echofinemap: Getting Started
Author: Brian M. Schilder
Author: Brian M. Schilder
Updated: Mar-16-2026
Source: Updated: Mar-16-2026
vignettes/echofinemap.Rmd
echofinemap.RmdQuick start: ABF (Approximate Bayes Factor)
ABF is the fastest fine-mapping method because it requires no LD matrix and no external software. It is a good first pass for identifying credible sets.
## Load bundled example data
dat <- echodata::BST1
LD_matrix <- echodata::BST1_LD_matrix
## Strip any previous fine-mapping columns so we start fresh
dat_clean <- echofinemap::drop_finemap_cols(dat = dat)## Gathering method sources.## Gathering method citations.## Dropping 11 pre-existing fine-mapping columns.
## Run ABF
dat_abf <- echofinemap::ABF(dat = dat_clean, verbose = TRUE)## Loading required namespace: MungeSumstats## Preparing sample size column (N).## Computing effective sample size using the LDSC method:
## 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)]))## + Mapping colnames from MungeSumstats ==> echolocatoR## Running ABF.## Warning in .maybe_warn_merge_dots(...): merge.data.table() received 1 unknown
## keyword argument which will be ignored: [on]Inspect the results – ABF adds posterior probability columns and a credible set indicator:
## Key fine-mapping columns added by ABF
abf_cols <- grep("ABF|PP|CS", names(dat_abf), value = TRUE)
print(abf_cols)## [1] "mean.CS" "PP" "CS"
## Top SNPs by ABF posterior probability
topsnps <- head(dat_abf[order(-dat_abf$PP),
c("SNP","CHR","POS","P","PP","CS")], 10)
print(topsnps)## Key: <SNP>
## SNP CHR POS P PP CS
## <char> <int> <int> <num> <num> <num>
## 1: rs4698412 4 15737348 2.058e-28 3.870528e-01 0
## 2: rs11724635 4 15737101 2.832e-28 3.064438e-01 0
## 3: rs4698413 4 15737882 3.849e-28 2.427324e-01 0
## 4: rs34559912 4 15730146 1.213e-27 6.211324e-02 0
## 5: rs4613561 4 15737890 1.039e-25 1.411516e-03 0
## 6: rs6449168 4 15727713 1.766e-24 1.298016e-04 0
## 7: rs4266290 4 15737118 3.111e-24 4.631918e-05 0
## 8: rs4403048 4 15737120 3.057e-24 4.631918e-05 0
## 9: rs4263397 4 15739390 1.175e-23 2.058995e-05 0
## 10: rs4631042 4 15712550 8.208e-23 1.318113e-06 0Full pipeline: multifinemap()
multifinemap() runs one or more fine-mapping methods in
a single call. The example below runs ABF, SUSIE, and FINEMAP
together.
Note: This chunk is set to
eval=FALSEbecause SUSIE and FINEMAP can be slow and FINEMAP requires an external binary. See the individual method help pages for installation instructions.
dat_clean <- echofinemap::drop_finemap_cols(dat = echodata::BST1)
LD_matrix <- echodata::BST1_LD_matrix
locus_dir <- file.path(tempdir(), echodata::locus_dir)
dat2 <- echofinemap::multifinemap(
dat = dat_clean,
locus_dir = locus_dir,
LD_matrix = LD_matrix,
finemap_methods = c("ABF", "SUSIE", "FINEMAP")
)Session Info
utils::sessionInfo()## R Under development (unstable) (2026-03-12 r89607)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.4 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: UTC
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] echofinemap_1.0.0 BiocStyle_2.39.0
