Exploring fine-mapping results
Author: Brian M. Schilder
Author: Brian M. Schilder
Updated: Mar-16-2026
Source: Updated: Mar-16-2026
vignettes/explore_results.Rmd
explore_results.Rmd## ## ββ π¦ π¦ π¦ e c h o l o c a t o R π¦ π¦ π¦ βββββββββββββββββββββββββββββββββ## ## ββ v2.0.5 ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ## ## ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ## β β β ‘β£β£β β β ‘β£β£β β β ’β£β‘ β β β ’β£β‘ β β β ’β£β‘ β β β ’β£β‘ β β β ’β£β‘ β β β ’β£β‘
## β β’β‘β β£β β’β‘β β£β β’β‘β β£β β’β‘β β£β β’β‘β β£β β’β‘β β£β β’β‘β β£β β’β‘β ‘β£
## β β‘β‘β’β’β β‘β‘β’β’β β‘β‘β’β’β β‘β‘β’β’β ‘β β‘β €β’β ‘β β‘β €β’β ‘β β‘β‘ β’β’β β‘β‘β’
## β β‘β‘β’β’β β‘β‘β’β’β β‘β‘β’β’β β‘β‘β’β’β ‘β β‘β €β’β ‘β β‘β €β’β ‘β β‘β‘ β’β’β β‘β‘β’
## β β’β‘β β£β β’β‘β β£β β’β‘β β£β β’β‘β β£β β’β‘β β£β β’β‘β β£β β’β‘β β£β β’β‘β ‘β£
## β β β ‘β£β£β β β ‘β£β£β β β ’β£β‘ β β β ’β£β‘ β β β ’β£β‘ β β β ’β£β‘ β β β ’β£β‘ β β β ’β£β‘
## β If you use echolocatoR or any of the echoverse subpackages, please cite:
## βΆ Brian M Schilder, Jack Humphrey, Towfique
## Raj (2021) echolocatoR: an automated
## end-to-end statistical and functional
## genomic fine-mapping pipeline,
## Bioinformatics; btab658,
## https://doi.org/10.1093/bioinformatics/btab658
## β Please report any bugs/feature requests on GitHub:
## βΆ
## https://github.com/RajLabMSSM/echolocatoR/issues
## β Contributions are welcome!:
## βΆ
## https://github.com/RajLabMSSM/echolocatoR/pullsOverview
This vignette demonstrates how to explore and interpret the
fine-mapping results produced by finemap_loci(). All
examples use bundled data included in echodata, so
no internet connection is required.
Load example results
echodata ships with pre-computed fine-mapping results
for three Parkinsonβs disease loci from Nalls et al.Β (2019):
## [1] 6216 26Each row is a SNP within the locus window. Key columns include:
-
GWAS columns:
SNP,CHR,POS,P,Effect,StdErr,MAF -
Fine-mapping posterior probabilities:
ABF.PP,SUSIE.PP,FINEMAP.PP -
Credible set assignments:
ABF.CS,SUSIE.CS,FINEMAP.CS -
Consensus columns:
Support(number of methods placing SNP in a CS),Consensus_SNP,mean.PP,mean.CS
## Fine-mapping specific columns
fm_cols <- c("SNP","P","ABF.PP","SUSIE.PP","FINEMAP.PP",
"Support","Consensus_SNP","mean.PP")
knitr::kable(head(dat[order(dat$mean.PP, decreasing = TRUE), ..fm_cols], 10),
digits = 4, row.names = FALSE,
caption = "Top 10 SNPs by mean posterior probability")| SNP | P | ABF.PP | SUSIE.PP | FINEMAP.PP | Support | Consensus_SNP | mean.PP |
|---|---|---|---|---|---|---|---|
| rs4541502 | 0 | 0.0000 | 1 | 1.0000 | 3 | TRUE | 0.7500 |
| rs34559912 | 0 | 0.0621 | 1 | 0.0000 | 2 | TRUE | 0.5155 |
| rs4389574 | 0 | 0.0000 | 1 | 0.0000 | 2 | TRUE | 0.5000 |
| rs4698412 | 0 | 0.3871 | 0 | 1.0000 | 1 | FALSE | 0.3468 |
| rs6852450 | 0 | 0.0000 | 0 | 1.0000 | 1 | FALSE | 0.2500 |
| rs3756246 | 0 | 0.0000 | 0 | 1.0000 | 1 | FALSE | 0.2500 |
| rs35519415 | 0 | 0.0000 | 0 | 0.9994 | 1 | FALSE | 0.2498 |
| rs11724635 | 0 | 0.3064 | 0 | 0.0000 | 0 | FALSE | 0.0766 |
| rs4698413 | 0 | 0.2427 | 0 | 0.0000 | 0 | FALSE | 0.0607 |
| rs4613561 | 0 | 0.0014 | 0 | 0.0000 | 0 | FALSE | 0.0004 |
Consensus SNPs
Consensus SNPs are those placed in a credible set by multiple
fine-mapping methods. The Support column indicates how many
methods agree:
consensus <- subset(dat, Consensus_SNP == TRUE)
knitr::kable(
consensus[, c("SNP","POS","P","ABF.PP","SUSIE.PP","FINEMAP.PP",
"Support","mean.PP")],
digits = 4, row.names = FALSE,
caption = "Consensus fine-mapped SNPs (BST1 locus)"
)| SNP | POS | P | ABF.PP | SUSIE.PP | FINEMAP.PP | Support | mean.PP |
|---|---|---|---|---|---|---|---|
| rs4541502 | 15712787 | 0 | 0.0000 | 1 | 1 | 3 | 0.7500 |
| rs34559912 | 15730146 | 0 | 0.0621 | 1 | 0 | 2 | 0.5155 |
| rs4389574 | 15730398 | 0 | 0.0000 | 1 | 0 | 2 | 0.5000 |
SNP group counts
Summarise how many SNPs fall into each support level:
support_table <- table(Support = dat$Support)
knitr::kable(as.data.frame(support_table),
caption = "SNP counts by support level")| Support | Freq |
|---|---|
| 0 | 6209 |
| 1 | 4 |
| 2 | 2 |
| 3 | 1 |
Credible sets
Each fine-mapping method assigns SNPs to credible sets (CS). A value
of 0 means the SNP is not in any CS; 1 = first
CS, 2 = second CS, etc.
