disgenet2r: An R package to explore the molecular underpinnings of human diseases
Introduction
The disgenet2r package contains a set of functions to retrieve, visualize and expand DISGENET data (Piñero et al. 2021, 2019). DISGENET is a comprehensive discovery platform that integrates more than 30 millions associations between genes, variants, and human diseases. The information in DISGENET has been extracted from expert-curated resources and from the literature using state-of-the-art text mining technologies (Table 1).
To use DISGENET and the disgenet2r package, you need to acquire a license. Please contact us at info@disgenet.com for license conditions and pricing.
Source_Name | Type_of_data | Description |
---|---|---|
ALL | GDAs/VDAs | All data sources |
CLINICALTRIALS | GDAs | Data from Clinicaltrials.org |
CLINVAR | GDAs/VDAs | ClinVar Database |
CLINGEN | GDAs/VDAs | The Clinical Genome Resource |
CURATED | GDAs/VDAs | Human curated sources: ClinGen,ClinVar,GenCC,UniProt,Orphanet,PsyGeNET,MGD,RGD |
FINNGEN | GDAs/VDAs | FinnGen data |
GENCC | GDAs | the Gene Curation Coalition Database |
GWASCAT | GDAs/VDAs | The NHGRI-EBI GWAS Catalog |
HPO | GDAs | Human Phenotype Ontology |
INFERRED | GDAs | Inferred data from the HPO and the GWAS and PHEWAS Catalogs, and from UK and FinnGen biobanks |
MGD_HUMAN | GDAs | Mouse Genome Database, human data |
MGD_MOUSE | GDAs | Mouse Genome Database, mouse data |
MODELS | GDAs | Data from animal models: MGD MOUSE, RGD RAT, and TEXT MINING MODELS |
ORPHANET | GDAs | The portal for rare diseases and orphan drugs |
PHEWASCAT | GDAs/VDAs | The PHEWAS Catalog |
PSYGENET | GDAs | Psychiatric disorders Gene association NETwork |
RGD_HUMAN | GDAs | Rat Genome Database, human data |
RGD_RAT | GDAs | Rat Genome Database, rat data |
TEXT MINING HUMAN | GDAs/VDAs | Data from text mining medline abstracts, human |
TEXT MINING MODELS | GDAs | Data from text mining medline abstracts, animal models |
UK BIOBANK | GDAs/VDAs | UK Biobank GWAS data |
UNIPROT | GDAs/VDAs | The Universal Protein Resource |
You can test DISGENET and the disgenet2r package by registering for a free trial account here.
disgenet2r package usage limits
Trial account
Please note that the trial account enables you to test all the functions of the disgenet2r package, but the queries to DISGENET database have the following restrictions:
Only the top-30 results ordered by descending DISGENET score are returned (pagination is not supported).
Multiple-entity queries support at most 10 entities (genes, diseases, variants).
The access to DISGENET with a TRIAL account will expire after 7 days from the day of activation.
Other plans
There are limits in place for the disgenet2r package to ensure smooth performance for all users. These limits apply to academics, advanced, and premium users, mirroring the limits of the DISGENET REST API.
Here’s a breakdown of the limitations:
A maximum of 100 pages of results are returned.
Multiple-entity queries support at most 100 entities (genes, diseases, variants).
Important Note: The package will display a warning message if you exceed these limits.
Recommendations for Efficient Use:
To improve performance and avoid exceeding limits, consider querying with smaller batches of entities. You can also use disgenet metrics and annotations to refine your search and reduce the number of returned results.
Installation and first run
The package disgenet2r is available through GitLab. The package requires an R version > 3.5.
Install disgenet2r by typing in R:
To load the package:
Once you have completed the registration process, go to your user profile…
… and retrieve your API key
After retrieving the API key from your user profile, run the lines below so the key is available for all the disgenet2r functions.
In the following document, we illustrate how to use the disgenet2r package through a series of examples.
Quick Start
The functions in the disgenet2r package receive as parameters one entity (gene, disease, variant, and chemical), or a list of entities (up to 100) and combinations of them. In addition, they have the following parameters:
score
A vector with two elements: 1) initial value of score 2) final value of score. Default0-1
.database
Name of the database that will be queried. DefaultCURATED
. It can take the values: ‘CLINGEN’, ‘GENCC’, ‘CLINVAR’, ‘ORPHANET’, ‘PSYGENET’, ‘UNIPROT’, ‘CURATED’, ‘HPO’, ‘GWASCAT’, ‘PHEWASCAT’, ‘UKBIOBANK’, ‘FINNGEN’, ‘INFERRED’, ‘MGD_HUMAN’, ‘MGD_MOUSE’, ‘RGD_HUMAN’, ‘RGD_RAT’, ‘TEXTMINING_MODELS’, ‘MODELS’, ‘TEXTMINING_HUMAN’, “CLINICALTRIALS” , and ‘ALL’.n_pags
A number between 1 and 100 indicating the number of pages to retrieve from the results of the query. Default100
. If a number of pages larger than 100 is indicated, the function will stop.verbose
By defaultFALSE
. Change it to TRUE to enable real-time logging from the function.order_by
By defaultscore
. Depending on the type of query, it can accept the following values: score, dsi, dpi, pli, pmYear, ei, yearInitial, yearFinal, numCTsupportingAssociation.
