vignettes/echolocatoR_Finemapping_Portal.Rmd
echolocatoR_Finemapping_Portal.RmdThe *echolocatoR.
The following functions provides API access to the fine-mapping results, pre-computed LD matrices, and plots available on the echolocatoR Fine-mapping Portal.
Peruse the metadata to see the available data types (e.g. “GWAS”, “QTL”), datasets (e.g. “Ripke_2014”, “Wray_2018”), and phenotypes (e.g. “Schizophrenia”, “Major Depressive Disorder”).
meta <- echodata::portal_metadata()
knitr::kable(meta)| dataset_type | dataset | phenotype | prop_cases | build | reference |
|---|---|---|---|---|---|
| GWAS | Ripke_2014 | Schizophrenia | 0.2460 | hg19 | https://www.nature.com/articles/nature13595 |
| GWAS | Wray_2018 | Major Depressive Disorder | 0.2820 | hg19 | https://www.nature.com/articles/s41588-018-0090-3 |
| GWAS | IMSGC_2019 | Multiple Sclerosis | 0.4090 | hg19 | https://science.sciencemag.org/content/365/6460/eaav7188 |
| GWAS | Stahl_2019 | Bipolar Disorder | 0.3950 | hg19 | https://www.nature.com/articles/s41588-019-0397-8 |
| GWAS | Daner_2020 | Bipolar Disorder | 0.1010 | hg19 | https://www.medrxiv.org/content/10.1101/2020.09.17.20187054v1 |
| GWAS | Nalls23andMe_2019 | Parkinson’s Disease | 0.0386 | hg19 | https://www.biorxiv.org/content/10.1101/388165v3 |
| GWAS | Lambert_2013 | Alzheimer’s Disease | 0.3450 | hg19 | https://www.nature.com/articles/ng.2802 |
| GWAS | Marioni_2018 | Alzheimer’s Disease | 0.1740 | hg19 | https://www.nature.com/articles/s41398-018-0150-6 |
| GWAS | Jansen_2018 | Alzheimer’s Disease | 0.1570 | hg19 | https://www.nature.com/articles/s41588-018-0311-9 |
| GWAS | Kunkle_2019 | Alzheimer’s Disease | 0.3700 | hg19 | https://www.nature.com/articles/s41588-019-0358-2 |
| QTL | Microglia_all_regions | eQTL | NA | hg38 | https://www.biorxiv.org/content/10.1101/2020.10.27.356113v1 |
Query and download data from the echolocatoR Fine-mapping Portal.
portal_query will return a list of paths where each file
has been downloaded locally, in a hierarchical folder structure
(i.e. dataset_type --> dataset --> locus --> data_types
)
results_dir <- tempdir()
local_files <- echodata::portal_query(dataset_types="GWAS",
phenotypes = c("schizophrenia",
"parkinson"),
file_types = c("multi_finemap","LD"),
loci = c("BST1","CHRNB1","LRRK2"),
LD_panels = "UKB",
results_dir = results_dir)
knitr::kable(utils::head(local_files))Next, we can gather all of the fine-mapping results generated by
finemap_loci() previously.merge_finemapping_results recursively searches for the
correct files within a hierarchical folder structure and imports only
the multi-finemap files.
merged_DT <- echodata::merge_finemapping_results(dataset = results_dir,
minimum_support = 0,
include_leadSNPs = TRUE,
consensus_thresh = 2)
echodata::results_report(merged_DT)| Dataset | Locus | SNP | CHR | POS | P | Effect | StdErr | A1 | A2 | Freq | MAF | N_cases | N_controls | proportion_cases | N | t_stat | leadSNP | ABF.CS | ABF.PP | SUSIE.CS | SUSIE.PP | POLYFUN_SUSIE.CS | POLYFUN_SUSIE.PP | FINEMAP.CS | FINEMAP.PP | Support | Consensus_SNP | mean.PP | mean.CS | Mb |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Nalls23andMe_2019 | BST1 | rs4541502 | 4 | 15712787 | 0 | -0.0897 | 0.0093 | T | G | 0.4749 | 0.4749 | 56306 | 1417791 | 0.0382 | 216621 | -9.645161 | FALSE | NA | NA | 2 | 1 | 2 | 1 | 1 | 1 | 3 | TRUE | 0.75 | 0 | 15.712787 |
| Nalls23andMe_2019 | CHRNB1 | rs12600861 | 17 | 7355621 | 0 | -0.0565 | 0.0099 | A | C | 0.6484 | 0.3516 | 56306 | 1417791 | 0.0382 | 216621 | -5.707071 | TRUE | NA | NA | 3 | 1 | 2 | 1 | 1 | 1 | 3 | TRUE | 0.75 | 0 | 7.355621 |
| Nalls23andMe_2019 | LRRK2 | rs7294619 | 12 | 40617202 | 0 | -0.1276 | 0.0140 | T | C | 0.8783 | 0.1217 | 56306 | 1417791 | 0.0382 | 216621 | -9.114286 | FALSE | NA | NA | 2 | 1 | 2 | 1 | 1 | 1 | 3 | TRUE | 0.75 | 0 | 40.617202 |
| Nalls23andMe_2019 | LRRK2 | rs76904798 | 12 | 40614434 | 0 | 0.1439 | 0.0130 | T | C | 0.1444 | 0.1444 | 56306 | 1417791 | 0.0382 | 216621 | 11.069231 | TRUE | NA | NA | 1 | 1 | 1 | 1 | 1 | 1 | 3 | TRUE | 0.75 | 0 | 40.614434 |
| Nalls23andMe_2019 | BST1 | rs34559912 | 4 | 15730146 | 0 | 0.1030 | 0.0095 | T | G | 0.5526 | 0.4474 | 56306 | 1417791 | 0.0382 | 216621 | 10.842105 | FALSE | NA | NA | 3 | 1 | 3 | 1 | NA | NA | 2 | TRUE | 0.50 | 0 | 15.730146 |
| Nalls23andMe_2019 | BST1 | rs4389574 | 4 | 15730398 | 0 | -0.0977 | 0.0116 | A | G | 0.4443 | 0.4443 | 42598 | 1322509 | 0.0312 | 165075 | -8.422414 | FALSE | NA | NA | 1 | 1 | 1 | 1 | NA | NA | 2 | TRUE | 0.50 | 0 | 15.730398 |
Next, we import the a subset of the LD matrices for only the lead SNP.
ld_files <- local_files[file_type=="LD",]
ld_matrices <- lapply(stats::setNames(ld_files$path_local,
ld_files$locus),
function(x){
data.table::fread(x)
})
knitr::kable(utils::head(ld_matrices[[1]]))| SNP | rs4698412 | rs4698412.1 |
|---|---|---|
| rs61337515 | 0.0022108 | 0.0022108 |
| rs58950976 | 0.0149364 | 0.0149364 |
| rs13152654 | -0.0047314 | -0.0047314 |
| rs13103770 | -0.0037451 | -0.0037451 |
| rs6837632 | 0.0061673 | 0.0061673 |
| rs114563990 | -0.0004498 | -0.0004498 |
utils::sessionInfo()## R Under development (unstable) (2023-11-08 r85496)
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