## Count SNPs in each method's credible sets
cs_summary <- data.frame(
Method = c("ABF", "SUSIE", "FINEMAP"),
SNPs_in_CS = c(
sum(dat$ABF.CS > 0, na.rm = TRUE),
sum(dat$SUSIE.CS > 0, na.rm = TRUE),
sum(dat$FINEMAP.CS > 0, na.rm = TRUE)
)
)
knitr::kable(cs_summary, caption = "Number of SNPs in credible sets per method")| Method | SNPs_in_CS |
|---|---|
| ABF | 0 |
| SUSIE | 3 |
| FINEMAP | 5 |
Working with multiple loci
echodata also bundles LRRK2 and MEX3C loci. You can
combine them to simulate multi-locus analysis:
bst1 <- data.table::copy(echodata::BST1); bst1[, Locus := "BST1"]
lrrk2 <- data.table::copy(echodata::LRRK2); lrrk2[, Locus := "LRRK2"]
mex3c <- data.table::copy(echodata::MEX3C); mex3c[, Locus := "MEX3C"]
shared_cols <- Reduce(intersect, list(colnames(bst1), colnames(lrrk2), colnames(mex3c)))
all_loci <- rbind(bst1[, ..shared_cols], lrrk2[, ..shared_cols], mex3c[, ..shared_cols])
## Consensus SNPs per locus
consensus_per_locus <- do.call(rbind, lapply(
split(all_loci, all_loci$Locus),
function(x) data.frame(
Locus = x$Locus[1],
Total_SNPs = nrow(x),
Consensus_SNPs = sum(x$Consensus_SNP, na.rm = TRUE),
Min_P = min(x$P, na.rm = TRUE)
)
))
knitr::kable(consensus_per_locus, row.names = FALSE,
caption = "Summary across bundled loci")| Locus | Total_SNPs | Consensus_SNPs | Min_P |
|---|---|---|---|
| BST1 | 6216 | 3 | 0 |
| LRRK2 | 3844 | 4 | 0 |
| MEX3C | 5372 | 2 | 0 |
LD matrix
A pre-computed LD matrix for BST1 is also available:
ld <- echodata::BST1_LD_matrix
cat("LD matrix dimensions:", dim(ld), "\n")## LD matrix dimensions: 95 95## SNPs in LD matrix: 95
## Show a small corner
knitr::kable(round(ld[1:5, 1:5], 3), caption = "LD matrix (top-left corner)")| rs4698412 | rs6852450 | rs10000232 | rs10000250 | rs10000290 | |
|---|---|---|---|---|---|
| rs4698412 | 1.000 | 0.885 | -0.023 | -0.045 | -0.431 |
| rs6852450 | 0.885 | 1.000 | -0.006 | -0.044 | -0.464 |
| rs10000232 | -0.023 | -0.006 | 1.000 | -0.017 | 0.024 |
| rs10000250 | -0.045 | -0.044 | -0.017 | 1.000 | 0.089 |
| rs10000290 | -0.431 | -0.464 | 0.024 | 0.089 | 1.000 |
Next steps
- Visualize loci:
vignette("plotting") - Run the full pipeline:
vignette("echolocatoR") - Summarise across loci:
vignette("summarise") - Learn about sub-packages:
vignette("echoverse_modules")
Session info
utils::sessionInfo()## R version 4.5.1 (2025-06-13)
## Platform: aarch64-apple-darwin20
## Running under: macOS Tahoe 26.3.1
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.1
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: America/New_York
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
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## other attached packages:
## [1] echolocatoR_2.0.5 BiocStyle_2.38.0
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## [15] pals_1.10 OrganismDbi_1.52.0
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## [43] biovizBase_1.58.0 BiocGenerics_0.56.0
## [45] RCurl_1.98-1.17 nnet_7.3-20
## [47] VariantAnnotation_1.56.0 IRanges_2.44.0
## [49] S4Vectors_0.48.0 echofinemap_1.0.0
## [51] echoLD_0.99.12 catalogueR_2.0.1
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