Getting Traits • quincunx

Introduction

Polygenic scores (PGSs) are annotated with information about the phenotype that it predicts, i.e. the reported trait (as reported in the original publication). This can be found as the column reported_trait in slot scores of scores objects:

pgs_01 <- get_scores('PGS000001')
#> Warning: Automatic coercion from integer to character was deprecated in purrr 1.0.0.
#> ℹ Please use an explicit call to `as.character()` within `map_chr()` instead.
#> ℹ The deprecated feature was likely used in the tidyjson package.
#>   Please report the issue at <https://github.com/colearendt/tidyjson/issues>.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
#> generated.
pgs_01@scores
#> # A tibble: 1 × 12
#>   pgs_id    pgs_name scoring_file             matches_publication reported_trait
#>   <chr>     <chr>    <chr>                    <lgl>               <chr>         
#> 1 PGS000001 PRS77_BC https://ftp.ebi.ac.uk/p… TRUE                Breast Cancer 
#> # ℹ 7 more variables: trait_additional_description <chr>,
#> #   pgs_method_name <chr>, pgs_method_params <chr>, n_variants <int>,
#> #   n_variants_interactions <int>, assembly <chr>, license <chr>

The predicted phenotype is also mapped to Experimental Factor Ontology (EFO) terms (a controlled vocabulary for the unambiguous identification of traits and diseases, and their relationships), namely, the EFO trait. The EFO traits associated with a polygenic score can also be found in scores objects in the slot traits, column trait:

pgs_01@traits
#> # A tibble: 1 × 5
#>   pgs_id    efo_id      trait            description                       url  
#>   <chr>     <chr>       <chr>            <chr>                             <chr>
#> 1 PGS000001 EFO_0000305 breast carcinoma A carcinoma that arises from epi… http…

Many PGSs have been developed and demonstrated to be predictive of common complex traits, e.g. body mass index (BMI)¹, blood lipids² and educational attainment³.

Similarly, PGSs for various diseases have been shown to be predictive of disease incidence, defining marked increases in risk over the life course or at earlier ages for people with high PGSs, e.g. coronary artery disease^4,5, breast cancer⁶ and schizophrenia⁷.

Getting catalogued traits from PGS Catalog

If you are interested in retrieving polygenic scores from the Catalog, you might want to search them by the trait they predict. get_scores() is the function that searches for PGSs, however, this function only allows to search by pgs_id, efo_id or pubmed_id. So in order to search by a trait term, we need to first find the associated EFO identifiers (efo_id).

To search for traits (or diseases), you use the function get_traits(). With this function you can search by:

The EFO trait identifier: efo_id;
or by the trait term: a term to be matched in the EFO identifier (efo_id), label, description synonyms, trait categories, or external mapped terms.

The most useful search criteria is the trait term, and that is typically want you will want to use. Unless you already know the EFO trait you are interested in, and are looking for extra details about it, you won’t search directly with the EFO identifier.

Basic example

Let’s say you are interested in PGSs related to medical condition, stroke. Then you can search for "stroke" with get_traits():

