS.df = GWAS.df.example, eQTL.df = eQTL.df.example, gene = "ACTN3", gbuild = "hg19", trait = "LDL", tissue = "all", CollapseMethod = "min")
This generates the following plot: #### Figure 2 !(man/figures/Figure2.jpeg)<!-- --> Unlike the previous example for *BBS1*, Figure 2 shows very poor evidence for colocalization between *ACTN3* eQTLs and LDL cholesterol-significant variants. Although there is significant enrichment for *ACTN3* eQTLs among GWAS-significant variants (Figure 2B), there is poor evidence for correlation between P<sub>GWAS</sub> and P<sub>eQTL</sub> (Figure 2D), and it is intuitively clear in Figure 2A that the eQTL and GWAS signals do not colocalize (the brightest colored points, with the strongest association with *ACTN3* expression, are not among the variants most significantly associated with LDL cholesterol levels). <p> </p> ### Example 2 –The `TissueList` function and adding LD information to eQTpLot The plots generated in Example 1 illustrated colocalization between *BBS1* eQTLs and the GWAS peak for LDL cholesterol on chromosome 11, using a PanTissue analysis approach. The user may next wish to investigate if there are specific tissues in which *BBS1* expression is most clearly correlated with the LDL GWAS peak. Using the `TissueList` function of eQTpLot, we can se the Pearson correlation statistics between P<sub>GWAS</sub> and P<sub>eQTL</sub> for *BBS1* and the LDL trait across each tissue contained within `eQTL.df` ranked by degree of correlation:
eQTpLot(GWAS.df = GWAS.df.example, eQTL.df = eQTL.df.example, gene ="BBS1", gbuild = "hg19", trait = "LDL", tissue = "all", TissueList = T)  "For gene:"  "BBS1"  "eQTL analysis for tissue Cells_Cultured_fibroblasts: Pearson correlation: 0.902, p-value: 1.12e-65"  "eQTL analysis for tissue Whole_Blood: Pearson correlation: 0.85, p-value: 1.64e-55"  "eQTL analysis for tissue Brain_Frontal_Cortex_BA9: Pearson correlation: 0.84, p-value: 1.02e-51"  "eQTL analysis for tissue Brain_Nucleus_accumbens_basal_ganglia: Pearson correlation: 0.841, p-value: 1.74e-48"  "eQTL analysis for tissue Brain_Cortex: Pearson correlation: 0.818, p-value: 2.44e-43"
 "eQTL analysis for tissue Nerve_Tibial: Pearson correlation: -0.007, p-value: 9.56e-01"  "Complete"
This output demonstrates a strong correlation between LDL cholesterol levels and *BBS1* expression levels in a number of tissues. To further explore these associations, the user can specifically run eQTpLot on data from a single tissue, for example "Whole_Blood", while also supplying LD data to eQTpLot using the argument `LD.df`:
eQTpLot(GWAS.df = GWAS.df.example, eQTL.df = eQTL.df.example, gene = "BBS1", gbuild = "hg19", trait = "LDL", tissue = "Whole_Blood", LD.df =LD.df.example, R2min = 0.25, LDmin = 100)
Here the argument `LD.df` refers to a data frame containing a list of pairwise LD correlation measurements between all the variants within the LOI, as one might obtain from a PLINK linkage disequilibrium analysis using the --r2 option. `R2min=0.25` such that `LD.df` is filtered to drop variant LD pairs with R<sup>2</sup> < 0.25, and `LDmin=100`, such that only variants in LD with >= 100 other variants will be plotted in the LD heatmap. This generates the following plot: #### Figure 3 !(man/figures/Figure3.jpeg)<!-- --> Figure 3 is different than Figure 1 (the same eQTpLot analysis carried out without LD information supplied) in two important ways. First, a heat map of the LD landscape for all *BBS1* eQTL variants in the tissue "Whole_Blood" within the LOI is shown in Figure 3C; this heatmap makes it clear that a number of GWAS-significant variants are in strong LD with each other. Second, the P-P plot, Figure 3E, now includes LD information for all plotted variants; a lead variant, rs3741360, has been defined (by default the upper-right most variant on the P-P plot), and all other variants are plotted with a color scale corresponding to their squared coefficient of linkage correlation with rs3741360. rs3741360 is also labeled in Figure 3A for reference. Although colocalization of the BBS1 eQTL and LDL GWAS signal spans the entire association peak, most but not all of the GWAS-significant variants are in strong LD with each other. This implies that there are at least two distinct LD blocks at the *BBS1* locus with strong evidence of colocalization between the *BBS1* eQTL and LDL GWAS signals. <p> </p> ### Example 3 – Separating Congruous from Incongruous Variants In addition to including LD data in our eQTpLot analysis, we can also include information on the directions of effect of each variant, with respect to the GWAS trait and *BBS1* expression levels. This is accomplished by setting `congruence=TRUE`:
eQTpLot(GWAS.df = GWAS.df.example, eQTL.df = eQTL.df.example, gene = "BBS1", gbuild = "hg19", trait = "LDL", tissue = "Whole_Blood", LD.df = LD.df.example, R2min = 0.25, LDmin = 100, congruence = TRUE) ```
This generates the following plot:
Figure 4 divides all BBS1 eQTL variants in "Whole_Blood" into two groups: congruent – those associated with either an increase in both BBS1 expression levels and LDL levels, or a decrease in both – and incongruent – those with opposite directions of effect on BBS1 expression levels and LDL levels. In carrying out such an analysis, it becomes clear that it is specifically variants with congruent directions of effect on BBS1 levels and LDL cholesterol levels that are driving the signal colocalization; that is, variants associated with decreases in BBS1 expression strongly colocalize with variants associated with decreases in LDL cholesterol.
Multiple additional modifications to the plots can be specified, as noted above.
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