Below, an example of a query for the BRCA1 gene in ALL the data. Notice that this query retrieves over 300 pages of results. Only the first 10,000 results will be retrieved (100 pages, 100 results per page).
## Notice that your query has a maximum of 282 pages.
## By using the default n_pags (100), your query of 282 pages has been reduced to 100 pages.
## Object of class 'DataGeNET.DGN'
## . Search: single
## . Type: gene-evidence
## . Database: ALL
## . Score: 0-1
## . Term: BRCA1
## . Results: 10000
Retrieving Gene-Disease Associations from DISGENET
Searching by gene
The gene2disease function retrieves the GDAs in DISGENET for a given gene, or a for a list of genes. The gene(s) can be identified by either the NCBI gene identifier, or the official Gene Symbol, and the type of identifier used must be specified using the parameter vocabulary
. By default, vocabulary = "HGNC"
. To switch to Entrez NCBI Gene identifiers, set vocabulary to ENTREZ.
The function also requires the user to specify the source database using the argument database
. By default, all the functions in the disgenet2r package use as source database CURATED, which includes GDAs from ClinGen, ClinVar, MGD (Human data), RGD (Human data), Gen CC, PsyGeNET, UniProt, and Orphanet.
The information can be filtered using the DISGENET score. The argument score
consists of a range of score to perform the search. The score is entered as a vector which first position is the initial value of score, and the second argument is the final value of score. Both values will always be included. By default, score=c(0,1)
.
In the example, the query for the Leptin Receptor (Gene Symbol LEPR
, and Entrez NCBI Identifier 3953
) is performed in the curated data in DISGENET.
The function gene2disease produces an object DataGeNET.DGN
that contains the results of the query.
## [1] "DataGeNET.DGN"
## attr(,"package")
## [1] "disgenet2r"
Type the name of the object to display its attributes: the input parameters such as whether a single entity, or a list were searched (single
or list
), the type of entity (gene-disease
), the selected database (CURATED), the score range used in the search (0-1
), and the gene NCBI identifier (3953
).
## Object of class 'DataGeNET.DGN'
## . Search: single
## . Type: gene-disease
## . Database: CURATED
## . Score: 0-1
## . Term: 3953
## . Results: 68
To obtain the data frame with the results of the query
## gene_symbol geneid ensemblid geneNcbiType geneDSI geneDPI genepLI
## 1 LEPR 3953 ENSG00000116678 protein-coding 0.41 0.913 8.8607e-05
## 2 LEPR 3953 ENSG00000116678 protein-coding 0.41 0.913 8.8607e-05
## 3 LEPR 3953 ENSG00000116678 protein-coding 0.41 0.913 8.8607e-05
## uniprotids protein_classid protein_class_name
## 1 P48357 DTO_05007599 Signaling
## 2 P48357 DTO_05007599 Signaling
## 3 P48357 DTO_05007599 Signaling
## disease_name diseaseType diseaseUMLSCUI
## 1 Obesity [disease] C0028754
## 2 Diabetes Mellitus, Non-Insulin-Dependent [disease] C0011860
## 3 Diabetes Mellitus [disease] C0011849
## diseaseClasses_MSH
## 1 Pathological Conditions, Signs and Symptoms (C23), Nutritional and Metabolic Diseases (C18)
## 2 Endocrine System Diseases (C19), Nutritional and Metabolic Diseases (C18)
## 3 Endocrine System Diseases (C19), Nutritional and Metabolic Diseases (C18)
## diseaseClasses_UMLS_ST
## 1 Disease or Syndrome (T047)
## 2 Disease or Syndrome (T047)
## 3 Disease or Syndrome (T047)
## diseaseClasses_DO
## 1 disease of metabolism (0014667)
## 2 disease of metabolism (0014667), genetic disease (630)
## 3 disease of metabolism (0014667), genetic disease (630)
## diseaseClasses_HPO
## 1 Growth abnormality (01507)
## 2 Abnormality of the endocrine system (00818), Abnormality of metabolism/homeostasis (01939)
## 3 Abnormality of the endocrine system (00818), Abnormality of metabolism/homeostasis (01939)
## disease_prevalence_class disease_prevalence_geo_area disease_prevalence_type
## 1
## 2
## 3
## disease_inheritance numCTsupportingAssociation numPMIDs
## 1 17 14
## 2 2 5
## 3 3 1
## chemsIncludedInEvidenceBySource numChemsIncludedInEvidences
## 1 NULL NA
## 2 NULL NA
## 3 NULL NA
## numPMIDSWithChemsIncludedInEvidences numberChemsFiltered
## 1 NA NA
## 2 NA NA
## 3 NA NA
## numberPmidsWithChemsFiltered score yearInitial yearFinal evidence_index
## 1 NA 1.0 1986 2023 0.8847458
## 2 NA 1.0 2010 2024 0.9252874
## 3 NA 0.9 2003 2003 0.8800000
## evidence_level diseaseid
## 1 <NA> C0028754
## 2 <NA> C0011860
## 3 <NA> C0011849
The same query can be performed using the Gene Symbol (LEPR
) and the data source (TEXTMINING_HUMAN). Notice how the number of diseases associated to the Leptin Receptor has increased.
results <- gene2disease( gene = "LEPR",
vocabulary = "HGNC",
database = "TEXTMINING_HUMAN" )
results
## Object of class 'DataGeNET.DGN'
## . Search: single
## . Type: gene-disease
## . Database: TEXTMINING_HUMAN
## . Score: 0-1
## . Term: LEPR
## . Results: 453
The same query can be performed using the ENSEMBL gene identifier of the LEPR gene (ENSG00000116678
) by setting the vocabulary to ENSEMBL
.