get_traits(trait_term = 'stroke')
#> An object of class "traits"
#> Slot "traits":
#> # A tibble: 1 × 6
#>   efo_id      parent_efo_id is_child trait  description                    url  
#>   <chr>       <chr>         <lgl>    <chr>  <chr>                          <chr>
#> 1 EFO_0000712 NA            FALSE    stroke A sudden loss of neurological… http…
#> 
#> Slot "pgs_ids":
#> # A tibble: 10 × 4
#>    efo_id      parent_efo_id is_child pgs_id   
#>    <chr>       <chr>         <lgl>    <chr>    
#>  1 EFO_0000712 NA            FALSE    PGS000038
#>  2 EFO_0000712 NA            FALSE    PGS000039
#>  3 EFO_0000712 NA            FALSE    PGS000665
#>  4 EFO_0000712 NA            FALSE    PGS000911
#>  5 EFO_0000712 NA            FALSE    PGS001793
#>  6 EFO_0000712 NA            FALSE    PGS001798
#>  7 EFO_0000712 NA            FALSE    PGS002259
#>  8 EFO_0000712 NA            FALSE    PGS002724
#>  9 EFO_0000712 NA            FALSE    PGS002725
#> 10 EFO_0000712 NA            FALSE    PGS002770
#> 
#> Slot "child_pgs_ids":
#> # A tibble: 0 × 4
#> # ℹ 4 variables: efo_id <chr>, parent_efo_id <chr>, is_child <lgl>,
#> #   child_pgs_id <chr>
#> 
#> Slot "trait_categories":
#> # A tibble: 2 × 4
#>   efo_id      parent_efo_id is_child trait_categories      
#>   <chr>       <chr>         <lgl>    <chr>                 
#> 1 EFO_0000712 NA            FALSE    Cardiovascular disease
#> 2 EFO_0000712 NA            FALSE    Neurological disorder 
#> 
#> Slot "trait_synonyms":
#> # A tibble: 53 × 4
#>    efo_id      parent_efo_id is_child trait_synonyms                 
#>    <chr>       <chr>         <lgl>    <chr>                          
#>  1 EFO_0000712 NA            FALSE    Acute Cerebrovascular Accident 
#>  2 EFO_0000712 NA            FALSE    Acute Cerebrovascular Accidents
#>  3 EFO_0000712 NA            FALSE    Acute Stroke                   
#>  4 EFO_0000712 NA            FALSE    Acute Strokes                  
#>  5 EFO_0000712 NA            FALSE    Apoplexy                       
#>  6 EFO_0000712 NA            FALSE    Apoplexy, Cerebrovascular      
#>  7 EFO_0000712 NA            FALSE    Brain Vascular Accident        
#>  8 EFO_0000712 NA            FALSE    Brain Vascular Accidents       
#>  9 EFO_0000712 NA            FALSE    CEREBROVASCULAR ACCIDENT, (CVA)
#> 10 EFO_0000712 NA            FALSE    CVA                            
#> # ℹ 43 more rows
#> 
#> Slot "trait_mapped_terms":
#> # A tibble: 13 × 4
#>    efo_id      parent_efo_id is_child trait_mapped_terms  
#>    <chr>       <chr>         <lgl>    <chr>               
#>  1 EFO_0000712 NA            FALSE    HP:0001297          
#>  2 EFO_0000712 NA            FALSE    ICD10:I64           
#>  3 EFO_0000712 NA            FALSE    MESH:D020521        
#>  4 EFO_0000712 NA            FALSE    MONDO:0005098       
#>  5 EFO_0000712 NA            FALSE    MeSH:D020521        
#>  6 EFO_0000712 NA            FALSE    MedDRA:10042244     
#>  7 EFO_0000712 NA            FALSE    NCIT:C3390          
#>  8 EFO_0000712 NA            FALSE    NCIt:C3390          
#>  9 EFO_0000712 NA            FALSE    NIFSTD:birnlex_12783
#> 10 EFO_0000712 NA            FALSE    OMIM:601367         
#> 11 EFO_0000712 NA            FALSE    SCTID:230690007     
#> 12 EFO_0000712 NA            FALSE    SNOMEDCT:230690007  
#> 13 EFO_0000712 NA            FALSE    SNOMEDCT:422504002

As can be seen from the returned traits object, we get a set of six tables (slots) that include several details about stroke.

In the first table traits we got only one row, indicating that this query returned only one trait in the Catalog. This trait is named "stroke" (column trait), and is unambiguously identified by the EFO identifier EFO_0000712.

Exact matching

By default, the trait term is matched exactly. If you want to relax the matching, then indicate with the parameter exact_term set to FALSE. This way you will get, potentially, more results, in this example case, ischemic stroke (HP_0002140) is now also returned:

get_traits(trait_term = 'stroke', exact_term = FALSE)
#> An object of class "traits"
#> Slot "traits":
#> # A tibble: 4 × 6
#>   efo_id      parent_efo_id is_child trait                     description url  
#>   <chr>       <chr>         <lgl>    <chr>                     <chr>       <chr>
#> 1 EFO_0005669 NA            FALSE    intracerebral hemorrhage  Bleeding i… http…
#> 2 HP_0002140  NA            FALSE    Ischemic stroke           Acute isch… http…
#> 3 EFO_0010555 NA            FALSE    left ventricular stroke … Quantifica… http…
#> 4 EFO_0000712 NA            FALSE    stroke                    A sudden l… http…
#> 
#> Slot "pgs_ids":
#> # A tibble: 17 × 4
#>    efo_id      parent_efo_id is_child pgs_id   
#>    <chr>       <chr>         <lgl>    <chr>    
#>  1 EFO_0005669 NA            FALSE    PGS003457
#>  2 HP_0002140  NA            FALSE    PGS000039
#>  3 HP_0002140  NA            FALSE    PGS000665
#>  4 HP_0002140  NA            FALSE    PGS000911
#>  5 HP_0002140  NA            FALSE    PGS002724
#>  6 HP_0002140  NA            FALSE    PGS002725
#>  7 EFO_0010555 NA            FALSE    PGS001413
#>  8 EFO_0000712 NA            FALSE    PGS000038
#>  9 EFO_0000712 NA            FALSE    PGS000039
#> 10 EFO_0000712 NA            FALSE    PGS000665
#> 11 EFO_0000712 NA            FALSE    PGS000911
#> 12 EFO_0000712 NA            FALSE    PGS001793
#> 13 EFO_0000712 NA            FALSE    PGS001798
#> 14 EFO_0000712 NA            FALSE    PGS002259
#> 15 EFO_0000712 NA            FALSE    PGS002724
#> 16 EFO_0000712 NA            FALSE    PGS002725
#> 17 EFO_0000712 NA            FALSE    PGS002770
#> 
#> Slot "child_pgs_ids":
#> # A tibble: 0 × 4
#> # ℹ 4 variables: efo_id <chr>, parent_efo_id <chr>, is_child <lgl>,
#> #   child_pgs_id <chr>
#> 
#> Slot "trait_categories":
#> # A tibble: 6 × 4
#>   efo_id      parent_efo_id is_child trait_categories          
#>   <chr>       <chr>         <lgl>    <chr>                     
#> 1 EFO_0005669 NA            FALSE    Cardiovascular disease    
#> 2 EFO_0005669 NA            FALSE    Neurological disorder     
#> 3 HP_0002140  NA            FALSE    Other trait               
#> 4 EFO_0010555 NA            FALSE    Cardiovascular measurement
#> 5 EFO_0000712 NA            FALSE    Cardiovascular disease    
#> 6 EFO_0000712 NA            FALSE    Neurological disorder     
#> 
#> Slot "trait_synonyms":
#> # A tibble: 56 × 4
#>    efo_id      parent_efo_id is_child trait_synonyms                 
#>    <chr>       <chr>         <lgl>    <chr>                          
#>  1 EFO_0005669 NA            FALSE    intracerebral hemorrhage       
#>  2 EFO_0005669 NA            FALSE    stroke, hemorrhagic            
#>  3 HP_0002140  NA            FALSE    Ischaemic stroke               
#>  4 EFO_0000712 NA            FALSE    Acute Cerebrovascular Accident 
#>  5 EFO_0000712 NA            FALSE    Acute Cerebrovascular Accidents
#>  6 EFO_0000712 NA            FALSE    Acute Stroke                   
#>  7 EFO_0000712 NA            FALSE    Acute Strokes                  
#>  8 EFO_0000712 NA            FALSE    Apoplexy                       
#>  9 EFO_0000712 NA            FALSE    Apoplexy, Cerebrovascular      
#> 10 EFO_0000712 NA            FALSE    Brain Vascular Accident        
#> # ℹ 46 more rows
#> 
#> Slot "trait_mapped_terms":
#> # A tibble: 23 × 4
#>    efo_id      parent_efo_id is_child trait_mapped_terms   
#>    <chr>       <chr>         <lgl>    <chr>                
#>  1 EFO_0005669 NA            FALSE    ICD10:I61            
#>  2 EFO_0005669 NA            FALSE    ICD9:431             
#>  3 EFO_0005669 NA            FALSE    MESH:D002543         
#>  4 EFO_0005669 NA            FALSE    MONDO:0013792        
#>  5 EFO_0005669 NA            FALSE    MedDRA:10022754      
#>  6 EFO_0005669 NA            FALSE    OMIM:614519          
#>  7 EFO_0005669 NA            FALSE    SCTID:274100004      
#>  8 HP_0002140  NA            FALSE    SNOMEDCT_US:422504002
#>  9 HP_0002140  NA            FALSE    UMLS:C0948008        
#> 10 EFO_0010555 NA            FALSE    PMID:31554410        
#> # ℹ 13 more rows

