The EMBL-EBI curation of the GWAS catalog (originated at NHGRI) includes labelings of GWAS hit records with terms from the EBI Experimental Factor Ontology (EFO). The Bioconductor gwascat package includes a graph representation of the ontology and records the EFO assignments of GWAS results in its basic representations of the catalog.
Term names are regimented.
## A graphNEL graph with directed edges
## Number of Nodes = 16331
## Number of Edges = 22186## [1] "EFO:0000001" "BFO:0000007" "BFO:0000016" "BFO:0000019" "BFO:0000020"
## [6] "BFO:0000023"The nodeData of the graph includes a def field. We will
process that to create a data.frame.
nd = graph::nodeData(efo.obo.g)
alldef = sapply(nd, function(x) unlist(x[["def"]]))
allnames = sapply(nd, function(x) unlist(x[["name"]]))
alld2 = sapply(alldef, function(x) if(is.null(x)) return(" ") else x[1])
mydf = data.frame(id = names(allnames), concept=as.character(allnames), def=unlist(alld2))We can create an interactive data table for all terms, but for performance we limit the table size to terms involving the string ‘autoimm’.
## Loading required namespace: DTThe use of the graph representation allows various approaches to traversal and selection. Here we examine metadata for a term of interest, transform to an undirected graph, and obtain the adjacency list for that term.
## $`EFO:0000540`
## $`EFO:0000540`$name
## $`EFO:0000540`$name[[1]]
## [1] "immune system disease"
##
##
## $`EFO:0000540`$def
## $`EFO:0000540`$def[[1]]
## [1] "\"A group of non-neoplastic and neoplastic disorders resulting from the deregulation and/or deficiency of immune system functions. It includes autoimmune disorders (e.g., lupus erythematosus, dermatomyositis, rheumatoid arthritis), congenital and acquired immunodeficiency syndromes including the acquired immune deficiency syndrome (AIDS), and neoplasms (e.g., lymphomas and malignancies secondary to transplantation.)\" []"
##
##
## $`EFO:0000540`$xref
## NULLue = graph::ugraph(efo.obo.g)
neighISD = graph::adj(ue, "EFO:0000540")[[1]]
sapply(graph::nodeData(graph::subGraph(neighISD, efo.obo.g)), "[[", "name")## $`EFO:0000408`