results <- gene2disease( gene = "ENSG00000116678",
vocabulary = "ENSEMBL",
database = "TEXTMINING_HUMAN" )
results
## Object of class 'DataGeNET.DGN'
## . Search: single
## . Type: gene-disease
## . Database: TEXTMINING_HUMAN
## . Score: 0-1
## . Term: ENSG00000116678
## . Results: 453
Additionally, a minimum threshold for the score can be defined. In the example, a cutoff of score=c(0.3,1)
is used. Notice how the number of diseases associated to the Leptin Receptor drops when the score is restricted.
results <- gene2disease( gene = "LEPR",
vocabulary = "HGNC",
database = "ALL",
score =c(0.3,1))
results
## Object of class 'DataGeNET.DGN'
## . Search: single
## . Type: gene-disease
## . Database: ALL
## . Score: 0.3-1
## . Term: LEPR
## . Results: 99
In Table 2 are shown the top 10 diseases associated to the LEPR gene
tab <- unique(results@qresult[ ,c("gene_symbol", "disease_name","score", "yearInitial", "yearFinal")] )
knitr::kable(tab[1:10,], caption = "Top diseases associated to LEPR" )
gene_symbol | disease_name | score | yearInitial | yearFinal |
---|---|---|---|---|
LEPR | Obesity | 1.00 | 1966 | 2025 |
LEPR | Diabetes Mellitus, Non-Insulin-Dependent | 1.00 | 1966 | 2025 |
LEPR | Diabetes Mellitus | 0.90 | 1995 | 2025 |
LEPR | Hypertensive disease | 0.85 | 1999 | 2023 |
LEPR | Hyperphagia | 0.85 | 1986 | 2023 |
LEPR | Morbid obesity | 0.85 | 1994 | 2022 |
LEPR | Hyperinsulinism | 0.85 | 1986 | 2023 |
LEPR | Insulin Resistance | 0.80 | 1997 | 2024 |
LEPR | Hyperglycemia | 0.80 | 1986 | 2024 |
LEPR | Overweight | 0.80 | 2001 | 2025 |
Visualizing the diseases associated to a single gene
The disgenet2r package offers two options to visualize the results of querying a single gene in DISGENET: a network showing the diseases associated to the gene of interest (Gene-Disease Network
), and a network showing the MeSH Disease Classes of the diseases associated to the gene (Gene-Disease Class Network
). These graphics can be obtained by changing the class
argument in the plot function.
By default, the plot function produces a Gene-Disease Network
on a DataGeNET.DGN
object (Figure 1). In the Gene-Disease Network
the blue nodes are diseases, the pink nodes are genes, and the width of the edges is proportional to the score of the association. The prop
parameter allows to adjust the size of the nodes, while the eprop
parameter adjusts the width of the edges while keeping the proportionality to the score.
Figure 1: The Gene-Disease Network for the Leptin Receptor gene
Use interactive = TRUE
to display an interactive plot (Figure 2).
Figure 2: The interactive Gene-Disease Network for the Leptin Receptor gene
The results can also be visualized in a network in which diseases are grouped by the MeSH Disease Class if the class
argument is set to DiseaseClass (Gene-Disease Class Network
, Figure 3). In the Gene-Disease Class Network
, the node size of is proportional to the fraction of diseases in the disease class, with respect to the total number of diseases with disease classes associated to the gene. In the example, the Leptin Receptor is associated mainly to Nutritional and Metabolic Diseases. There diseases that do not have annotations to MeSH disease class will be shown as a warning.
Figure 3: The Disease Class Network for the Leptin Receptor Gene
Exploring the attributes of a gene
The gene2attribute function allows to retrieve the information for a specific gene, or list of genes.
## Object of class 'DataGeNET.DGN'
## . Search: single
## . Type: gene
## . Database: ALL
## . Score:
## . Term: 3953
The result shows the the Disease Specificity Index (DSI), and the Disease Pleiotropy Index (DPI) for the gene (Table 3).
description | geneid | gene_symbol | ensembl_ids | uniprotids | proteinClasses | ncbi_type | geneDSI | geneDPI | genepLI |
---|---|---|---|---|---|---|---|---|---|
leptin receptor | 3953 | LEPR | ENSG00000116678 | P48357 | DTO_05007599, DTO , Signaling | protein-coding | 0.41 | 0.913 | 8.86e-05 |
Exploring the evidences associated to a gene
You can extract the evidences associated to a particular gene using the function gene2evidence. The evidence types in DISGENET are scientific publications (PMIDs), and clinical trials (NCTIDs).
