locuszoomr

In locuszoomr: Gene Locus Plot with Gene Annotations

knitr::opts_chunk$set(
  collapse = TRUE
)
library(locuszoomr)

Introduction

The locuszoomr package allows users to produce publication ready gene locus plots very similar to those produced by the web interface 'locuszoom' (http://locuszoom.org), but running purely locally in R. Plots can easily be customised, labelled and stacked.

These gene annotation plots are produced via R base graphics or 'ggplot2'. A 'plotly' version can also be generated.

Installation

Bioconductor packages ensembldb and an Ensembl database installed either as a package or obtained through Bioconductor packages AnnotationHub are required before installation. To run the examples in this vignette the 'EnsDb.Hsapiens.v75' ensembl database package needs to be installed from Bioconductor.

if (!requireNamespace("BiocManager", quietly = TRUE))
  install.packages("BiocManager")
BiocManager::install("ensembldb")
BiocManager::install("EnsDb.Hsapiens.v75")

Install from CRAN

install.packages("locuszoomr")

Install from Github

devtools::install_github("myles-lewis/locuszoomr")

locuszoomr can access the LDlinkR package to query 1000 Genomes for linkage disequilibrium (LD) across SNPs. In order to make use of this API function you will need a personal access token, available from the LDlink website.

We recommend that users who want to add recombination rate lines to multiple plots download the recombination rate track from UCSC and use it as described in the section Add recombination rate.

Example locus plot

The quick example below uses a small subset (3 loci) of a GWAS dataset incorporated into the package as a demo. The dataset is from a genetic study on Systemic Lupus Erythematosus (SLE) by Bentham et al (2015). The full GWAS summary statistics can be downloaded from https://www.ebi.ac.uk/gwas/studies/GCST003156. The data format is shown below.

library(locuszoomr)
data(SLE_gwas_sub)  ## limited subset of data from SLE GWAS
head(SLE_gwas_sub)

We plot a locus from this dataset by extracting a subset of the data using the locus() function. Make sure you load the correct Ensembl database.

library(EnsDb.Hsapiens.v75)
loc <- locus(data = SLE_gwas_sub, gene = 'UBE2L3', flank = 1e5,
             ens_db = "EnsDb.Hsapiens.v75")
summary(loc)
locus_plot(loc)

if (require(EnsDb.Hsapiens.v75)) {
loc <- locus(data = SLE_gwas_sub, gene = 'UBE2L3', flank = 1e5,
             ens_db = "EnsDb.Hsapiens.v75")
summary(loc)
locus_plot(loc)
}

For users who only want to plot the gene tracks alone alongside their own plots, see section Plot gene annotation only below.

When locus() is called, the function tries to autodetect which columns in the data object refer to chromosome, position, SNP/feature ID and p-value. These columns may need to be specified manually using the arguments chrom, pos, labs and p respectively.

Accessing Ensembl databases

Ensembl databases up to version 86 for Homo sapiens were loaded as individual packages on Bioconductor. Recent databases are available through the AnnotationHub Bioconductor package. Below we show a toy example to load H. sapiens ensembl database v106 (even though it is misaligned with the genotype data). If the argument ens_db in locus() is a character string it specifies an Ensembl package which is queried through get(). For AnnotationHub databases ens_db needs to be set to be the object containing the database (not a string).

library(AnnotationHub)
ah <- AnnotationHub()
query(ah, c("EnsDb", "Homo sapiens"))

## AnnotationHub with 25 records
## # snapshotDate(): 2023-04-25
## # $dataprovider: Ensembl
## # $species: Homo sapiens
## # $rdataclass: EnsDb
## # additional mcols(): taxonomyid, genome, description, coordinate_1_based, maintainer,
## #   rdatadateadded, preparerclass, tags, rdatapath, sourceurl, sourcetype
## # retrieve records with, e.g., 'object[["AH53211"]]'
## 
##              title
##   AH53211  | Ensembl 87 EnsDb for Homo Sapiens
##   ...        ...
##   AH100643 | Ensembl 106 EnsDb for Homo sapiens
##   AH104864 | Ensembl 107 EnsDb for Homo sapiens
##   AH109336 | Ensembl 108 EnsDb for Homo sapiens
##   AH109606 | Ensembl 109 EnsDb for Homo sapiens
##   AH113665 | Ensembl 110 EnsDb for Homo sapiens

