• Because echolocatoR now relies on many subpackages that rely on one another, sometimes errors can occur when R tries to update one R package before updating its echoverse dependencies (and thus is unable to find new functions). As echoverse stabilizes over time, this should happen less frequently. However, in the meantime the solution is to simply rerun remotes::install_github("RajLabMSSM/echolocatoR") until all subpackages are fully updates.
• susieR: Sometimes an older version of susieR is installed from CRAN (e.g. 0.11.92), but echofinemap requires version >= 0.12.0. To get around this, you can install susieR directly from GitHub: devtools::install_github("stephenslab/susieR")
• System dependencies can sometimes cause issues when using different packages. I’ve tried to account for as many of these as possible automatically within the code, but using the Docker/Singularity provided below can further mitigate these issues.
• The R package XML (which some echoverse subpackages depend on) has some additional system dependencies that must be installed beforehand. If XML does not install automatically, try installing lbxml on your system using brew install libxml2 (MacOS), sudo apt-get install libxml2 (Linux) or conda install r-xml if you are running echolocatoR from within a conda environment.

echoverse <- c('echolocatoR','echodata','echotabix',
               'echoannot','echoconda','echoLD',
               'echoplot','catalogueR','downloadR',
               'echofinemap','echodeps', # under construction
               'echogithub')
toc <- echogithub::github_pages_vignettes(owner = "RajLabMSSM",
                                          repo = echoverse,
                                          as_toc = TRUE,
                                          verbose = FALSE)

• 🦇 echolocatoR

  • QTLs

  • docker

  • echolocatoR

  • finemapping portal

  • plot locus

  • summarise

• 🦇 echodata

  • echodata

  • echolocatoR Finemapping Portal

• 🦇 echotabix

  • echotabix

• 🦇 echoannot

  • cell type specific epigenomics

  • echoannot

• 🦇 echoconda

  • echoconda

• 🦇 echoLD

  • echoLD

• 🦇 echoplot

  • echoplot

• 🦇 echofinemap

  • echoverseTemplate

• 🦇 echodeps

  • echoverseTemplate

• echoverse subpackages: echolocatoR has been broken into separate subpackages, making it much easier to edit/debug each step of the full finemap_loci pipeline, and improving robustness throughout. It also provides greater flexibility for users to construct their own custom pipelines from these modules.
• GITHUB_TOKEN: GitHub now requires users to create Personal Authentication Tokens (PAT) to avoid download limits. This is essential for installing echolocatoR as many resources from GitHub need to be downloaded. See here for further instructions. = echodata::construct_colmap(): Previously, users were required to input key column name mappings as separate arguments to echolocatoR::finemap_loci. This functionality has been deprecated and replaced with a single argument, colmap=. This allows users to save the construct_colmap() output as a single variable and reuse it later without having to write out each mapping argument again (and helps reduce an already crowded list of arguments).
• MungeSumstats: finemap_loci now accepts the output of MungeSumstats::format_sumstats/import_sumstats as-is (without requiring colmap=, so long as munged=TRUE). Standardizing your GWAS/QTL summary stats this way greatly reduces (or eliminates) the time taken to do manual formatting.
• echolocatoR::finemap_loci arguments: Several arguments have been deprecated or had their names changed to be harmonized across all the subpackages and use a unified naming convention. See ?echolocatoR::finemap_loci for details.
• echoconda: The echoverse subpackage echoconda now handles all conda environment creation/use internally and automatically, without the need for users to create the conda environment themselves as a separate step. Also, the default conda env echoR has been replaced by echoR_mini, which reduces the number of dependencies to just the bare minimum (thus greatly speeding up build time and reducing potential version conflicts).
• FINEMAP: More outputs from the tool FINEMAP are now recorded in the echolocatoR results (see ?echofinemap::FINEMAP or this Issue for details). Also, a common dependency conflict between FINEMAP>=1.4 and MacOS has been resolved (see this Issue for details.
• echodata: All example data and data transformation functions have been moved to the echoverse subpackage echodata.
• LD_reference=: In addition to the UKB, 1KGphase1/3 LD reference panels, finemap_loci() can now take custom LD panels by supplying finemap_loci(LD_reference=) with a list of paths to VCF files (.vcf / vcf.gz / vcf.bgz) or pre-computed LD matrices with RSIDs as the row/col names (.rda / .rds / .csv / .tsv. / .txt / .csv.gz / tsv.gz / txt.gz).
• Expanded fine-mapping methods: “ABF”, “COJO_conditional”, “COJO_joint” “COJO_stepwise”,“FINEMAP”,“PAINTOR” (including multi-GWAS and multi-ancestry fine-mapping),“POLYFUN_FINEMAP” ,“POLYFUN_SUSIE”,“SUSIE”
• FINEMAP fixed: There were a number of issues with FINEMAP due to differing output formats across different versions, system dependency conflicts, and the fact that it can produce multiple Credible Sets. All of these have been fixed and the latest version of FINEMAP can be run on all OS platforms.
• Debug mode: Within finemap_loci() I use a tryCatch() when iterating across loci so that if one locus fails, the rest can continue. However this prevents using traceback feature in R, making debugging hard. Thus I now enabled debugging mode via a new argument: use_tryCatch=FALSE.