Additionally, you can explore the evidences for a specific gene-disease pair by specifying the disease identifier using the argument disease
.
results <- gene2evidence( gene = "LEPR", vocabulary = "HGNC",
disease ="UMLS_C3554225", database = "ALL")
results
## Object of class 'DataGeNET.DGN'
## . Search: single
## . Type: gene-evidence
## . Database: ALL
## . Score: 0-1
## . Term: LEPR
## . Results: 20
The results are shown in Table 4.
tab <- results@qresult
tab <- tab %>%
filter(reference_type == "PMID") %>%
select(reference, associationType, pmYear, sentence) %>% arrange(desc(pmYear))
tab <- tab %>% dplyr::rename(Year=pmYear, Sentence = sentence, pmid = reference)
tab %>% dplyr::mutate( pmid = kableExtra::cell_spec(pmid, link = paste0("https://pubmed.ncbi.nlm.nih.gov/", pmid) ) ) %>%
knitr::kable(format = 'markdown', row.names = F, caption = "Evidences supporting the association between LEPR & LEPTIN RECEPTOR DEFICIENCY" )
pmid | associationType | Year | Sentence |
---|---|---|---|
25751111 | GeneticVariation | 2015 | Seven novel deleterious LEPR mutations found in early-onset obesity: a ΔExon6-8 shared by subjects from Reunion Island, France, suggests a founder effect. |
25751111 | Causal Mutation | 2015 | Seven novel deleterious LEPR mutations found in early-onset obesity: a ΔExon6-8 shared by subjects from Reunion Island, France, suggests a founder effect. |
24319006 | Causal Mutation | 2014 | Novel LEPR mutations in obese Pakistani children identified by PCR-based enrichment and next generation sequencing. |
23616257 | Causal Mutation | 2014 | Whole-exome sequencing identifies novel LEPR mutations in individuals with severe early onset obesity. |
24611737 | CausalMutation | 2014 | Novel variants in the MC4R and LEPR genes among severely obese children from the Iberian population. |
22810975 | GeneticVariation | 2012 | Variants in the LEPR gene are nominally associated with higher BMI and lower 24-h energy expenditure in Pima Indians. |
18703626 | CausalMutation | 2008 | Functional characterization of naturally occurring pathogenic mutations in the human leptin receptor. |
17229951 | Genetic Variation | 2007 | Clinical and molecular genetic spectrum of congenital deficiency of the leptin receptor. |
17229951 | CausalMutation | 2007 | Clinical and molecular genetic spectrum of congenital deficiency of the leptin receptor. |
16284652 | CausalMutation | 2005 | Complete rescue of obesity, diabetes, and infertility in db/db mice by neuron-specific LEPR-B transgenes. |
12646666 | GeneticVariation | 2003 | Binge eating as a major phenotype of melanocortin 4 receptor gene mutations. |
9537324 | Genetic Variation | 1998 | A mutation in the human leptin receptor gene causes obesity and pituitary dysfunction. |
9537324 | CausalMutation | 1998 | A mutation in the human leptin receptor gene causes obesity and pituitary dysfunction. |
9537324 | GeneticVariation | 1998 | A mutation in the human leptin receptor gene causes obesity and pituitary dysfunction. |
9860295 | GeneticVariation | 1998 | Transmission disequilibrium and sequence variants at the leptin receptor gene in extremely obese German children and adolescents. |
9144432 | GeneticVariation | 1997 | Amino acid variants in the human leptin receptor: lack of association to juvenile onset obesity. |
To visualize the results when there are many evidences, we suggest to use plot the results using the argument Points
(Figure 4). It is important to set the parameter limit
to 10,000, in order to include all the evidences in the plot.
results <- gene2evidence( gene = "LEPR", vocabulary = "HGNC",
database = "ALL", score=c(0.7,1) )
plot(results, type="Points", interactive=T, limit=10000)
Figure 4: The Evidences plot for the Leptin Receptor gene
Searching multiple genes
The gene2disease function can also receive as input a list of genes, either as Entrez NCBI Gene Identifiers or Gene Symbols. In the example, we show how to create a vector with the Gene Symbols of several genes belonging to the family of voltage-gated potassium channels (Table 5) and then, we apply the function gene2disease.
Name | Description |
---|---|
KCNE1 | potassium channel, voltage gated subfamily E regulatory beta subunit 1 |
KCNE2 | potassium channel, voltage gated subfamily E regulatory beta subunit 2 |
KCNH1 | potassium channel, voltage gated eag related subfamily H, member 1 |
KCNH2 | potassium channel, voltage gated eag related subfamily H, member 2 |
KCNG1 | potassium voltage-gated channel modifier subfamily G member 1 |
Creating the vector with the list of genes belonging to the voltage-gated potassium channel family.
The gene2disease function also requires the user to specify the source database using the argument database
, and optionally, the DISGENET score
can also be applied to filter the results.
## Your query has 1 page.
## Warning in gene2disease(gene = myListOfGenes, database = "ALL", score = c(0.5, :
## One or more of the genes in the list is not in DISGENET ( 'ALL' ):
## - KCNG1
## Object of class 'DataGeNET.DGN'
## . Search: list
## . Type: gene-disease
## . Database: ALL
## . Score: 0.5-1
## . Term: KCNE1 ... KCNH2
## . Results: 46
In Table 6, the top 10 diseases associated to the list of genes belonging to the voltage-gated potassium channel family.
