finemap_locus: Run 'echolocatoR' pipeline on a single locus

In RajLabMSSM/echolocatoR: Automated genomic fine-mapping

Run echolocatoR pipeline on a single locus

Description

Unlike finemap_loci, you don't need to provide a topSNPs data.frame. Instead, just manually provide the coordinates of the locus you want to fine-map.

Usage

finemap_locus(
  locus,
  fullSS_path,
  fullSS_genome_build = NULL,
  results_dir = file.path(tempdir(), "results"),
  dataset_name = "dataset_name",
  dataset_type = "GWAS",
  case_control = TRUE,
  topSNPs = "auto",
  force_new_subset = FALSE,
  force_new_LD = FALSE,
  force_new_finemap = FALSE,
  finemap_methods = c("ABF", "FINEMAP", "SUSIE"),
  finemap_args = NULL,
  n_causal = 5,
  credset_thresh = 0.95,
  consensus_thresh = 2,
  fillNA = 0,
  conditioned_snps = NULL,
  priors_col = NULL,
  munged = FALSE,
  colmap = echodata::construct_colmap(munged = munged),
  compute_n = "ldsc",
  LD_reference = "1KGphase3",
  LD_genome_build = "hg19",
  leadSNP_LD_block = FALSE,
  superpopulation = "EUR",
  download_method = "axel",
  bp_distance = 5e+05,
  min_POS = NA,
  max_POS = NA,
  min_MAF = NA,
  trim_gene_limits = FALSE,
  max_snps = NULL,
  min_r2 = 0,
  remove_variants = FALSE,
  remove_correlates = FALSE,
  query_by = "tabix",
  qtl_suffixes = NULL,
  plot_types = c("simple"),
  zoom = "1x",
  show_plot = TRUE,
  tx_biotypes = NULL,
  nott_epigenome = FALSE,
  nott_show_placseq = FALSE,
  nott_binwidth = 200,
  nott_bigwig_dir = NULL,
  xgr_libnames = NULL,
  roadmap = FALSE,
  roadmap_query = NULL,
  remove_tmps = TRUE,
  seed = 2022,
  conda_env = "echoR_mini",
  nThread = 1,
  verbose = TRUE,
  top_SNPs = deprecated(),
  PP_threshold = deprecated(),
  consensus_threshold = deprecated(),
  plot.Nott_epigenome = deprecated(),
  plot.Nott_show_placseq = deprecated(),
  plot.Nott_binwidth = deprecated(),
  plot.Nott_bigwig_dir = deprecated(),
  plot.Roadmap = deprecated(),
  plot.Roadmap_query = deprecated(),
  plot.XGR_libnames = deprecated(),
  server = deprecated(),
  plot.types = deprecated(),
  plot.zoom = deprecated(),
  QTL_prefixes = deprecated(),
  vcf_folder = deprecated(),
  probe_path = deprecated(),
  file_sep = deprecated,
  chrom_col = deprecated(),
  chrom_type = deprecated(),
  position_col = deprecated(),
  snp_col = deprecated(),
  pval_col = deprecated(),
  effect_col = deprecated(),
  stderr_col = deprecated(),
  tstat_col = deprecated(),
  locus_col = deprecated(),
  freq_col = deprecated(),
  MAF_col = deprecated(),
  A1_col = deprecated(),
  A2_col = deprecated(),
  gene_col = deprecated(),
  N_cases_col = deprecated(),
  N_controls_col = deprecated(),
  N_cases = deprecated(),
  N_controls = deprecated(),
  proportion_cases = deprecated(),
  sample_size = deprecated(),
  PAINTOR_QTL_datasets = deprecated()
)

