# locuszoomr

#### Myles Lewis

# 1 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.

# 2 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.

# 3 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)
    ##   chrom       pos        rsid other_allele effect_allele           p
    ## 1     2 191794580 rs193239665            A             T 0.000723856
    ## 2     2 191794978  rs72907256            C             T 0.000481744
    ## 3     2 191795546   rs6434429            C             G 0.156723000
    ## 4     2 191795869 rs148265823            A             G 0.606197000
    ## 5     2 191799600  rs60202309            T             G 0.100580000
    ## 6     2 191800180 rs114544034            T             C 0.022496800
    ##          beta         se   OR  OR_lower  OR_upper    r2
    ## 1  0.32930375 0.09741618 1.39 1.1483981 1.6824305 0.037
    ## 2  0.39877612 0.11423935 1.49 1.1910878 1.8639264 0.034
    ## 3 -0.09431068 0.06659515 0.91 0.7986462 1.0368796 0.004
    ## 4 -0.04082199 0.07918766 0.96 0.8219877 1.1211846 0.004
    ## 5  0.07696104 0.04686893 1.08 0.9852084 1.1839119 0.001
    ## 6 -0.16251893 0.07122170 0.85 0.7392542 0.9773364 0.019

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)

    ## Gene UBE2L3
    ## Chromosome 22, position 21,803,736 to 22,078,323
    ## 514 SNPs/datapoints
    ## 19 gene transcripts
    ## 8 protein_coding, 3 snoRNA, 2 lincRNA, 2 miRNA, 2 misc_RNA, 1 pseudogene, 1 sense_intronic
    ## Ensembl version: 75
    ## Organism: Homo sapiens
    ## Genome build: GRCh37

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.

# 4 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)

# 5 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`.

# 6 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))

# 7 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)

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)

# 8 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.

## 8.1 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.

# 9 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'))

# 10 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")

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')

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

# 11 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")

# 12 Change y-axis variable

Instead of plotting -log10 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)

# 13 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()

# 14 Layering plots

## 14.1 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()

## 14.2 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)

# 15 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)

# 16 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)

## 16.1 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")

## 16.2 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.

## 16.3 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)

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.

# 17 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)

# 18 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()

# 19 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.

# 20 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

# 21 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.