Calculate Expression Quantitative Trait Loci

Calculate your own eQTLs

• This service calculates the gene-variant association for any given pair of gene and variant, which may or may not be significant.

• This requires as input a GENCODE ID, GTEx variant ID, and tissue site detail ID.

By default, the calculation is based on the latest GTEx release.

GTEx Portal API documentation.

Usage

calculate_expression_quantitative_trait_loci(
  tissueSiteDetailId,
  gencodeId,
  variantId,
  datasetId = "gtex_v8"
)

Arguments

tissueSiteDetailId

String. The ID of the tissue of interest. Can be a GTEx specific ID (e.g. "Whole_Blood"; use get_tissue_site_detail() to see valid values) or an Ontology ID.

gencodeId

String. A Versioned GENCODE ID of a gene, e.g. "ENSG00000065613.9".

variantId

String. A gtex variant ID.

datasetId

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

Value

A tibble.

Details

Notes on output:

• Beta and standard error are recorded in columns nes and error respectively (see GTEx FAQs)

• variantId contains (in order) chromosome, position, reference allele, alternative allele and human genome build separated by underscores. The reference and alternative alleles for "chr1_13550_G_A_b38" for example are "G" and "A" respectively.

• See examples for how to calculate minor and alternative allele frequencies.

Notes on input:

• Argument variantId also accepts RSIDs.

See also

Other Dynamic Association Endpoints: calculate_ieqtls(), calculate_isqtls(), calculate_splicing_quantitative_trait_loci()

Examples

# \dontrun{
# perform request - returns a tibble with a single row
calculate_expression_quantitative_trait_loci(tissueSiteDetailId = "Whole_Blood",
                                             gencodeId = "ENSG00000203782.5",
                                             variantId = "rs79641866")
#> # A tibble: 1 × 15
#>   data               error gencodeId  geneSymbol genotypes hetCount homoAltCount
#>   <list>             <dbl> <chr>      <chr>      <list>       <int>        <int>
#> 1 <tibble [670 × 1]> 0.148 ENSG00000… LOR        <tibble>        38            0
#> # ℹ 8 more variables: homoRefCount <int>, maf <dbl>, nes <dbl>, pValue <dbl>,
#> #   pValueThreshold <dbl>, tStatistic <dbl>, tissueSiteDetailId <chr>,
#> #   variantId <chr>

# unnest list columns with tidyr::unnest()
calculate_expression_quantitative_trait_loci(tissueSiteDetailId = "Whole_Blood",
                                             gencodeId = "ENSG00000203782.5",
                                             variantId = "rs79641866") |>
  tidyr::unnest(c("data", "genotypes"))
#> # A tibble: 670 × 15
#>       data error gencodeId         geneSymbol genotypes hetCount homoAltCount
#>      <dbl> <dbl> <chr>             <chr>          <int>    <int>        <int>
#>  1 -1.16   0.148 ENSG00000203782.5 LOR                0       38            0
#>  2  0.978  0.148 ENSG00000203782.5 LOR                0       38            0
#>  3  0.0542 0.148 ENSG00000203782.5 LOR                0       38            0
#>  4 -0.278  0.148 ENSG00000203782.5 LOR                0       38            0
#>  5  0.182  0.148 ENSG00000203782.5 LOR                1       38            0
#>  6 -0.614  0.148 ENSG00000203782.5 LOR                0       38            0
#>  7  0.462  0.148 ENSG00000203782.5 LOR                0       38            0
#>  8 -0.125  0.148 ENSG00000203782.5 LOR                0       38            0
#>  9  0.421  0.148 ENSG00000203782.5 LOR                0       38            0
#> 10  0.274  0.148 ENSG00000203782.5 LOR                0       38            0
#> # ℹ 660 more rows
#> # ℹ 8 more variables: homoRefCount <int>, maf <dbl>, nes <dbl>, pValue <dbl>,
#> #   pValueThreshold <dbl>, tStatistic <dbl>, tissueSiteDetailId <chr>,
#> #   variantId <chr>

# to calculate minor and alternative allele frequencies
calculate_expression_quantitative_trait_loci(
  tissueSiteDetailId = "Liver",
  gencodeId = "ENSG00000237973.1",
  variantId = "rs12119111"
) |>
 dplyr::bind_rows(.id = "rsid") |>

 tidyr::separate(
   col = "variantId",
   into = c(
     "chromosome",
     "position",
     "reference_allele",
     "alternative_allele",
     "genome_build"
   ),
   sep = "_"
 ) |>

 # ...then ascertain alternative_allele frequency
 dplyr::mutate(
   alt_allele_count = (2 * homoAltCount) + hetCount,
   total_allele_count = 2 * (homoAltCount + hetCount +  homoRefCount),
   alternative_allele_frequency = alt_allele_count / total_allele_count
 ) |>

 dplyr::select(
   rsid,
   beta = nes,
   se = error,
   pValue,
   minor_allele_frequency = maf,
   alternative_allele_frequency,
   chromosome:genome_build,
   tissueSiteDetailId
 )
#> # A tibble: 1 × 12
#>   rsid    beta     se pValue minor_allele_frequency alternative_allele_frequency
#>   <chr>  <dbl>  <dbl>  <dbl>                  <dbl>                        <dbl>
#> 1 1     0.0270 0.0670  0.688                  0.365                        0.635
#> # ℹ 6 more variables: chromosome <chr>, position <chr>, reference_allele <chr>,
#> #   alternative_allele <chr>, genome_build <chr>, tissueSiteDetailId <chr>
# }