tab <- results@qresult[ ,c("gene_symbol", "disease_name","score", "yearInitial", "yearFinal")] %>% unique() %>%
arrange(desc(score), yearInitial)
knitr::kable(tab[1:10,], caption = "Top GDAs for the list of genes belonging to the voltage-gated potassium channel family")
gene_symbol | disease_name | score | yearInitial | yearFinal |
---|---|---|---|---|
KCNH2 | Long QT Syndrome | 1.0 | 1970 | 2025 |
KCNH2 | Cardiac Arrhythmia | 1.0 | 1975 | 2025 |
KCNH2 | Long Qt Syndrome 2 | 1.0 | 1990 | 2025 |
KCNE1 | Jervell-Lange Nielsen Syndrome | 1.0 | 1993 | 2025 |
KCNE1 | Long QT Syndrome | 1.0 | 1997 | 2025 |
KCNE2 | Long QT Syndrome | 1.0 | 1999 | 2025 |
KCNH2 | Short QT Syndrome 1 | 1.0 | 1999 | 2024 |
KCNE1 | LONG QT SYNDROME 5 | 0.9 | 1991 | 2024 |
KCNH2 | Romano-Ward Syndrome | 0.9 | 1993 | 2020 |
KCNH2 | Prolonged QT interval | 0.9 | 1995 | 2025 |
Visualizing the diseases associated to multiple genes
By default, plotting a DataGeNET.DGN
resulting of the query with a list of genes produces a Gene-Disease Network
where the blue nodes are diseases, the pink nodes are genes, and the width of the edges is proportional to the score of the association (Figure 5).
Figure 5: The Gene-Disease Network for a list of genes belonging to the voltage-gated potassium channel family
Set the argument interactive = TRUE to see an interactive network (Figure 6).
Figure 6: The interactive Gene-Disease Network for a list of genes belonging to the voltage-gated potassium channel family
Setting the argument type
to Heatmap produces a Gene-Disease Heatmap
(Figure 7), where the scale of colors is proportional to the score of the GDA. The argument limit
can be used to limit the number of rows to the top scoring GDAs. The argument nchars
can be used to limit the length of the name of the disease. By default, the plot shows the 50 highest scoring GDAs.
Figure 7: The Gene-Disease Heatmap for a list of genes belonging to the voltage-gated potassium channel family
These results can also be visualized as a Gene-Disease Class Heatmap
by setting the argument type
to Heatmap and class
to DiseaseClass (Figure 8). In this case, diseases are grouped by the their MeSH disease classes, and the color scale is proportional to the percentage of diseases in each MeSH disease class. In the example, genes are associated mainly to Cardiovascular Diseases, and to Congenital, Hereditary, and Neonatal Diseases and Abnormalities.
Figure 8: The Gene-Disease Class Heatmap for a list of genes belonging to the voltage-gated potassium channel family
Alternative, set the arguments type
to Network and class
to DiseaseClass to generate a Gene-Disease Class Network
(Figure 9).
Figure 9: The Gene-Disease Class Network for a list of genes belonging to the voltage-gated potassium channel family
Exploring the evidences associated to a list of genes
First, create the object gene-evidence
using the gene2evidence function.
## Your query has 35 pages.
To visualize the results set the argument class=Points
(Figure 10).
Figure 10: The Evidences plot for a list of genes belonging to the voltage-gated potassium channel family
Exploring the Clinical trials associated to a list of genes
First, create the object gene-evidence
using the gene2evidence function.
results <- gene2evidence(gene = c("MMP1", "MMP2", "MMP3", "MMP9", "MMP10"),
database = "CLINICALTRIALS", verbose = TRUE )
## Your query has 17 pages.
To visualize the results set the argument class=Points
and the argument reference_type
to NCTID (Figure 11).
Figure 11: The Evidences plot for a list of MMPs in clinical trials
Searching by gene and chemical
You can search GDAs by chemicals by specifying a chemical identifier using the chemical filter in the gene2disease function. Table 7 shows the diseases associated to LEPR associated to metformin.
results <- gene2disease( gene = "LEPR", vocabulary = "HGNC",
database = "TEXTMINING_HUMAN",
chemical = "CHEMBL_CHEMBL1431" )
results
## Object of class 'DataGeNET.DGN'
## . Search: single
## . Type: gene-disease
## . Database: TEXTMINING_HUMAN
## . Score: 0-1
## . Term: LEPR
## . Results: 6
tab <- results@qresult
tab <-tab%>% dplyr::select(chemical_name, gene_symbol, disease_name, score)
knitr::kable(tab, caption = "GDAs for LEPR and metformin")
chemical_name | gene_symbol | disease_name | score |
---|---|---|---|
Metformin | LEPR | Hyperinsulinism | 0.85 |
Metformin | LEPR | Polycystic Ovary Syndrome | 0.45 |
Metformin | LEPR | Steatohepatitis | 0.35 |
Metformin | LEPR | Schizophrenia | 0.25 |
Metformin | LEPR | Pulmonary arterial hypertension | 0.10 |
Metformin | LEPR | Idiopathic pulmonary arterial hypertension | 0.10 |
Retrieving the chemicals associated to a gene
For GDAs that have a chemical annotation, we can perform a query with a gene, or list of genes, to retrieve the chemicals annotated to this associations.
