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Get Independent Eqtl

Source: R/get_independent_eqtl.R
get_independent_eqtl.Rd

Retrieve Independent eQTL Data

  • Finds and returns Independent eQTL Data data for the provided list of genes

  • By default, this endpoint fetches data from the latest GTEx version

The retrieved data is split into pages with items_per_page entries per page

GTEx portal API documentation

Usage

get_independent_eqtl(
  gencodeIds,
  tissueSiteDetailIds = NULL,
  datasetId = "gtex_v8",
  page = 0,
  itemsPerPage = getOption("gtexr.itemsPerPage"),
  .verbose = getOption("gtexr.verbose"),
  .return_raw = FALSE
)

Arguments

gencodeIds

A character vector of Versioned GENCODE IDs, e.g. c("ENSG00000132693.12", "ENSG00000203782.5").

tissueSiteDetailIds

Character vector of IDs for tissues of interest. Can be GTEx specific IDs (e.g. "Whole_Blood"; use get_tissue_site_detail() to see valid values) or Ontology IDs.

datasetId

String. Unique identifier of a dataset. Usually includes a data source and data release. Options: "gtex_v8", "gtex_snrnaseq_pilot".

page

Integer (default = 0).

itemsPerPage

Integer (default = 250). Set globally to maximum value 100000 with options(list(gtexr.itemsPerPage = 100000)).

.verbose

Logical. If TRUE (default), print paging information. Set to FALSE globally with options(list(gtexr.verbose = FALSE)).

.return_raw

Logical. If TRUE, return the raw API JSON response. Default = FALSE

Value

A tibble. Or a list if .return_raw = TRUE.

See also

Other Static Association Endpoints: get_eqtl_genes(), get_fine_mapping(), get_multi_tissue_eqtls(), get_significant_single_tissue_eqtls(), get_significant_single_tissue_eqtls_by_location(), get_significant_single_tissue_ieqtls(), get_significant_single_tissue_isqtls(), get_significant_single_tissue_sqtls(), get_sqtl_genes()

Examples

# search by gene
get_independent_eqtl(gencodeIds = c(
  "ENSG00000132693.12",
  "ENSG00000203782.5"
))
#> 
#> ── Paging info ─────────────────────────────────────────────────────────────────
#> • numberOfPages = 1
#> • page = 0
#> • maxItemsPerPage = 250
#> • totalNumberOfItems = 8
#> # A tibble: 8 × 14
#>   gencodeId       geneSymbol variantId snpId tissueSiteDetailId ontologyId  rank
#>   <chr>           <chr>      <chr>     <chr> <chr>              <chr>      <int>
#> 1 ENSG0000013269… CRP        chr1_160… rs66… Esophagus_Gastroe… UBERON:00…     1
#> 2 ENSG0000013269… CRP        chr1_159… rs57… Thyroid            UBERON:00…     1
#> 3 ENSG0000020378… LOR        chr1_153… rs14… Artery_Coronary    UBERON:00…     1
#> 4 ENSG0000020378… LOR        chr1_153… rs87… Whole_Blood        UBERON:00…     1
#> 5 ENSG0000020378… LOR        chr1_153… rs12… Skin_Sun_Exposed_… UBERON:00…     1
#> 6 ENSG0000020378… LOR        chr1_152… rs77… Heart_Atrial_Appe… UBERON:00…     1
#> 7 ENSG0000020378… LOR        chr1_153… rs12… Esophagus_Mucosa   UBERON:00…     1
#> 8 ENSG0000013269… CRP        chr1_159… rs86… Muscle_Skeletal    UBERON:00…     1
#> # ℹ 7 more variables: tssDistance <int>, maf <dbl>, pValue <dbl>, nes <dbl>,
#> #   chromosome <chr>, pos <int>, datasetId <chr>

# optionally filter for a single variant and/or one or more tissues
get_independent_eqtl(
  gencodeIds = c(
    "ENSG00000132693.12",
    "ENSG00000203782.5"
  ),
  tissueSiteDetailIds = c(
    "Whole_Blood",
    "Thyroid"
  )
)
#> 
#> ── Paging info ─────────────────────────────────────────────────────────────────
#> • numberOfPages = 1
#> • page = 0
#> • maxItemsPerPage = 250
#> • totalNumberOfItems = 2
#> # A tibble: 2 × 14
#>   gencodeId       geneSymbol variantId snpId tissueSiteDetailId ontologyId  rank
#>   <chr>           <chr>      <chr>     <chr> <chr>              <chr>      <int>
#> 1 ENSG0000013269… CRP        chr1_159… rs57… Thyroid            UBERON:00…     1
#> 2 ENSG0000020378… LOR        chr1_153… rs87… Whole_Blood        UBERON:00…     1
#> # ℹ 7 more variables: tssDistance <int>, maf <dbl>, pValue <dbl>, nes <dbl>,
#> #   chromosome <chr>, pos <int>, datasetId <chr>