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examples

Source: vignettes/web_only/introduction.Rmd
introduction.Rmd

Introduction

The otargen package provides seamless access to the Open Targets Platform API, enabling researchers to query gene-disease associations, variant annotations, pharmacogenomics, and more. This vignette demonstrates examples from major functional areas in otargen, organized into categories such as GWAS, genetic constraint analysis, variants, pharmacogenomics, and more.

GWAS and Colocalisation

Example: GWAS Credible Sets for Gene-Disease Associations

Objective: Identify causal variants linking a gene to a disease using GWAS credible sets.
Function: gwasCredibleSetsQuery

# Retrieve GWAS credible sets for APOE and Alzheimer’s disease
result <- gwasCredibleSetsQuery(
  ensemblId = "ENSG00000198125",
  efoId = "EFO_0003767",
  size = 5
)
print(result)
#> NULL

Genetic Analysis

Example: Genetic Constraint Analysis

Objective: Assess a gene’s tolerance to mutations using genetic constraint metrics like pLI and LOEUF.
Function: geneticConstraintQuery

# Retrieve genetic constraint data for TP53
result <- geneticConstraintQuery(ensgId = "ENSG00000141510")
print(result)
#> # A tibble: 3 × 6
#>   constraintType  score upperBin upperBin6 geneId          approvedSymbol
#>   <chr>           <dbl>    <int>     <int> <chr>           <chr>         
#> 1 syn            -0.544       NA        NA ENSG00000141510 TP53          
#> 2 mis             0.983       NA        NA ENSG00000141510 TP53          
#> 3 lof             0.532        2         1 ENSG00000141510 TP53

Variants and Annotations

Example: Clinical Variant Evidence

Objective: Explore ClinVar evidence for gene-disease associations to uncover clinical significance.
Function: clinVarQuery

# Retrieve ClinVar evidence for CFTR and cystic fibrosis
result <- clinVarQuery(
  ensemblId = "ENSG00000080815",
  efoId = "MONDO_0004975",
  size = 5
)
print(result)
#> # A tibble: 5 × 24
#>   variantEffect directionOnTrait diseaseFromSource   variantRsId studyId     
#>   <chr>         <chr>            <chr>               <chr>       <chr>       
#> 1 LoF           risk             Alzheimer disease 3 rs63750219  RCV002470967
#> 2 NA            risk             Alzheimer disease 3 rs63750450  RCV001199924
#> 3 NA            risk             Alzheimer disease 3 rs63750083  RCV000019785
#> 4 NA            risk             Alzheimer disease 3 rs63750082  RCV000995615
#> 5 NA            risk             Alzheimer disease 3 rs63751024  RCV001808318
#> # ℹ 19 more variables: clinicalSignificances <list>,
#> #   allelicRequirements <list>, alleleOrigins <list>, confidence <chr>,
#> #   literature <list>, cohortPhenotypes <list>, disease.id <chr>,
#> #   disease.name <chr>, variant.id <chr>, variant.hgvsId <chr>,
#> #   variant.referenceAllele <chr>, variant.alternateAllele <chr>,
#> #   variantFunctionalConsequence.id <chr>,
#> #   variantFunctionalConsequence.label <chr>, approvedSymbol <chr>, …

Example: UniProt Variants

Objective: Annotate variants with functional data from UniProt to understand their biological impact.
Function: uniProtVariantsQuery

# Retrieve UniProt variants for a specific variant
result <- uniProtVariantsQuery(variantId = "12_111446804_T_C")
print(result)
#> NULL

Pharmacogenomics

Example: Pharmacogenomics Insights

Objective: Investigate how genetic variants influence drug response using pharmacogenomics data.
Function: pharmacogenomicsQuery

# Retrieve pharmacogenomics data for atorvastatin
result <- pharmacogenomicsChemblQuery(chemblId = "CHEMBL1016")
print(result)
#> # A tibble: 24 × 17
#>    variantRsId genotypeId       haplotypeId haplotypeFromSourceId isDirectTarget
#>    <chr>       <chr>            <lgl>       <lgl>                 <lgl>         
#>  1 rs3758785   11_94398973_A_A… NA          NA                    FALSE         
#>  2 rs1275988   2_26691496_C_T,T NA          NA                    FALSE         
#>  3 rs3184504   12_111446804_T_… NA          NA                    FALSE         
#>  4 rs6722745   2_108258788_T_C… NA          NA                    FALSE         
#>  5 rs3184504   12_111446804_T_… NA          NA                    FALSE         
#>  6 rs5186      3_148742201_A_A… NA          NA                    TRUE          
#>  7 rs6722745   2_108258788_T_C… NA          NA                    FALSE         
#>  8 rs740406    19_2232222_A_A,G NA          NA                    FALSE         
#>  9 rs740406    19_2232222_A_A,G NA          NA                    FALSE         
#> 10 rs740406    19_2232222_A_G,G NA          NA                    FALSE         
#> # ℹ 14 more rows
#> # ℹ 12 more variables: phenotypeFromSourceId <lgl>,
#> #   genotypeAnnotationText <chr>, phenotypeText <chr>, pgxCategory <chr>,
#> #   evidenceLevel <chr>, studyId <chr>, literature <list>,
#> #   variantFunctionalConsequence.id <chr>,
#> #   variantFunctionalConsequence.label <chr>, target.id <chr>,
#> #   target.approvedSymbol <chr>, drugId <chr>

Additional Functions and Categories

Beyond the examples above, otargen includes many other queries grouped into logical categories:

  • Targets and Interactions
    • compGenomicsQuery
    • depMapQuery
    • interactionsQuery
    • hallmarksQuery
    • mousePhenotypesQuery
    • safetyQuery
  • Pathways and Ontologies
    • pathwaysQuery
    • geneOntologyQuery
  • External Data Sources
    • europePMCQuery
    • orphanetQuery
    • genomicsEnglandQuery
  • Chembl and Drug Queries
    • chemblQuery
    • indicationsQuery

See the package reference documentation for details on these functions and parameters.

Learn More

For a full list of available queries and example usage:

help(package = "otargen")

or browse the function reference on the pkgdown site.