Fetch ensembl database version 106.

ensDb_v106 <- ah[["AH100643"]]

# built-in mini dataset
data("SLE_gwas_sub")
loc <- locus(data = SLE_gwas_sub, gene = 'UBE2L3', fix_window = 1e6,
             ens_db = ensDb_v106)
locus_plot(loc)

Controlling the locus

The genomic locus can be specified in several ways. The simplest is to specify a gene by name/symbol using the gene argument. The location of the gene is obtained from the specified Ensembl database. The amount of flanking regions can either be controlled by specifying flank which defaults to 50kb either side of the ends of the gene. flank can either be a single number or a vector of 2 numbers if different down/upstream flanking lengths are required. Alternatively a fixed genomic window (eg. 1 Mb) centred on the gene of interest can be specified using the argument fix_window. The locus can be specified manually by specifying the chromosome using seqname and genomic position range using xrange. Finally, a region can be specified by naming the index_snp, in which case the object data is searched for the coordinates of that SNP and the size of the region defined using fix_window or flank.

Obtaining LD information

Once an API personal access token has been obtained, the LDlink API can be called using the function link_LD() to retrieve LD (linkage disequilibrium) information at the locus which is overlaid on the locus plot. This is shown as a colour overlay showing the level of $r^2$ between SNPs and the index SNP which defaults to the SNP with the lowest p-value (or the SNP can be specified manually). Requests to LDlink are cached using the memoise package, to reduce API requests.

# Locus plot using SLE GWAS data from Bentham et al 2015
# FTP download full summary statistics from
# https://www.ebi.ac.uk/gwas/studies/GCST003156
library(data.table)
SLE_gwas <- fread('../bentham_2015_26502338_sle_efo0002690_1_gwas.sumstats.tsv')
loc <- locus(SLE_gwas, gene = 'UBE2L3', flank = 1e5,
             ens_db = "EnsDb.Hsapiens.v75")
loc <- link_LD(loc, token = "your_token")
locus_plot(loc)

The subset of GWAS data included in the locuszoomr package has LD data already acquired from LDlink which is included in the r2 column. This can be plotted by setting LD = "r2". This method also allows users to add their own LD information from their own datasets to loci.

loc <- locus(SLE_gwas_sub, gene = 'UBE2L3', flank = 1e5, LD = "r2",
             ens_db = "EnsDb.Hsapiens.v75")
locus_plot(loc, labels = c("index", "rs140492"),
                label_x = c(4, -5))

if (require(EnsDb.Hsapiens.v75)) {
loc <- locus(SLE_gwas_sub, gene = 'UBE2L3', flank = 1e5, LD = "r2",
             ens_db = "EnsDb.Hsapiens.v75")
locus_plot(loc, labels = c("index", "rs140492"), label_x = c(4, -5))
}

Add recombination rate

In keeping with the original locuszoom, recombination rate can be shown on a secondary y axis using the link_recomb() function to retrieve recombination rate data from UCSC genome browser. Calls to UCSC are cached using memoise to reduce API requests.

loc3 <- locus(SLE_gwas_sub, gene = 'STAT4', flank = 1e5, LD = "r2",
              ens_db = "EnsDb.Hsapiens.v75")
loc3 <- link_recomb(loc3)
locus_plot(loc3)

knitr::include_graphics("recomb.png")

The recombination line can be offset from the scatter points to make it more visible by setting recomb_offset = 0.1.

If you are performing multiple UCSC queries, it is much faster to download the whole recombination rate track data file (around 30 MB) from UCSC genome browser (documented here).

The download site can be accessed at http://hgdownload.soe.ucsc.edu/gbdb/hg38/recombRate/. For hg38, download recomb1000GAvg.bw.

