Plot multi-GWAS/QTL or multi-ancestry fine-mapping results

Plot multi-GWAS/QTL or multi-ancestry(i.e. trans-ethnic) fine-mapping results generated by tools like PAINTOR.

Usage

plot_locus_multi(
  dat_ls,
  LD_ls,
  locus_dir,
  conditions = names(dat_ls),
  show_plot = TRUE,
  verbose = TRUE,
  ...
)

Arguments

dat_ls

A named list of data.tables generated by echolocatoR::finemap_loci.

LD_ls

A named list of link disequilibrium (LD) matrices (one per item in dat_ls).

locus_dir

Storage directory to use.

conditions

Conditions to group dat_ls by. The length of conditions must equal the number of items in dat_ls.

show_plot

Print plot to screen.

verbose

Print messages.

...

Arguments passed on to plot_locus

dataset_type

Dataset type (e.g. "GWAS" or "eQTL").

color_r2

Whether to color data points (SNPs) by how strongly they correlate with the lead SNP (i.g. LD measured in terms of r2).

finemap_methods

Fine-mapping methods to plot tracks for, where the y-axis show the Posterior Probabilities (PP) of each SNP being causal.

track_order

The order in which tracks should appear (from top to bottom).

track_heights

The height of each track (from top to bottom).

plot_full_window

Include a track with a Manhattan plot of the full GWAS/eQTL locus (not just the zoomed-in portion).

dot_summary

Include a dot-summary plot that highlights the Lead, Credible Set, and Consensus SNPs.

qtl_suffixes

If columns with QTL data is included in dat, you can indicate which columns those are with one or more string suffixes (e.g. qtl_suffixes=c(".eQTL1",".eQTL2") to use the columns "P.QTL1", "Effect.QTL1", "P.QTL2", "Effect.QTL2").

mean.PP

Include a track showing mean Posterior Probabilities (PP) averaged across all fine-mapping methods.

sig_cutoff

Filters out SNPs to plot based on an (uncorrected) p-value significance cutoff.

gene_track

Include a track showing gene bodies.

max_transcripts

Maximum number of transcripts per gene.

tx_biotypes

Transcript biotypes to include in the gene model track. By default (NULL), all transcript biotypes will be included. See get_tx_biotypes for a full list of all available biotypes

point_size

Size of each data point.

point_alpha

Opacity of each data point.

snp_group_lines

Include vertical lines to help highlight SNPs belonging to one or more of the following groups: Lead, Credible Set, Consensus.

labels_subset

Include colored shapes and RSID labels to help highlight SNPs belonging to one or more of the following groups: Lead, Credible Set, Consensus.

xtext

Include x-axis title and text for each track (not just the lower-most one).

show_legend_genes

Show the legend for the gene_track.

zoom_exceptions_str

Names of tracks to exclude when zooming.

xgr_libnames

Passed to XGR_plot. Which XGR annotations to check overlap with. For full list of libraries see here (XGR on CRAN). Passed to the RData.customised argument in XGR::xRDataLoader. Examples:

"ENCODE_TFBS_ClusteredV3_CellTypes"
"ENCODE_DNaseI_ClusteredV3_CellTypes"
"Broad_Histone"

xgr_n_top

Passed to XGR_plot. Number of top annotations to be plotted (passed to XGR_filter_sources and then XGR_filter_assays).

nott_epigenome

Include tracks showing brain cell-type-specific epigenomic data from Nott et al. (2019) (doi:10.1126/science.aay0793 ).

nott_regulatory_rects

Include track generated by NOTT2019_epigenomic_histograms.

nott_show_placseq

Include track generated by NOTT2019_plac_seq_plot.

nott_binwidth

When including Nott et al. (2019) epigenomic data in the track plots, adjust the bin width of the histograms.

nott_bigwig_dir

Instead of pulling Nott et al. (2019) epigenomic data from the UCSC Genome Browser, use a set of local bigwig files.

roadmap

Find and plot annotations from Roadmap.

roadmap_n_top

Passed to ROADMAP_plot. Number of top annotations to be plotted (passed to ROADMAP_query).

roadmap_query

Only plot annotations from Roadmap whose metadata contains a string or any items from a list of strings (e.g. "brain" or c("brain","liver","monocytes")).

credset_thresh

The minimum fine-mapped posterior probability for a SNP to be considered part of a Credible Set. For example, credset_thresh=.95 means that all Credible Set SNPs will be 95% Credible Set SNPs.

facet_formula

Formula to facet plots by. See facet_grid for details.

save_plot

Save plot as RDS file.

plot_format

Format to save plot as when saving with ggsave.

save_RDS

Save the tracks as an RDS file (Warning: These plots take up a lot disk space).

return_list

Return a named list with each track as a separate plot (default: FALSE). If TRUE, will return a merged plot using wrap_plots.

conda_env

Conda environment to use.

nThread

Number of threads to parallelise across (when applicable).