Arguments

locus	Locus name to fine-map (e.g. "BIN1"). Can be named to indicate a specific gene within a QTL locus (e.g. c(ENSG00000136731="BIN1")).
fullSS_path	Path to the full summary statistics file (GWAS or QTL) that you want to fine-map. It is usually best to provide the absolute path rather than the relative path.
fullSS_genome_build	Genome build of the full summary statistics (fullSS_path). Can be "GRCH37" or "GRCH38" or one of their synonyms.. If fullSS_genome_build==NULL and munged=TRUE, infers genome build (hg19 vs. hg38) from summary statistics using get_genome_builds.
results_dir	Where to store all results. IMPORTANT!: It is usually best to provide the absolute path rather than the relative path. This is especially important for FINEMAP.
dataset_name	The name you want to assign to the dataset being fine-mapped, This will be used to name the subdirectory where your results will be stored (e.g. Data/GWAS/<dataset_name>). Don't use special characters (e.g.".", "/").
dataset_type	The kind dataset you're fine-mapping (e.g. GWAS, eQTL, tQTL). This will also be used when creating the subdirectory where your results will be stored (e.g. Data/<dataset_type>/Kunkle_2019).
case_control	Whether the summary statistics come from a case-control study (e.g. a GWAS of having Alzheimer's Disease or not) (TRUE) or a quantitative study (e.g. a GWAS of height, or an eQTL) (FALSE).
topSNPs	A data.frame with the genomic coordinates of the lead SNP for each locus. The lead SNP will be used as the center of the window when extracting subset from the full GWAS/QTL summary statistics file. Only one SNP per Locus should be included. At minimum, topSNPs should include the following columns: Locus A unique name for each locus. Often, loci are named after a relevant gene (e.g. LRRK2) or based on the name/coordinates of the lead SNP (e.g. locus_chr12_40734202) CHR The chromosome that the SNP is on. Can be "chr12" or "12" format. POS The genomic position of the SNP (in basepairs)
force_new_subset	By default, if a subset of the full summary stats file for a given locus is already present, then echolocatoR will just use the pre-existing file. Set force_new_subset=T to override this and extract a new subset. Subsets are saved in the following path structure: Data/\<dataset_type\>/\<dataset_name\>/\<locus\>/Multi-finemap/ \<locus\>_\<dataset_name\>_Multi-finemap.tsv.gz
force_new_LD	Force new LD subset.
force_new_finemap	By default, if an fine-mapping results file for a given locus is already present, then echolocatoR will just use the preexisting file. Set force_new_finemap=T to override this and re-run fine-mapping.
finemap_methods	Which fine-mapping methods you want to use.
finemap_args	A named nested list containing additional arguments for each fine-mapping method. e.g. finemap_args = list(FINEMAP=list(), PAINTOR=list(method=""))
n_causal	The maximum number of potential causal SNPs per locus. This parameter is used somewhat differently by different fine-mapping tools. See tool-specific functions for details.
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.
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.
fillNA	Value to fill LD matrix NAs with.
conditioned_snps	Which SNPs to conditions on when fine-mapping with (e.g. COJO).
priors_col	[Optional] Name of the a column in dat to extract SNP-wise prior probabilities from.
munged	Whether fullSS_path have already been standardised/filtered full summary stats with format_sumstats. If munged=FALSE you'll need to provide the necessary column names to the colmap argument.
colmap	Column name mappings in in fullSS_path. Must be a named list. Can use construct_colmap to assist with this. This function can be used in two different ways: munged=FALSE : When munged=FALSE, you will need to provide the necessary column names to the colmap argument (default). munged=TRUE : Alternatively, instead of filling out each argument in construct_colmap, you can simply set munged=TRUE if fullSS_path has already been munged with format_sumstats.
compute_n	How to compute per-SNP sample size (new column "N"). If the column "N" is already present in dat, this column will be used to extract per-SNP sample sizes and the argument compute_n will be ignored. If the column "N" is not present in dat, one of the following options can be supplied to compute_n: 0: N will not be computed. >0: If any number >0 is provided, that value will be set as N for every row. **Note**: Computing N this way is incorrect and should be avoided if at all possible. "sum": N will be computed as: cases (N_CAS) + controls (N_CON), so long as both columns are present. "ldsc": N will be computed as effective sample size: Neff =(N_CAS+N_CON)*(N_CAS/(N_CAS+N_CON)) / mean((N_CAS/(N_CAS+N_CON))(N_CAS+N_CON)==max(N_CAS+N_CON)). "giant": N will be computed as effective sample size: Neff = 2 / (1/N_CAS + 1/N_CON). "metal": N will be computed as effective sample size: Neff = 4 / (1/N_CAS + 1/N_CON).
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.