##
## loaded via a namespace (and not attached):
## [1] splines_4.6.0 aws.s3_0.3.22
## [3] BiocIO_1.21.0 bitops_1.0-9
## [5] filelock_1.0.3 tibble_3.3.1
## [7] R.oo_1.27.1 cellranger_1.1.0
## [9] basilisk.utils_1.23.1 graph_1.89.1
## [11] rpart_4.1.24 XML_3.99-0.22
## [13] lifecycle_1.0.5 mixsqp_0.3-54
## [15] OrganismDbi_1.53.2 ensembldb_2.35.0
## [17] lattice_0.22-9 MASS_7.3-65
## [19] backports_1.5.0 magrittr_2.0.4
## [21] Hmisc_5.2-5 openxlsx_4.2.8.1
## [23] sass_0.4.10 rmarkdown_2.30
## [25] jquerylib_0.1.4 yaml_2.3.12
## [27] otel_0.2.0 zip_2.3.3
## [29] ggbio_1.59.0 reticulate_1.45.0
## [31] gld_2.6.8 DBI_1.3.0
## [33] RColorBrewer_1.1-3 abind_1.4-8
## [35] expm_1.0-0 GenomicRanges_1.63.1
## [37] purrr_1.2.1 R.utils_2.13.0
## [39] AnnotationFilter_1.35.0 biovizBase_1.59.0
## [41] BiocGenerics_0.57.0 RCurl_1.98-1.17
## [43] nnet_7.3-20 VariantAnnotation_1.57.1
## [45] IRanges_2.45.0 S4Vectors_0.49.0
## [47] echoLD_1.0.0 irlba_2.3.7
## [49] pkgdown_2.2.0 echodata_1.0.0
## [51] piggyback_0.1.5 codetools_0.2-20
## [53] DelayedArray_0.37.0 DT_0.34.0
## [55] xml2_1.5.2 tidyselect_1.2.1
## [57] UCSC.utils_1.7.1 farver_2.1.2
## [59] viridis_0.6.5 matrixStats_1.5.0
## [61] stats4_4.6.0 base64enc_0.1-6
## [63] Seqinfo_1.1.0 echotabix_1.0.1
## [65] GenomicAlignments_1.47.0 jsonlite_2.0.0
## [67] e1071_1.7-17 Formula_1.2-5
## [69] survival_3.8-6 systemfonts_1.3.2
## [71] tools_4.6.0 ragg_1.5.1
## [73] DescTools_0.99.60 Rcpp_1.1.1
## [75] glue_1.8.0 gridExtra_2.3
## [77] SparseArray_1.11.11 xfun_0.56
## [79] MatrixGenerics_1.23.0 GenomeInfoDb_1.47.2
## [81] dplyr_1.2.0 withr_3.0.2
## [83] BiocManager_1.30.27 fastmap_1.2.0
## [85] basilisk_1.23.0 boot_1.3-32
## [87] digest_0.6.39 R6_2.6.1
## [89] colorspace_2.1-2 textshaping_1.0.5
## [91] dichromat_2.0-0.1 RSQLite_2.4.6
## [93] cigarillo_1.1.0 R.methodsS3_1.8.2
## [95] tidyr_1.3.2 generics_0.1.4
## [97] data.table_1.18.2.1 rtracklayer_1.71.3
## [99] class_7.3-23 httr_1.4.8
## [101] htmlwidgets_1.6.4 S4Arrays_1.11.1
## [103] pkgconfig_2.0.3 gtable_0.3.6
## [105] Exact_3.3 blob_1.3.0
## [107] S7_0.2.1 XVector_0.51.0
## [109] echoconda_1.0.0 htmltools_0.5.9
## [111] susieR_0.14.2 bookdown_0.46
## [113] RBGL_1.87.0 ProtGenerics_1.43.0
## [115] scales_1.4.0 Biobase_2.71.0
## [117] lmom_3.2 png_0.1-8
## [119] knitr_1.51 rstudioapi_0.18.0
## [121] reshape2_1.4.5 tzdb_0.5.0
## [123] rjson_0.2.23 checkmate_2.3.4
## [125] curl_7.0.0 proxy_0.4-29
## [127] cachem_1.1.0 stringr_1.6.0
## [129] rootSolve_1.8.2.4 parallel_4.6.0
## [131] foreign_0.8-91 AnnotationDbi_1.73.0
## [133] restfulr_0.0.16 desc_1.4.3
## [135] pillar_1.11.1 grid_4.6.0
## [137] reshape_0.8.10 vctrs_0.7.1
## [139] cluster_2.1.8.2 htmlTable_2.4.3
## [141] evaluate_1.0.5 readr_2.2.0
## [143] GenomicFeatures_1.63.1 mvtnorm_1.3-5
## [145] cli_3.6.5 compiler_4.6.0
## [147] Rsamtools_2.27.1 rlang_1.1.7
## [149] crayon_1.5.3 aws.signature_0.6.0
## [151] ieugwasr_1.1.0 plyr_1.8.9
## [153] forcats_1.0.1 fs_1.6.7
## [155] stringi_1.8.7 coloc_5.2.3
## [157] echoannot_1.0.1 viridisLite_0.4.3
## [159] BiocParallel_1.45.0 Biostrings_2.79.5
## [161] lazyeval_0.2.2 Matrix_1.7-4
## [163] downloadR_1.0.0 dir.expiry_1.19.0
## [165] MungeSumstats_1.19.5 BSgenome_1.79.1
## [167] patchwork_1.3.2 hms_1.1.4
## [169] bit64_4.6.0-1 ggplot2_4.0.2
## [171] KEGGREST_1.51.1 SummarizedExperiment_1.41.1
## [173] haven_2.5.5 memoise_2.0.1
## [175] snpStats_1.61.1 bslib_0.10.0
## [177] bit_4.6.0 readxl_1.4.5