• finemap_dat: Fine-mapping results from all selected methods merged with the original summary statistics (i.e. Multi-finemap results).
• locus_plot: A nested list containing one or more zoomed views of locus plots.
• LD_matrix: The post-processed LD matrix used for fine-mapping.
• LD_plot: An LD plot (if used).
• locus_dir: Locus directory results are saved in.
• arguments: A record of the arguments supplied to finemap_loci.

In addition, the following object summarizes the results from the locus-specific elements:

• merged_dat: A merged data.table with all fine-mapping results from all loci.

The main output of echolocatoR are the multi-finemap files (for example, echodata::BST1). They are stored in the locus-specific Multi-finemap subfolders.

• Standardized GWAS/QTL summary statistics: e.g. SNP,CHR,POS,Effect,StdErr. See ?finemap_loci() for descriptions of each.
• leadSNP: The designated proxy SNP per locus, which is the SNP with the smallest p-value by default.
• <tool>.CS: The 95% probability Credible Set (CS) to which a SNP belongs within a given fine-mapping tool’s results. If a SNP is not in any of the tool’s CS, it is assigned NA (or 0 for the purposes of plotting).
• <tool>.PP: The posterior probability that a SNP is causal for a given GWAS/QTL trait.
• Support: The total number of fine-mapping tools that include the SNP in its CS.
• Consensus_SNP: By default, defined as a SNP that is included in the CS of more than N fine-mapping tool(s), i.e. Support>1 (default: N=1).
• mean.PP: The mean SNP-wise PP across all fine-mapping tools used.
• mean.CS: If mean PP is greater than the 95% probability threshold (mean.PP>0.95) then mean.CS is 1, else 0. This tends to be a very stringent threshold as it requires a high degree of agreement between fine-mapping tools.

• Separate multi-finemap files are generated for each LD reference panel used, which is included in the file name (e.g. UKB_LD.Multi-finemap.tsv.gz).
• Each fine-mapping tool defines its CS and PP slightly differently, so please refer to the associated original publications for the exact details of how these are calculated (links provided above).

• echolocatoR will automatically check whether you have the necessary columns to run each tool you selected in echolocatoR::finemap_loci(finemap_methods=...). It will remove any tools that for which there are missing necessary columns, and produces a message letting you know which columns are missing.
• Note that some columns (e.g. MAF,N,t-stat) will be automatically inferred if missing.
• For easy reference, we list the necessary columns here as well.
  See ?echodata::construct_colmap() for descriptions of these columns.
  All methods require the columns: SNP,CHR,POS,Effect,StdErr

• You can search, import, and standardize any GWAS in the Open GWAS database via MungeSumstats, specifically the functions find_sumstats and import_sumstats.

• API access to full summary statistics from many standardized e/s/t-QTL datasets.
• Data access and colocalization tests facilitated through the catalogueR R package.

• You can visit the echolocatoR Fine-mapping Portal to interactively visualize and download pre-computed fine-mapping results across a variety of phenotypes.
• This data can be searched and imported programmatically using echodata::portal_query().

• Data from this publication contains results from cell type-specific (neurons, oligodendrocytes, astrocytes, microglia, & peripheral myeloid cells) epigenomic assays (H3K27ac, ATAC, H3K4me3) from ex vivo pediatric human brain tissue.

• Data from this publication contains results from single-cell and bulk chromatin accessibility assays ([sc]ATAC-seq) and chromatin interactions ( FitHiChIP) from postmortem adult human brain tissue.

• API access to a diverse library of cell type/line-specific epigenomic (e.g. ENCODE) and other genome-wide annotations.

• API access to cell type-specific epigenomic data.

• API access to various genome-wide SNP annotations (e.g. missense, nonsynonmous, intronic, enhancer).

• API access to known per-SNP QTL and epigenomic data hits.

• Binomial enrichment tests between customisable foreground and background SNPs.

• Identification of transcript factor binding motifs (TFBM) and prediction of SNP disruption to said motifs.
• Includes a comprehensive list of TFBM databases via MotifDB (9,900+ annotated position frequency matrices from 14 public sources, for multiple organisms).

• Iterative pairwise permutation testing of overlap between all combinations of two GRangesList objects.

• Genomic enrichment with LD-informed heuristics.

• LD-informed iterative enrichment analysis.

• Genome-wide stratified LD score regression.
• Inlccles 187-annotation baseline model from Gazal et al. 2018.
• You can alternatively supply a custom annotations matrix.

• From user-supplied VCFs

• From user-supplied precomputed LD matrices

• echotabix::convert_and_query() detects whether the GWAS summary statistics file you provided is already tabix-indexed, and it not, automatically performs all steps necessary to convert it (sorting, bgzip-compression, indexing) across a wide variety of scenarios.
• echotabix::query() contains many different methods for making tabix queries (e.g. Rtracklayer,echoconda,VariantAnnotation,seqminer), each of which fail in certain circumstances. To avoid this, query() automatically selects the method that will work for the particular file being queried and your machine’s particular versions of R/Bioconductor/OS, taking the guesswork and troubleshooting out of tabix queries.

• Multi-threaded downloading is performed using axel, and is particularly useful for speeding up downloads of large files.
• axel is installed via the official echoverse conda environment: “echoR_mini”. This environment is automatically created by the function echoconda::yaml_to_env() when needed.