LD_reference

LD reference to use:

1KGphase1: 1000 Genomes Project Phase 1 (genome build: hg19).
1KGphase3: 1000 Genomes Project Phase 3 (genome build: hg19).
UKB: Pre-computed LD from a British European-decent subset of UK Biobank. Genome build : hg19
<vcf_path>: User-supplied path to a custom VCF file to compute LD matrix from.
Accepted formats: .vcf / .vcf.gz / .vcf.bgz
Genome build : defined by user with target_genome.
<matrix_path>: User-supplied path to a pre-computed LD matrix. Accepted formats: .rds / .rda / .csv / .tsv / .txt
Genome build : defined by user with target_genome.

zoom

Zoom into the center of the locus when plotting (without editing the fine-mapping results file). You can provide either:

The size of your plot window in terms of basepairs (e.g. zoom=50000 for a 50kb window).
How much you want to zoom in (e.g. zoom="1x" for the full locus, zoom="2x" for 2x zoom into the center of the locus, etc.).

You can pass a list of window sizes (e.g. c(50000,100000,500000)) to automatically generate multiple views of each locus. This can even be a mix of different style inputs: e.g. c("1x","4.5x",25000).

genomic_units

Which genomic units to return window limits in.

density_adjust

Passed to adjust argument in geom_density.

strip.text.y.angle

Angle of the y-axis facet labels.

dpi

dpi to use for raster graphics

height

height (defaults to the height of current plotting window)

width

width (defaults to the width of current plotting window)

dat

Data to query transcripts with.

LD_matrix

LD matrix.

consensus_thresh

The minimum number of fine-mapping tools in which a SNP is in the Credible Set in order to be included in the "Consensus_SNP" column.

Examples

locus_dir <- file.path(tempdir(),echodata::locus_dir)
#### Make dat_ls ####
dat <- echodata::filter_snps(echodata::BST1, bp_distance = 10000) 
#> FILTER:: Filtering by SNP features.
#> + FILTER:: Post-filtered data: 114 x 26
dat_ls <- list(gwas1=dat, gwas2=dat)
#### Make LD_ls ####
LD_matrix <- echodata::BST1_LD_matrix
LD_ls <- list(ancestry1=LD_matrix, ancestry2=LD_matrix)
#### Make plot ####
plot_list <- plot_locus_multi(dat_ls = dat_ls, 
                              LD_ls = LD_ls,
                              locus_dir = locus_dir)
#> LD_matrix detected. Coloring SNPs by LD with lead SNP.
#> Filling r/r2 NAs with 0
#> LD_matrix detected. Coloring SNPs by LD with lead SNP.
#> Filling r/r2 NAs with 0
#> +-------- Locus Plot:  BST1 --------+
#> + support_thresh = 2
#> + Calculating mean Posterior Probability (mean.PP)...
#> + 4 fine-mapping methods used.
#> + 8 Credible Set SNPs identified.
#> + 4 Consensus SNPs identified.
#> + Filling NAs in CS cols with 0.
#> + Filling NAs in PP cols with 0.
#> ++ echoplot:: GWAS full window track
#> ++ echoplot:: GWAS track
#> ++ echoplot:: Merged fine-mapping track
#> Melting PP and CS from 5 fine-mapping methods.
#> + echoplot:: Constructing SNP labels.
#> Data is already melted. Skipping.
#> Adding SNP group labels to locus plot.
#> ++ echoplot:: Adding Gene model track.
#> Converting dat to GRanges object.
#> max_transcripts= 1 . 
#> 1  transcripts from  1  genes returned.
#> Fetching data...
#> OK
#> Parsing exons...
#> OK
#> Defining introns...
#> OK
#> Defining UTRs...
#> OK
#> Defining CDS...
#> OK
#> aggregating...
#> Done
#> Constructing graphics...
#> + Adding vertical lines to highlight SNP groups.
#> +>+>+>+>+ zoom =  1x  +<+<+<+<+
#> + echoplot:: Get window suffix...
#> + echoplot:: Removing GWAS full window track @ zoom=1x
#> + Removing subplot margins...
#> + Reordering tracks...
#> + Ensuring last track shows genomic units.
#> + Aligning xlimits for each subplot...
#> + Checking track heights...