For hg19, the link is http://hgdownload.soe.ucsc.edu/gbdb/hg19/decode/ and the default track we use is hapMapRelease24CombinedRecombMap.bw.

The .bw track file can then be loaded into R as a GRanges object using import.bw(), and then used directly by link_recomb() as follows:

library(rtracklayer)
recomb.hg19 <- import.bw("/../hapMapRelease24CombinedRecombMap.bw")
loc3 <- link_recomb(loc3, recomb = recomb.hg19)
locus_plot(loc3)

Customise plots and labels

Various plotting options can be customised through arguments via the call to locus_plot(). When calling locus_plot() arguments are passed to either genetracks() to control the gene tracks or passed via the ... system onto scatter_plot() to control the scatter plot.

Plot borders can be set using border = TRUE. The chromosome position $x$ axis labels can be placed under the top or bottom plots using xtick = "top" or "bottom".

Additional arguments can also be passed onto plot() via the ... system, e.g. ylim, par() settings etc. For example col = NA can be added to locus_plot() or scatter_plot() to remove the black outline around the scatter plot markers.

Labels can be added by specifying a vector of SNP or genomic feature IDs as shown in the plot above using the argument labels (see scatter_plot()). The value "index" refers to the index SNP as the highest point in the locus or as defined by the argument index_snp when locus() is called. The easiest way to identify points is using the plotly version locus_plotly() which allows you to hover over points and see their rsid or feature label. label_x and label_y control the position of the labels and can be specified as a single value or a vector of values.

Point shapes

Advanced users familiar with base graphics can customise every single point on the scatter plot, by adding columns named bg, col, pch or cex directly to the dataframe stored in $data element of the locus object. Setting these will overrule any default settings. These columns refer to their respective base graphics arguments: bg sets the fill colour for points, col sets the outline colour, pch sets the symbols (see ?points for a list of these) and cex sets the size of points (default is 1).

# add column 'typed' as a factor which is 1 for typed, 0 for imputed
loc$data$typed <- factor(rbinom(n = nrow(loc$data), 1, 0.3))
# convert column to shapes by adding a column called 'pch'
loc$data$pch <- c(21, 24)[loc$data$typed]
locus_plot(loc)
# pch 21 = circles = imputed
# pch 24 = triangles = typed

See the help pages at ?locus_plot and ?scatter_plot for more details.

Customise gene tracks

The gene tracks can be also customised with colours and gene label text position. Gene names can be italicised by setting the argument italics = TRUE. See the help page at ?genetracks and ?locus_plot for more details.

# Filter by gene biotype
locus_plot(loc, filter_gene_biotype = "protein_coding")

# Custom selection of genes using gene names
locus_plot(loc, filter_gene_name = c('UBE2L3', 'RIMBP3C', 'YDJC', 'PPIL2',
                                     'PI4KAP2', 'MIR301B'))

Plot gene annotation only

The gene track can be plotted from a locus class object using the function genetracks(). This uses base graphics, so layout() can be used to stack custom-made plots above or below the gene tracks. The function set_layers() is designed to make this easier for users. See section Layering plots below.

genetracks(loc, highlight = "UBE2L3")

if (require(EnsDb.Hsapiens.v75)) {
genetracks(loc, highlight = "UBE2L3")
}

The function allows control over plotting of the gene tracks such as changing the number of gene annotation tracks and the colour scheme. Set showExons=FALSE to show only genes and hide the exons.