results <- gene2chemical( gene = "PDGFRA",
vocabulary = "HGNC",
database = "TEXTMINING_HUMAN" , score = c(0.8,1))
results
## Object of class 'DataGeNET.DGN'
## . Search: single
## . Type: gene-chemical
## . Database: TEXTMINING_HUMAN
## . Score: 0.8-1
## . Term: PDGFRA
## . Results: 17
tab <- results@qresult
tab <-tab %>% dplyr::filter(reference_type == "PMID") %>% dplyr::select(disease_name, chemical_name, chemical_effect,sentence, reference, pmYear)
tab <- tab %>% dplyr::rename( Disease = disease_name,
Chemical = chemical_name, `Chemical effect` = chemical_effect,
Year=pmYear, Sentence = sentence, pmid = reference) %>% dplyr::arrange(desc(Year))
tab[1:10,] %>% dplyr::mutate(
pmid = kableExtra::cell_spec(pmid, link = paste0("https://pubmed.ncbi.nlm.nih.gov/", pmid ) )) %>%
knitr::kable(format = 'markdown', row.names = F, caption = "Selection of chemicals associated to PDGFRA" )
Disease | Chemical | Chemical effect | Sentence | pmid | Year |
---|---|---|---|---|---|
Gastrointestinal Stromal Tumors | 2-deoxy-2-((18)F)fluoro-aldehydo-D-glucose | other | 18F-Fluorodeoxyglucose Uptake in PDGFRA-Mutant Gastrointestinal Stromal Tumors. | 39853981 | 2025 |
Gastrointestinal Stromal Tumors | Imatinib | therapeutic|therapeutic | Ripretinib, an oral switch-control inhibitor of KIT tyrosine kinase and platelet-derived growth factor receptor alpha kinase, is approved for adults with advanced gastrointestinal stromal tumor who received prior treatment with three or more kinase inhibitors, including imatinib. | 40684393 | 2025 |
Gastrointestinal Stromal Tumors | Ripretinib | therapeutic|therapeutic | Ripretinib, an oral switch-control inhibitor of KIT tyrosine kinase and platelet-derived growth factor receptor alpha kinase, is approved for adults with advanced gastrointestinal stromal tumor who received prior treatment with three or more kinase inhibitors, including imatinib. | 40684393 | 2025 |
Gastrointestinal Stromal Tumors | Imatinib | therapeutic | First-line imatinib therapy can be employed to treat GISTs harboring mutations in the tyrosine-protein kinase KIT (KIT) and platelet-derived growth factor receptor α (PDGFRα) genes to reduce the tumor size to resectable levels and minimize surgical risks. | 40276085 | 2025 |
Eosinophilia | Imatinib | toxicity | Clonal eosinophilia with exclusive pulmonary involvement driven by PDGFRA rearrangement treated with imatinib: A case report. | 40115037 | 2025 |
Gastrointestinal Stromal Tumors | Avapritinib | therapeutic | Avapritinib is the only drug for adult patients with PDGFRA exon 18 mutated unresectable or metastatic gastrointestinal stromal tumor (GIST). | 38803186 | 2024 |
Gastrointestinal Stromal Tumors | Avapritinib | therapeutic | Avapritinib is the only potent and selective inhibitor approved for the treatment of D842V-mutant gastrointestinal stromal tumors (GIST), the most common primary mutation of the platelet-derived growth factor receptor α (PDGFRA). | 38167404 | 2024 |
Gastrointestinal Stromal Tumors | Imatinib | therapeutic|therapeutic | The most common driver mutations are KIT and PDGFRA which can be treated with imatinib or avapritinib (for PDGFRA D842V-mutant GIST), respectively. | 38756640 | 2024 |
Gastrointestinal Stromal Tumors | Avapritinib | therapeutic|therapeutic | The most common driver mutations are KIT and PDGFRA which can be treated with imatinib or avapritinib (for PDGFRA D842V-mutant GIST), respectively. | 38756640 | 2024 |
Gastrointestinal Stromal Tumors | Imatinib | therapeutic | This review focuses on the mechanisms contributing to drug resistance phenotype in GIST, such as primary imatinib-resistant mutants, secondary mutations, non-covalent binding of TKI to its target, tumor heterogeneity, re-activation of pro-survival/proliferation pathways through non-KIT/PDGFRA kinases, and loss of therapeutic targets in wild-type GIST. | 39441520 | 2024 |
To visualize the results use the plot function.
Figure 12: The Gene-Chemical Network for PDGFRA
Searching by disease
The disease2gene function allows to retrieve the genes associated to a disease, or a list of diseases. The function uses as input the disease, or list of diseases of interest (each disease should have the format: IDENT_ID where IDENT is one of UMLS, ICD9CM, ICD10, MESH, OMIM, DO, EFO, NCI, HPO, MONDO, or ORDO), ID is the identifier in the vocabulary, and the database (by default, CURATED
). A threshold value for the score can be set, like in the gene2disease function.
In the example, we will use the disease2gene function to retrieve the genes associated to the UMLS CUI C0036341. This function also receives as input the database, in the example, CURATED, and a score range, in the example, from 0.8 to 1.
results <- disease2gene( disease = "UMLS_C0036341",
database = "CURATED",
score = c( 0.8,1 ) )
results
## Object of class 'DataGeNET.DGN'
## . Search: single
## . Type: disease-gene
## . Database: CURATED
## . Score: 0.8-1
## . Term: UMLS_C0036341
## . Results: 157
In Table 9, the top 10 genes associated to UMLS CUI C0036341.
tab <- unique(results@qresult[ ,c("gene_symbol", "disease_name","score", "yearInitial", "yearFinal")] ) %>%
arrange(desc(score), yearInitial)
knitr::kable(tab[1:10,], caption = "Top 10 genes associated to Schizophrenia")
gene_symbol | disease_name | score | yearInitial | yearFinal |
---|---|---|---|---|
DRD3 | Schizophrenia | 1 | 1999 | 1999 |
DRD2 | Schizophrenia | 1 | 2000 | 2011 |
BDNF | Schizophrenia | 1 | 2003 | 2008 |
RTN4R | Schizophrenia | 1 | 2004 | 2017 |
HTR2A | Schizophrenia | 1 | 2004 | 2008 |
COMT | Schizophrenia | 1 | 2005 | 2010 |
MTHFR | Schizophrenia | 1 | 2006 | 2009 |
TNF | Schizophrenia | 1 | 2006 | 2006 |
ZNF804A | Schizophrenia | 1 | 2008 | 2018 |
DISC1 | Schizophrenia | 1 | 2010 | 2011 |
Visualizing the genes associated to a single disease
There are two options to visualize the results from searching a single disease: a Gene-Disease Network
showing the genes related to the disease of interest (Figure 13), and a Disease-Protein Class Network
with the genes grouped grouped by the the Drug Target Ontology Protein Class (Figure 14).