Subtraits (child traits)

By default, subtraits (child traits), are not retrieved by get_traits(). If you want to get all matching traits and those that are child traits thereof, then indicate with the parameter include_children set to TRUE. Here is an example with "breast cancer":

get_traits(trait_term = 'breast cancer', include_children = TRUE)
#> An object of class "traits"
#> Slot "traits":
#> # A tibble: 9 × 6
#>   efo_id        parent_efo_id is_child trait                   description url  
#>   <chr>         <chr>         <lgl>    <chr>                   <chr>       <chr>
#> 1 MONDO_0007254 NA            FALSE    breast cancer           A primary … http…
#> 2 MONDO_0021115 MONDO_0007254 TRUE     luminal B breast carci… A biologic… http…
#> 3 MONDO_0021116 MONDO_0007254 TRUE     luminal A breast carci… A biologic… http…
#> 4 EFO_1000649   MONDO_0007254 TRUE     estrogen-receptor posi… A subtype … http…
#> 5 EFO_1000650   MONDO_0007254 TRUE     estrogen-receptor nega… A subtype … http…
#> 6 EFO_1000294   MONDO_0007254 TRUE     HER2 Positive Breast C… A biologic… http…
#> 7 EFO_0005537   MONDO_0007254 TRUE     triple-negative breast… An invasiv… http…
#> 8 EFO_0000305   MONDO_0007254 TRUE     breast carcinoma        A carcinom… http…
#> 9 EFO_0009780   MONDO_0007254 TRUE     HER2 negative breast c… A biologic… http…
#> 
#> Slot "pgs_ids":
#> # A tibble: 118 × 4
#>    efo_id        parent_efo_id is_child pgs_id   
#>    <chr>         <chr>         <lgl>    <chr>    
#>  1 MONDO_0021115 MONDO_0007254 TRUE     PGS000214
#>  2 MONDO_0021116 MONDO_0007254 TRUE     PGS000212
#>  3 EFO_1000649   MONDO_0007254 TRUE     PGS000002
#>  4 EFO_1000649   MONDO_0007254 TRUE     PGS000005
#>  5 EFO_1000649   MONDO_0007254 TRUE     PGS000008
#>  6 EFO_1000649   MONDO_0007254 TRUE     PGS000046
#>  7 EFO_1000649   MONDO_0007254 TRUE     PGS000347
#>  8 EFO_1000649   MONDO_0007254 TRUE     PGS000774
#>  9 EFO_1000650   MONDO_0007254 TRUE     PGS000003
#> 10 EFO_1000650   MONDO_0007254 TRUE     PGS000006
#> # ℹ 108 more rows
#> 
#> Slot "child_pgs_ids":
#> # A tibble: 137 × 4
#>    efo_id        parent_efo_id is_child child_pgs_id
#>    <chr>         <chr>         <lgl>    <chr>       
#>  1 MONDO_0007254 NA            FALSE    PGS000001   
#>  2 MONDO_0007254 NA            FALSE    PGS000002   
#>  3 MONDO_0007254 NA            FALSE    PGS000003   
#>  4 MONDO_0007254 NA            FALSE    PGS000004   
#>  5 MONDO_0007254 NA            FALSE    PGS000005   
#>  6 MONDO_0007254 NA            FALSE    PGS000006   
#>  7 MONDO_0007254 NA            FALSE    PGS000007   
#>  8 MONDO_0007254 NA            FALSE    PGS000008   
#>  9 MONDO_0007254 NA            FALSE    PGS000009   
#> 10 MONDO_0007254 NA            FALSE    PGS000015   
#> # ℹ 127 more rows
#> 
#> Slot "trait_categories":
#> # A tibble: 9 × 4
#>   efo_id        parent_efo_id is_child trait_categories
#>   <chr>         <chr>         <lgl>    <chr>           
#> 1 MONDO_0007254 NA            FALSE    Cancer          
#> 2 MONDO_0021115 MONDO_0007254 TRUE     Cancer          
#> 3 MONDO_0021116 MONDO_0007254 TRUE     Cancer          
#> 4 EFO_1000649   MONDO_0007254 TRUE     Cancer          
#> 5 EFO_1000650   MONDO_0007254 TRUE     Cancer          
#> 6 EFO_1000294   MONDO_0007254 TRUE     Cancer          
#> 7 EFO_0005537   MONDO_0007254 TRUE     Cancer          
#> 8 EFO_0000305   MONDO_0007254 TRUE     Cancer          
#> 9 EFO_0009780   MONDO_0007254 TRUE     Cancer          
#> 
#> Slot "trait_synonyms":
#> # A tibble: 40 × 4
#>    efo_id        parent_efo_id is_child trait_synonyms                  
#>    <chr>         <chr>         <lgl>    <chr>                           
#>  1 MONDO_0007254 NA            FALSE    BC                              
#>  2 MONDO_0007254 NA            FALSE    breast cancer                   
#>  3 MONDO_0007254 NA            FALSE    cancer of breast                
#>  4 MONDO_0007254 NA            FALSE    malignant breast neoplasm       
#>  5 MONDO_0007254 NA            FALSE    malignant breast tumor          
#>  6 MONDO_0007254 NA            FALSE    malignant neoplasm of breast    
#>  7 MONDO_0007254 NA            FALSE    malignant neoplasm of the breast
#>  8 MONDO_0007254 NA            FALSE    malignant tumor of breast       
#>  9 MONDO_0007254 NA            FALSE    malignant tumor of the breast   
#> 10 MONDO_0007254 NA            FALSE    mammary cancer                  
#> # ℹ 30 more rows
#> 
#> Slot "trait_mapped_terms":
#> # A tibble: 44 × 4
#>    efo_id        parent_efo_id is_child trait_mapped_terms
#>    <chr>         <chr>         <lgl>    <chr>             
#>  1 MONDO_0007254 NA            FALSE    DOID:1612         
#>  2 MONDO_0007254 NA            FALSE    ICD10CM:C50       
#>  3 MONDO_0007254 NA            FALSE    ICD9:174.8        
#>  4 MONDO_0007254 NA            FALSE    NCIT:C9335        
#>  5 MONDO_0007254 NA            FALSE    OMIM:600048       
#>  6 MONDO_0007254 NA            FALSE    OMIM:605365       
#>  7 MONDO_0007254 NA            FALSE    SCTID:254837009   
#>  8 MONDO_0021115 MONDO_0007254 TRUE     NCIT:C53555       
#>  9 MONDO_0021115 MONDO_0007254 TRUE     UMLS:C3642346     
#> 10 MONDO_0021116 MONDO_0007254 TRUE     NCIT:C53554       
#> # ℹ 34 more rows