# Limit the number of tracks
# Filter by gene biotype
# Customise colours
genetracks(loc, maxrows = 3, filter_gene_biotype = 'protein_coding',
           gene_col = 'grey', exon_col = 'orange', exon_border = 'darkgrey')

if (require(EnsDb.Hsapiens.v75)) {
genetracks(loc, maxrows = 3, filter_gene_biotype = 'protein_coding',
           gene_col = 'grey', exon_col = 'orange', exon_border = 'darkgrey')
}

For advanced users who only want the gene tracks to add to their own plots, locus() can be called without the data argument specified (or data can be set to NULL). Then genetracks can be plotted in base graphics, ggplot2 or plotly.

loc00 <- locus(gene = 'UBE2L3', flank = 1e5, ens_db = "EnsDb.Hsapiens.v75")

genetracks(loc00)  # base graphics
gg_genetracks(loc00)  # ggplot2
genetrack_ly(loc00)  # plotly

Add eQTL data via LDlink

GTEx eQTL data can be accessed via the LDlinkR package which queries the LDlink server API to download data and add it to a 'locus' object. The eQTL data is stored in LDexp slot in the locus object.

# obtain GTEx eQTL data from LDlinkR
# needs personal access token for LDlink
loc2 <- locus(SLE_gwas_sub, gene = 'IRF5', flank = c(7e4, 2e5), LD = "r2",
              ens_db = "EnsDb.Hsapiens.v75")
loc2 <- link_eqtl(loc2, token = "..")
head(loc2$LDexp)  # show eQTL data

Often at a locus eQTL data may be available on a range of genes and tissues. The following code shows how to see which genes and tissues are available and how eQTL SNPs they each have.

table(loc2$LDexp$Gene_Symbol)
##      AHCYL2           IRF5            KCP        METTL2B           NRF1   RP11-212P7.2 
##           5           1618              1              2              9              3 
## ...

table(loc2$LDexp$Tissue)
##             Adipose - Subcutaneous          Adipose - Visceral (Omentum) 
##                                 78                                    69 
##                      Adrenal Gland                        Artery - Aorta 
##                                 38                                   109
## ...

Embedded eQTL data can be plotted using eqtl_plot() or shown as an overlay plot using overlay_plot() which shows GWAS or association data on the y axis and overlays eQTL results using symbols and colours.

# just plot eQTL results
eqtl_plot(loc2, tissue = "Whole Blood", eqtl_gene = "IRF5")

# overlay eqtl plot
overlay_plot(loc2, eqtl_gene = "IRF5")

knitr::include_graphics("eqtl_overlay.png")

Change y-axis variable

Instead of plotting -log~10~ p-value on the y axis, it is possible to specify a different variable in your dataset using the argument yvar.

locb <- locus(SLE_gwas_sub, gene = 'UBE2L3', flank = 1e5, yvar = "beta",
              ens_db = "EnsDb.Hsapiens.v75")
locus_plot(locb)

if (require(EnsDb.Hsapiens.v75)) {
locb <- locus(SLE_gwas_sub, gene = 'UBE2L3', flank = 1e5, yvar = "beta",
              ens_db = "EnsDb.Hsapiens.v75")
locus_plot(locb)
}

Arrange multiple locus plots

locuszoomr uses graphics::layout to arrange plots. To layout multiple locus plots side by side, use the function multi_layout() to set the number of locus plots per row and column. The plots argument in multi_layout() can either be a list of locus class objects, one for each gene. Or for full control it can be an 'expression' with a series of manual calls to locus_plot(). Alternatively a for loop could be called within the plots expression.

genes <- c("STAT4", "IRF5", "UBE2L3")

# generate list of 'locus' class objects, one for each gene
loclist <- lapply(genes, locus,
                  data = SLE_gwas_sub,
                  ens_db = "EnsDb.Hsapiens.v75",
                  LD = "r2")

## produce 3 locus plots, one on each page
pdf("myplot.pdf")
multi_layout(loclist)
dev.off()

## place 3 locus plots in a row on a single page
pdf("myplot.pdf")
multi_layout(loclist, ncol = 3, labels = "index")
dev.off()