Figure 13 shows the default Gene-Disease Network
for Schizophrenia. As in the case of the gene2disease function, the blue nodes is the disease, the pink nodes are genes, and the width of the edges is proportional to the score of the association.
Figure 13: The Gene-Disease Network for genes associated to Schizophrenia
Alternatively, in the Disease-Protein Class Network
, genes are grouped by the the Drug Target Ontology Protein Class (Figure 14). This is a better choice when there is a large number of genes associated to the disease. This plot uses as class
argument ProteinClass. The resulting network will show in blue the disease, and in green the Protein Classes of the genes associated to the disease. The node size is proportional to the number of genes in the Protein Class. In the example, the largest proportion of the genes associated to Schizophrenia are G-protein coupled receptors. Notice again that not all genes have annotations to Protein classes.
Figure 14: The Protein Class-Disease Network for genes associated to Schizophrenia
The same results are obtained when querying DISGENET with the MeSH identifier for Schizophrenia (D012559).
results <- disease2gene( disease = "MESH_D012559",
database = "CURATED",
score = c( 0.8,1 ) )
results
## Object of class 'DataGeNET.DGN'
## . Search: single
## . Type: disease-gene
## . Database: CURATED
## . Score: 0.8-1
## . Term: MESH_D012559
## . Results: 157
The same results are obtained when querying DISGENET with the OMIM identifier for Schizophrenia (181500).
results <- disease2gene( disease = "OMIM_181500",
database = "CURATED",
score = c( 0.8,1 ) )
results
## Object of class 'DataGeNET.DGN'
## . Search: single
## . Type: disease-gene
## . Database: CURATED
## . Score: 0.8-1
## . Term: OMIM_181500
## . Results: 157
The same results are obtained when querying DISGENET with the ICD9-CM identifier for Schizophrenia (295).
## Object of class 'DataGeNET.DGN'
## . Search: single
## . Type: disease-gene
## . Database: CURATED
## . Score: 0.8-1
## . Term: ICD9CM_295
## . Results: 157
The same results are obtained when querying DISGENET with the NCI identifier for Schizophrenia (C3362).
## Object of class 'DataGeNET.DGN'
## . Search: single
## . Type: disease-gene
## . Database: CURATED
## . Score: 0.8-1
## . Term: NCI_C3362
## . Results: 157
The same results are obtained when querying DISGENET with the DO identifier for Schizophrenia (5419).
results <- disease2gene( disease = "HPO_HP:0100753",
database = "CURATED",
score = c( 0.8,1 ) )
results
## Object of class 'DataGeNET.DGN'
## . Search: single
## . Type: disease-gene
## . Database: CURATED
## . Score: 0.8-1
## . Term: HPO_HP:0100753
## . Results: 157
Searching by disease and chemical
You can filter the results to find associations that are mentioned in the context of a chemical, like the example below.
results <- disease2gene( disease = "UMLS_C0678222", chemical = "CHEMBL_CHEMBL83",
database = "ALL" , n_pags = 1 )
## Notice that your query has a maximum of 10 pages.
## By indicating n_pags = 1, your query of 10 pages has been reduced to 1 pages.
## Object of class 'DataGeNET.DGN'
## . Search: single
## . Type: chemical-gda
## . Database: ALL
## . Score: 0-1
## . Term: UMLS_C0678222
## . Results: 100
tab <- unique(results@qresult[ ,c("gene_symbol", "disease_name","score", "chemical_name", "chemicalid")] )%>% dplyr::arrange(desc(score))
knitr::kable(tab[1:10,], caption = "Top GDAs associated to Breast Carcinoma")
gene_symbol | disease_name | score | chemical_name | chemicalid |
---|---|---|---|---|
BRCA2 | Breast Carcinoma | 1.00 | Tamoxifen | CHEMBL83 |
ESR1 | Breast Carcinoma | 1.00 | Tamoxifen | CHEMBL83 |
TP53 | Breast Carcinoma | 1.00 | Tamoxifen | CHEMBL83 |
CHEK2 | Breast Carcinoma | 1.00 | Tamoxifen | CHEMBL83 |
PALB2 | Breast Carcinoma | 1.00 | Tamoxifen | CHEMBL83 |
BRIP1 | Breast Carcinoma | 0.95 | Tamoxifen | CHEMBL83 |
BRCA1 | Breast Carcinoma | 0.90 | Tamoxifen | CHEMBL83 |
CAV1 | Breast Carcinoma | 0.90 | Tamoxifen | CHEMBL83 |
CDH1 | Breast Carcinoma | 0.90 | Tamoxifen | CHEMBL83 |
EGFR | Breast Carcinoma | 0.90 | Tamoxifen | CHEMBL83 |
Retrieving the chemicals associated to a disease
For GDAs that have a chemical annotation, we can perform a query with a disease, or list of disease, to retrieve the chemicals annotated to this associations.