The column is_child indicates whether that trait is being retrieved because it is a direct result of the query or not. is_child is TRUE when the trait is returned because it is a child trait of a matching trait, and FALSE if a direct result of the query.

In the case of child traits, the column parent_efo_id indicates the EFO trait identifier of the parent trait, i.e. the direct matching trait, or NA otherwise.

Getting all traits

To retrieve all traits simply leave the parameters efo_id and trait_term as NULL (default):

get_traits()

References

Khera, A. V. et al. Polygenic prediction of weight and obesity trajectories from birth to adulthood. Cell 177, 587–596.e9 (2019).

Kuchenbaecker, K. et al. The transferability of lipid loci across african, asian and european cohorts. Nature Communications 10, (2019).

Lee, J. J. et al. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nature Genetics 50, 1112–1121 (2018).

Inouye, M. et al. Genomic risk prediction of coronary artery disease in 480,000 adults. Journal of the American College of Cardiology 72, 1883–1893 (2018).

Khera, A. V. et al. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nature Genetics 50, 1219–1224 (2018).

Mavaddat, N. et al. Polygenic Risk Scores for Prediction of Breast Cancer and Breast Cancer Subtypes. The American Journal of Human Genetics 104, 21–34 (2019).

Zheutlin, A. B. et al. Penetrance and pleiotropy of polygenic risk scores for schizophrenia in 106,160 patients across four health care systems. American Journal of Psychiatry 176, 846–855 (2019).