## full control
loc2 <- locus(SLE_gwas_sub, gene = 'IRF5', flank = c(7e4, 2e5), LD = "r2",
              ens_db = "EnsDb.Hsapiens.v75")
loc3 <- locus(SLE_gwas_sub, gene = 'STAT4', flank = 1e5, LD = "r2",
              ens_db = "EnsDb.Hsapiens.v75")

pdf("myplot.pdf", width = 9, height = 6)
multi_layout(ncol = 3,
             plots = {
               locus_plot(loc, use_layout = FALSE, legend_pos = 'topleft')
               locus_plot(loc2, use_layout = FALSE, legend_pos = NULL)
               locus_plot(loc3, use_layout = FALSE, legend_pos = NULL,
                          labels = "index")
             })
dev.off()

if (require(EnsDb.Hsapiens.v75)) {
loc2 <- locus(SLE_gwas_sub, gene = 'IRF5', flank = c(7e4, 2e5), LD = "r2",
              ens_db = "EnsDb.Hsapiens.v75")
loc3 <- locus(SLE_gwas_sub, gene = 'STAT4', flank = 1e5, LD = "r2",
              ens_db = "EnsDb.Hsapiens.v75")

multi_layout(ncol = 3,
             plots = {
               locus_plot(loc, use_layout = FALSE, legend_pos = 'topleft')
               locus_plot(loc2, use_layout = FALSE, legend_pos = NULL)
               locus_plot(loc3, use_layout = FALSE, legend_pos = NULL,
                          labels = "index")
             })
}

Layering plots

Column of plots

locuszoomr has been designed with modular functions to enable layering of plots on top of each other in a column with gene tracks on the bottom. set_layers() is used to set layout of plots in base graphics by calling layout(). You will need to reset par() to original layout settings once the multi-panel plot is finished.

scatter_plot() is used to generate the locus plot. line_plot() is used as an example of an additional plot. Also see eqtl_plot() for plotting eQTL information retrieved via the LDlinkR package.

pdf("myplot2.pdf", width = 6, height = 8)
# set up layered plot with 2 plots & a gene track; store old par() settings
oldpar <- set_layers(2)
scatter_plot(loc, xticks = FALSE)
line_plot(loc, col = "orange", xticks = FALSE)
genetracks(loc)
par(oldpar)  # revert par() settings
dev.off()

if (require(EnsDb.Hsapiens.v75)) {
oldpar <- set_layers(2)
scatter_plot(loc, xticks = FALSE)
line_plot(loc, col = "orange", xticks = FALSE)
genetracks(loc)
par(oldpar)
}

Overlaid plots

scatter_plot() can be called with argument add = TRUE to add multiple sets of points overlaid on one plot.

dat <- SLE_gwas_sub
dat$p2 <- -log10(dat$p * 0.1)
locp <- locus(dat, gene = 'UBE2L3', flank = 1e5, ens_db = "EnsDb.Hsapiens.v75")
locp2 <- locus(dat, gene = 'UBE2L3', flank = 1e5, yvar = "p2",
               ens_db = "EnsDb.Hsapiens.v75")

oldpar <- set_layers(1)
scatter_plot(locp, xticks = FALSE, pcutoff = NULL, ylim = c(0, 16))
scatter_plot(locp2, xticks = FALSE, pcutoff = NULL, scheme = "orange",
             pch = 22, add = TRUE)
genetracks(loc)
par(oldpar)

if (require(EnsDb.Hsapiens.v75)) {
dat <- SLE_gwas_sub
dat$p2 <- -log10(dat$p * 0.1)
locp <- locus(dat, gene = 'UBE2L3', flank = 1e5, ens_db = "EnsDb.Hsapiens.v75")
locp2 <- locus(dat, gene = 'UBE2L3', flank = 1e5, yvar = "p2",
               ens_db = "EnsDb.Hsapiens.v75")

oldpar <- set_layers(1)
scatter_plot(locp, xticks = FALSE, pcutoff = NULL, ylim = c(0, 16))
scatter_plot(locp2, xticks = FALSE, pcutoff = NULL, scheme = "orange",
             pch = 22, add = TRUE)
genetracks(loc)
par(oldpar)
}

Add custom legend / features

The power of base graphics is that it gives complete control over plotting. In the example below, we show how to add your own legend, text labels, lines to demarcate a gene and extra points on top or underneath the main plot. When plot() is called, base graphics allows additional plotting using the arguments panel.first and panel.last. Since these are called inside the locus_plot() function they need to be quoted using quote().

