2. Query eQTL Catalogue datasets by region

2.2 Method 2: RESTful API Slower than tabix (unless you're only querying several specific SNPs).

fetch_restAPI(
  unique_id,
  quant_method = "ge",
  infer_region = T,
  gwas_data = NULL,
  chrom = NULL,
  bp_lower = NULL,
  bp_upper = NULL,
  is_gwas = F,
  size = NULL,
  verbose = T
)

Arguments

quant_method

eQTL Catalogue actually contains more than just eQTL data. For each dataset, the following kinds of QTLs can be queried: gene expression QTL quant_method="ge" (default) or quant_method="microarray", depending on the dataset. catalogueR will automatically select whichever option is available. exon expression QTL *under construction* quant_method="ex" transcript usage QTL *under construction* quant_method="tx" promoter, splice junction and 3' end usage QTL *under construction* quant_method="txrev"

verbose

Show more (=T) or fewer (=F) messages.

See also

Other eQTL Catalogue: BST1__Alasoo_2018.macrophage_IFNg, LRRK2__Alasoo_2018.macrophage_IFNg, MEX3C__Alasoo_2018.macrophage_IFNg, eQTL_Catalogue.fetch(), eQTL_Catalogue.header, eQTL_Catalogue.iterate_fetch(), eQTL_Catalogue.query(), eQTL_Catalogue.search_metadata(), fetch_tabix(), merge_gwas_qtl(), meta

Examples

data("meta"); data("BST1");
qtl.subset <- fetch_restAPI(unique_id=meta$unique_id[1], gwas_data=BST1)
#> [1] "+ Inferring coordinates from gwas_data"
#> + eQTL_Catalogue:: Fetching: 
#> http://www.ebi.ac.uk/eqtl/api/chromosomes/4/associations?paginate=False&study=Alasoo_2018&qtl_group=macrophage_naive&quant_method=ge&bp_lower=14737349&bp_upper=16737284
#> [1] "+ eQTL_Catalogue:: 77672 SNPs returned in 47.3 seconds."