Introduction to gtexr

The GTEx Portal API V2 enables programmatic access to data available from the Genotype-Tissue Expression Portal. The gtexr package wraps this API, providing R functions that correspond to each API endpoint:

Shiny app

Users can try out all functions interatively with the ⭐gtexr shiny app⭐, which pre-populates query parameters with those for the first working example from each function’s documentation.

Examples

The rest of this vignette outlines some example applications of gtexr.

library(gtexr)
library(dplyr)
library(purrr)

Get build 37 coordinates for a variant

get_variant(snpId = "rs1410858") |>
  tidyr::separate(
    col = b37VariantId,
    into = c(
      "chromosome",
      "position",
      "reference_allele",
      "alternative_allele",
      "genome_build"
    ),
    sep = "_",
    remove = FALSE
  ) |>
  select(snpId:genome_build)
#> 
#> ── Paging info ─────────────────────────────────────────────────────────────────
#> • numberOfPages = 1
#> • page = 0
#> • maxItemsPerPage = 250
#> • totalNumberOfItems = 1
#> # A tibble: 1 × 7
#>   snpId     b37VariantId chromosome position reference_allele alternative_allele
#>   <chr>     <chr>        <chr>      <chr>    <chr>            <chr>             
#> 1 rs1410858 1_153182116… 1          1531821… C                A                 
#> # ℹ 1 more variable: genome_build <chr>

Convert gene symbol to versioned GENCODE ID

Use get_gene() or get_genes()

get_genes("CRP") |>
  select(geneSymbol, gencodeId)
#> 
#> ── Paging info ─────────────────────────────────────────────────────────────────
#> • numberOfPages = 1
#> • page = 0
#> • maxItemsPerPage = 250
#> • totalNumberOfItems = 1
#> # A tibble: 1 × 2
#>   geneSymbol gencodeId         
#>   <chr>      <chr>             
#> 1 CRP        ENSG00000132693.12

Convert rsID to GTEx variant ID

Use get_variant()

get_variant(snpId = "rs1410858") |>
  select(snpId, variantId)
#> 
#> ── Paging info ─────────────────────────────────────────────────────────────────
#> • numberOfPages = 1
#> • page = 0
#> • maxItemsPerPage = 250
#> • totalNumberOfItems = 1
#> # A tibble: 1 × 2
#>   snpId     variantId             
#>   <chr>     <chr>                 
#> 1 rs1410858 chr1_153209640_C_A_b38

For a gene of interest, which tissues have significant cis-eQTLs?

Use get_significant_single_tissue_eqtls() (note this requires versioned GENCODE IDs)

gene_symbol_of_interest <- "CRP"

gene_gencodeId_of_interest <- get_genes(gene_symbol_of_interest) |>
  pull(gencodeId) |>
  suppressMessages()

gene_gencodeId_of_interest |>
  get_significant_single_tissue_eqtls() |>
  distinct(geneSymbol, gencodeId, tissueSiteDetailId)
#> 
#> ── Paging info ─────────────────────────────────────────────────────────────────
#> • numberOfPages = 1
#> • page = 0
#> • maxItemsPerPage = 250
#> • totalNumberOfItems = 93
#> # A tibble: 3 × 3
#>   geneSymbol gencodeId          tissueSiteDetailId                 
#>   <chr>      <chr>              <chr>                              
#> 1 CRP        ENSG00000132693.12 Thyroid                            
#> 2 CRP        ENSG00000132693.12 Esophagus_Gastroesophageal_Junction
#> 3 CRP        ENSG00000132693.12 Muscle_Skeletal

Get data for non-eQTL variants

Some analyses (e.g. Mendelian randomisation) require data for variants which may or may not be significant eQTLs. Use calculate_expression_quantitative_trait_loci() with purrr::map() to retrieve data for multiple variants

variants_of_interest <- c("rs12119111", "rs6605071", "rs1053870")

variants_of_interest |>
  set_names() |>
  map(
    \(x) calculate_expression_quantitative_trait_loci(
      tissueSiteDetailId = "Liver",
      gencodeId = "ENSG00000237973.1",
      variantId = x
    )
  ) |>
  bind_rows(.id = "rsid") |>
  
  # optionally, reformat output - first extract genomic coordinates and alleles
  tidyr::separate(
    col = "variantId",
    into = c(
      "chromosome",
      "position",
      "reference_allele",
      "alternative_allele",
      "genome_build"
    ),
    sep = "_"
  ) |>
  
  # ...then ascertain alternative_allele frequency
  mutate(
    alt_allele_count = (2 * homoAltCount) + hetCount,
    total_allele_count = 2 * (homoAltCount + hetCount +  homoRefCount),
    alternative_allele_frequency = alt_allele_count / total_allele_count
  ) |>
  
  select(
    rsid,
    beta = nes,
    se = error,
    pValue,
    minor_allele_frequency = maf,
    alternative_allele_frequency,
    chromosome:genome_build,
    tissueSiteDetailId
  )
#> # A tibble: 3 × 12
#>   rsid         beta     se  pValue minor_allele_frequency alternative_allele_f…¹
#>   <chr>       <dbl>  <dbl>   <dbl>                  <dbl>                  <dbl>
#> 1 rs121191…  0.0270 0.0670 6.88e-1                 0.365                   0.635
#> 2 rs6605071 -0.601  0.166  3.88e-4                 0.0409                  0.959
#> 3 rs1053870  0.0247 0.0738 7.38e-1                 0.214                   0.214
#> # ℹ abbreviated name: ¹​alternative_allele_frequency
#> # ℹ 6 more variables: chromosome <chr>, position <chr>, reference_allele <chr>,
#> #   alternative_allele <chr>, genome_build <chr>, tissueSiteDetailId <chr>