# add vertical lines for gene of interest under the main plot
pf <- quote({
  v <- locp$TX[locp$TX$gene_name == "UBE2L3", c("start", "end")]
  abline(v = v, col = "orange")
})

pl <- quote({
  # add custom text label for index SNP
  lx <- locp$data$pos[locp$data$rsid == locp$index_snp]
  ly <- locp$data$logP[locp$data$rsid == locp$index_snp]
  text(lx, ly, locp$index_snp, pos = 4, cex = 0.8)
  # add extra points
  px <- rep(22.05e6, 3)
  py <- 10:12
  points(px, py, pch = 21, bg = "green")
  # add custom legend
  legend("topleft", legend = c("group A", "group B"),
         pch = 21, pt.bg = c("blue", "green"), bty = "n")
})

locus_plot(locp, pcutoff = NULL, panel.first = pf, panel.last = pl)

if (require(EnsDb.Hsapiens.v75)) {
pf <- quote({
  v <- locp$TX[locp$TX$gene_name == "UBE2L3", c("start", "end")]
  abline(v = v, col = "orange")
})

pl <- quote({
  lx <- locp$data$pos[locp$data$rsid == locp$index_snp]
  ly <- locp$data$logP[locp$data$rsid == locp$index_snp]
  text(lx, ly, locp$index_snp, pos = 4, cex = 0.8)
  px <- rep(22.05e6, 3)
  py <- 10:12
  points(px, py, pch = 21, bg = "green")
  legend("topleft", legend = c("group A", "group B"),
         pch = 21, pt.bg = c("blue", "green"), bty = "n")
})

locus_plot(locp, pcutoff = NULL, panel.first = pf, panel.last = pl)
}

ggplot2 version

ggplot2 versions of several of the above functions are available. For a whole locus plot use locus_ggplot().

locus_ggplot(loc)

The gene tracks can be produced on their own as a ggplot2 object using gg_genetracks().

gg_genetracks(loc)

The scatter plot alone can be produced as a ggplot2 object.

p <- gg_scatter(loc)
p

Finally, gg_addgenes() can be used to add gene tracks to an existing ggplot2 plot that has been previously created and customised.

gg_addgenes(p, loc)

If you are using ggplot2, the easiest way to edit the scatter plot and change legend and other details, is to use gg_scatter() and modify the plot using the ggplot2 + notation. For example, to increase the font size of the legend you could do the following:

p <- gg_scatter(loc) +
  theme(legend.text = element_text(size = 14),
        legend.title = element_text(size = 14))
gg_addgenes(p, loc)

Shapes

Shapes can be overlaid in the scatter plot by setting either beta or shape to a column name in the dataset. Setting beta shows up-triangles for positive beta values and down-triangles for negative beta values. If shape is set, for example to show imputed vs typed SNPs, the column needs to be a factor (to make the legend nice). shape_values is then set as a vector of shapes to which the levels of the factor are mapped on the scatter plot.

locus_ggplot(loc, beta = "beta")

# add column 'typed' as a factor with levels "typed", "imputed"
loc$data$typed <- factor(rbinom(n = nrow(loc$data), 1, 0.3), labels = c("imputed", "typed"))
locus_ggplot(loc, shape = "typed")

Layering plots

Plots can be layered above gene tracks, using cowplot::plot_grid() or using the patchwork package.

g <- gg_genetracks(loc)
library(cowplot)
plot_grid(p, p, g, ncol = 1, rel_heights = c(2, 2, 1), align = "v")

# patchwork method
library(patchwork)
p / p / g

# patchwork method 2
wrap_plots(p, p, g, ncol = 1)

For users who prefer the grid system we recommend also looking at Bioconductor packages Gviz or ggbio as possible alternatives.