## [1] "disease"
##
## $`EFO:0000398`
## [1] "dermatomyositis"
##
## $`EFO:0000404`
## [1] "diffuse scleroderma"
##
## $`EFO:0000676`
## [1] "psoriasis"
##
## $`EFO:0000706`
## [1] "spondyloarthropathy"
##
## $`EFO:0000717`
## [1] "systemic scleroderma"
##
## $`EFO:0000783`
## [1] "myositis"
##
## $`EFO:0002498`
## [1] "aggressive insulitis"
##
## $`EFO:0002502`
## [1] "benign insulitis"
##
## $`EFO:0003775`
## [1] "Job's syndrome"
##
## $`EFO:0003778`
## [1] "psoriatic arthritis"
##
## $`EFO:0003785`
## [1] "allergy"
##
## $`EFO:0004246`
## [1] "mucocutaneous lymph node syndrome"
##
## $`EFO:0004599`
## [1] "acute graft vs. host disease"
##
## $`EFO:0004711`
## [1] "elephantiasis"
##
## $`EFO:0005140`
## [1] "autoimmune disease"
##
## $`EFO:0005555`
## [1] "gamma chain deficiency"
##
## $`EFO:0005565`
## [1] "janus kinase-3 deficiency"
##
## $`EFO:0005809`
## [1] "type II hypersensitivity reaction disease"
##
## $`Orphanet:179`
## [1] "Birdshot chorioretinopathy"
##
## $`Orphanet:183770`
## [1] "Rare genetic immune disease"With RBGL we can compute paths to terms from root.
requireNamespace("RBGL")
p = RBGL::sp.between( efo.obo.g, "EFO:0000685", "EFO:0000001")
sapply(graph::nodeData(graph::subGraph(p[[1]]$path_detail, efo.obo.g)), "[[", "name")## $`EFO:0000685`
## [1] "rheumatoid arthritis"
##
## $`EFO:0005856`
## [1] "arthritis"
##
## $`EFO:0005140`
## [1] "autoimmune disease"
##
## $`EFO:0000540`
## [1] "immune system disease"
##
## $`EFO:0000408`
## [1] "disease"
##
## $`BFO:0000016`
## [1] "disposition"
##
## $`BFO:0000020`
## [1] "material property"
##
## $`EFO:0000001`
## [1] "experimental factor "The mcols element of the GRanges instances
provided by gwascat include mapped EFO terms and EFO URIs.
## [1] "DATE ADDED TO CATALOG" "PUBMEDID"
## [3] "FIRST AUTHOR" "DATE"
## [5] "JOURNAL" "LINK"
## [7] "STUDY" "DISEASE/TRAIT"
## [9] "INITIAL SAMPLE SIZE" "REPLICATION SAMPLE SIZE"
## [11] "REGION" "CHR_ID"
## [13] "CHR_POS" "REPORTED GENE(S)"
## [15] "MAPPED_GENE" "UPSTREAM_GENE_ID"
## [17] "DOWNSTREAM_GENE_ID" "SNP_GENE_IDS"
## [19] "UPSTREAM_GENE_DISTANCE" "DOWNSTREAM_GENE_DISTANCE"
## [21] "STRONGEST SNP-RISK ALLELE" "SNPS"
## [23] "MERGED" "SNP_ID_CURRENT"
## [25] "CONTEXT" "INTERGENIC"
## [27] "RISK ALLELE FREQUENCY" "P-VALUE"
## [29] "PVALUE_MLOG" "P-VALUE (TEXT)"
## [31] "OR or BETA" "95% CI (TEXT)"
## [33] "PLATFORM [SNPS PASSING QC]" "CNV"
## [35] "MAPPED_TRAIT" "MAPPED_TRAIT_URI"
## [37] "STUDY ACCESSION" "GENOTYPING TECHNOLOGY"## gwasloc instance with 46 records and 38 attributes per record.
## Extracted: 2020-04-30 23:24:51
## metadata()$badpos includes records for which no unique locus was given.
## Genome: GRCh38
## Excerpt:
## GRanges object with 5 ranges and 3 metadata columns:
## seqnames ranges strand | DISEASE/TRAIT SNPS P-VALUE
## <Rle> <IRanges> <Rle> | <character> <character> <numeric>
## [1] 12 103493699 * | Autoimmune traits rs1320344 6e-11
## [2] 12 111773070 * | Autoimmune traits rs191252491 1e-08
## [3] 17 47252111 * | Autoimmune traits rs73316435 6e-08
## [4] 19 10352442 * | Autoimmune traits rs34536443 4e-28
## [5] 6 412802 * | Autoimmune traits rs9392504 1e-10
## -------
## seqinfo: 24 sequences from GRCh38 genome## DataFrame with 46 rows and 2 columns
## MAPPED_TRAIT MAPPED_TRAIT_URI
## <character> <character>
## 1 autoimmune disease http://www.ebi.ac.uk..
## 2 autoimmune disease http://www.ebi.ac.uk..
## 3 autoimmune disease http://www.ebi.ac.uk..
## 4 autoimmune disease http://www.ebi.ac.uk..
## 5 autoimmune disease http://www.ebi.ac.uk..
## ... ... ...
## 42 autoimmune thyroid d.. http://www.ebi.ac.uk..
## 43 autoimmune thyroid d.. http://www.ebi.ac.uk..
## 44 salivary gland lesio.. http://www.ebi.ac.uk..
## 45 autoimmune thyroid d.. http://www.ebi.ac.uk..
## 46 autoimmune thyroid d.. http://www.ebi.ac.uk..