LD_genome_build	Genome build of the LD panel. This is automatically assigned to the correct genome build for each LD panel except when the user supplies custom vcf/LD files.
leadSNP_LD_block	Only return SNPs within the same LD block as the lead SNP (the SNP with the smallest p-value).
superpopulation	Superpopulation to subset LD panel by (used only if LD_reference is "1KGphase1" or "1KGphase3"). See popDat_1KGphase1 and popDat_1KGphase3 for full tables of their respective samples.
download_method	"axel" : Multi-threaded "wget" : Single-threaded "download.file" : Single-threaded "internal" : Single-threaded (passed to download.file) "wininet" : Single-threaded (passed to download.file) "libcurl" : Single-threaded (passed to download.file) "curl" : Single-threaded (passed to download.file)
bp_distance	Distance around the lead SNP to include.
min_POS	Minimum genomic position to include.
max_POS	Maximum genomic position to include.
min_MAF	Minimum Minor Allele Frequency (MAF) of SNPs to include.
trim_gene_limits	If a gene name is supplied to this argument (e.g. trim_gene_limits="BST"), only SNPs within the gene body will be included.
max_snps	Maximum number of SNPs to include.
min_r2	Correlation threshold for remove_correlates.
remove_variants	A list of SNP RSIDs to remove.
remove_correlates	A list of SNPs. If provided, all SNPs that correlates with these SNPs (at r2>=min_r2) will be removed from both dat and LD list items..
query_by	Choose which method you want to use to extract locus subsets from the full summary stats file. Methods include: "tabix" Convert the full summary stats file in an indexed tabix file. Makes querying lightning fast after the initial conversion is done. (default) "coordinates" Extract locus subsets using min/max genomic coordinates with awk.
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").
plot_types	Which kinds of plots to include. Options: "simple"Just plot the following tracks: GWAS, fine-mapping, gene models "fancy"Additionally plot XGR annotation tracks (XGR, Roadmap, Nott2019). ' "LD"LD heatmap showing the 10 SNPs surrounding the lead SNP.
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).
show_plot	Print plot to screen.
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
nott_epigenome	Include tracks showing brain cell-type-specific epigenomic data from Nott et al. (2019).
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.
xgr_libnames	Passed to XGR_plot. Which XGR annotations to check overlap with. For full list of libraries see here. Passed to the RData.customised argument in xRDataLoader. Examples: "ENCODE_TFBS_ClusteredV3_CellTypes" "ENCODE_DNaseI_ClusteredV3_CellTypes" "Broad_Histone"
roadmap	Find and plot annotations from Roadmap.
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")).
remove_tmps	Whether to remove any temporary files (e.g. FINEMAP output files) after the pipeline is done running.
seed	Set the seed for all functions where this is possible.
conda_env	Conda environment to use.
nThread	Number of threads to parallelise saving across.
verbose	Print messages.
top_SNPs	[deprecated]
PP_threshold	[deprecated]
consensus_threshold	[deprecated]
plot.Nott_epigenome	[deprecated]
plot.Nott_show_placseq	[deprecated]
plot.Nott_binwidth	[deprecated]
plot.Nott_bigwig_dir	[deprecated]
plot.Roadmap	[deprecated]
plot.Roadmap_query	[deprecated]
plot.XGR_libnames	[deprecated]
server	[deprecated]
plot.types	[deprecated]
plot.zoom	[deprecated]
QTL_prefixes	[deprecated]
vcf_folder	[deprecated]
probe_path	[deprecated]
file_sep	[deprecated]
chrom_col	[deprecated]
chrom_type	[deprecated]
position_col	[deprecated]
snp_col	[deprecated]
pval_col	[deprecated]
effect_col	[deprecated]
stderr_col	[deprecated]
tstat_col	[deprecated]
locus_col	[deprecated]
freq_col	[deprecated]
MAF_col	[deprecated]
A1_col	[deprecated]
A2_col	[deprecated]
gene_col	[deprecated]
N_cases_col	[deprecated]
N_controls_col	[deprecated]
N_cases	[deprecated]
N_controls	[deprecated]
proportion_cases	[deprecated]
sample_size	[deprecated]
PAINTOR_QTL_datasets	[deprecated]

Details

The primary functions of echolocatoR that expedite fine-mapping by wrapping many other echolocatoR functions into one. Encompasses steps including:

Subset & standardize

Extract subsets of the full summary stats GWAS or QTL file and reformat them to be compatible with echolocatoR's various functions

Calculate linkage disequilibrium

Download and prepare the necessary LD matrix.

Fine-map

Run various fine-mapping tools and merge the results into a single multi-finemap data.frame.

Plot

Summarise the results in a multi-track plot for each locus.

See Also

Other MAIN: finemap_loci()

Examples

topSNPs <- echodata::topSNPs_Nalls2019
fullSS_path <- echodata::example_fullSS(dataset = "Nalls2019")

res <- echolocatoR::finemap_locus(
  fullSS_path = fullSS_path,
  topSNPs = topSNPs,
  locus = "BST1",
  finemap_methods = c("ABF","FINEMAP","SUSIE"),
  dataset_name = "Nalls23andMe_2019",
  fullSS_genome_build = "hg19",
  bp_distance = 1000,
  munged = TRUE)

RajLabMSSM/echolocatoR documentation built on Jan. 29, 2023, 6:04 a.m.