Arrange multiple ggplots

It is possible to use either the cowplot or ggpubr packages to layout multiple locus ggplots on the same page.

library(cowplot)
p1 <- locus_ggplot(loc, labels = "index", nudge_x = 0.03)
p2 <- locus_ggplot(loc2, legend_pos = NULL)
plot_grid(p1, p2, ncol = 2)

if (require(EnsDb.Hsapiens.v75)) {
p1 <- locus_ggplot(loc, labels = "index", nudge_x = 0.03)
p2 <- locus_ggplot(loc2, legend_pos = NULL)
cowplot::plot_grid(p1, p2, ncol = 2)
}

Or using ggpubr or gridExtra:

library(ggpubr)
pdf("my_ggplot.pdf", width = 10)
ggarrange(p1, p2, ncol = 2)
dev.off()

library(gridExtra)
pdf("my_ggplot.pdf", width = 10)
grid.arrange(p1, p2, ncol = 2)
dev.off()

A known issue with the ggplot2 version locus_ggplot() is that the system for ensuring that gene labels seems to go wrong when the plot window is resized during arrangement of multiple plots. The workable solution at present is to make sure that the height and width of the final pdf/output is enlarged as per the example above, which plots fine without any text overlapping when exported to pdf at an appropriate size.

Plotly version

locuszoomr includes a 'plotly' version for plotting locus plots which is interactive. This allows users to hover over the plot and reveal additional information such as SNP rs IDs for each point and information about each gene in the gene tracks. This can help when exploring a locus or region and trying to identify particular SNPs (or genomic features) of interest.

locus_plotly(loc2)

knitr::include_graphics("plotly.png")

Plot all GWAS peaks

locuszoomr includes a function quick_peak() to quickly find GWAS peaks based on a set p-value threshold (default 5e-08) and specifying a minimum distance between peaks (default 1 Mb). The function will either find all peaks ordered by lowest p-value, or the top n peaks can be identified by setting npeaks.

# FTP download full summary statistics of this SLE GWAS from
# https://www.ebi.ac.uk/gwas/studies/GCST003156
library(data.table)
SLE_gwas <- fread('../bentham_2015_26502338_sle_efo0002690_1_gwas.sumstats.tsv')

pks <- quick_peak(SLE_gwas)
## 34 peaks found (0.328 secs)

top_snps <- SLE_gwas$rsid[pks]
head(top_snps)
## [1] "rs141910407" "rs114056368" "rs4274624"   "rs17849501"  "rs1143679"   "rs114115096"

We can now plot locus plots for all the GWAS peaks as a single pdf, as specified by the vector of top SNPs. The link_recomb() line below can be sped up by downloading the recombination track file from UCSC - see example code in Add recombination rate.

all_loci <- lapply(top_snps, function(i) {
  loc <- locus(data = SLE_gwas, index_snp = i, fix_window = 1e6,
        ens_db = "EnsDb.Hsapiens.v75")
  link_recomb(loc)
})

pdf("sle_loci.pdf")
tmp <- lapply(all_loci, locus_plot, labels = "index")
dev.off()

Alternatively export plots using ggplot2.

pp <- lapply(all_loci, locus_ggplot, labels = "index", nudge_y = 1)

library(gridExtra)
pdf("sle_loci_gg.pdf")
for (i in seq_along(pp)) {
  grid.arrange(pp[[i]])
}
dev.off()

Manhattan & other plots

For Manhattan plots, log p-value QQ plot and easy labelling of volcano plots or other scatter plots with draggable labels, check out our sister package easylabel on CRAN at https://cran.r-project.org/package=easylabel.

Citation

If you use this package please cite as:

Lewis MJ, Wang S. (2025) locuszoomr: an R package for visualising publication-ready regional gene locus plots. Bioinformatics Advances 2025; vbaf006, doi:10.1093/bioadv/vbaf006

References

Pruim RJ, Welch RP, Sanna S, Teslovich TM, Chines PS, Gliedt TP, Boehnke M, Abecasis GR, Willer CJ. (2010) LocusZoom: Regional visualization of genome-wide association scan results. Bioinformatics 2010; 26(18): 2336-7.

locuszoomr documentation built on April 12, 2025, 2:28 a.m.