results <- disease2chemical( disease = "UMLS_C0010674",
database = "TEXTMINING_MODELS" , score = c(0.8,1))
results
## Object of class 'DataGeNET.DGN'
## . Search: single
## . Type: disease-chemical
## . Database: TEXTMINING_MODELS
## . Score: 0.8-1
## . Term: UMLS_C0010674
## . Results: 64
tab <- results@qresult
tab <-tab %>% dplyr::filter(reference_type =="PMID") %>% dplyr::select(gene_symbol, chemical_name,chemical_effect ,sentence, reference, pmYear)
tab <- tab %>% dplyr::rename(Gene = gene_symbol, Chemical = chemical_name,
`Chemical Effect`=chemical_effect , Year=pmYear, Sentence = sentence, pmid = reference) %>% dplyr::arrange(desc(Year))
tab[1:10,] %>% dplyr::mutate(
pmid = kableExtra::cell_spec(pmid, link = paste0("https://pubmed.ncbi.nlm.nih.gov/", pmid) )) %>%
knitr::kable(format = 'markdown', row.names = F, caption = "Top chemicals associated to Cystic Fibrosis" )
Gene | Chemical | Chemical Effect | Sentence | pmid | Year |
---|---|---|---|---|---|
CFTR | Elexacaftor | therapeutic|therapeutic|therapeutic | Triple-combination CFTR modulators, including ivacaftor/tezacaftor/elexacaftor with an additional class 2 corrector, are now the standard of care for most CF patients, transforming the outlook for this disease. | 39882833 | 2025 |
CFTR | Tezacaftor | therapeutic|therapeutic|therapeutic | Triple-combination CFTR modulators, including ivacaftor/tezacaftor/elexacaftor with an additional class 2 corrector, are now the standard of care for most CF patients, transforming the outlook for this disease. | 39882833 | 2025 |
CFTR | Ivacaftor | therapeutic|therapeutic|therapeutic | Triple-combination CFTR modulators, including ivacaftor/tezacaftor/elexacaftor with an additional class 2 corrector, are now the standard of care for most CF patients, transforming the outlook for this disease. | 39882833 | 2025 |
CFTR | Ivacaftor | therapeutic|other | This allosteric inhibitory mechanism readily explains our observations that pig CFTR, which preserves all the amino acid residues involved in Inh-172 binding, exhibits a much-reduced sensitivity to Inh-172 and that the apparent affinity of Inh-172 is altered by the CF drug ivacaftor (i.e., VX-770) which enhances CFTR’s activity through binding to a site also comprising TM8. | 39107303 | 2024 |
CFTR | Isoniazid | therapeutic|other | This allosteric inhibitory mechanism readily explains our observations that pig CFTR, which preserves all the amino acid residues involved in Inh-172 binding, exhibits a much-reduced sensitivity to Inh-172 and that the apparent affinity of Inh-172 is altered by the CF drug ivacaftor (i.e., VX-770) which enhances CFTR’s activity through binding to a site also comprising TM8. | 39107303 | 2024 |
CFTR | Linaclotide | other | These data provide further insights into the action of linaclotide and how DRA may compensate for loss of CFTR in regulating luminal pH. Linaclotide may be a useful therapy for CF individuals with impaired bicarbonate secretion. | 38869953 | 2024 |
CFTR | 2,6-DIAMINOPURINE | other | The ability of DAP to correct various endogenous UGA nonsense mutations in the CFTR gene and to restore its function in mice, in organoids derived from murine or patient cells, and in cells from patients with cystic fibrosis reveals the potential of such readthrough-stimulating molecules in developing a therapeutic approach. | 36641622 | 2023 |
CFTR | Cyclic adenosine monophosphate | other | Cystic fibrosis (CF) is a life-threatening genetic disorder, caused by mutations in the CF transmembrane-conductance regulator gene (cftr) that encodes CFTR, a cAMP-activated chloride and bicarbonate channel. | 36289287 | 2022 |
CFTR | LYSOPHOSPHATIDYLCHOLINE | other|other|other | We have previously demonstrated that airway conditioning with lysophosphatidylcholine (LPC) followed by delivery of a HIV-1-based lentiviral (LV) vector functionally corrects the CF transmembrane conductance regulator (CFTR) defect in the nasal airways of CF mice. | 33446042 | 2021 |
CFTR | LYSOPHOSPHATIDYLCHOLINE | other|other|other | We have previously demonstrated that airway conditioning with lysophosphatidylcholine (LPC) followed by delivery of a HIV-1-based lentiviral (LV) vector functionally corrects the CF transmembrane conductance regulator (CFTR) defect in the nasal airways of CF mice. | 33446042 | 2021 |
To visualize the results use the plot function.
Figure 15: The Disease-Chemical Network associated to Cystic Fibrosis
Exploring the attributes of a disease
The disease2attribute function allows to retrieve the information for a specific disease
## Object of class 'DataGeNET.DGN'
## . Search: single
## . Type: disease
## . Database: ALL
## . Score:
## . Term: UMLS_C0036341
## . Results: 12
The results (Table 12) show the mappings to different disease vocabularies, and the disease type.
tab <- results@qresult %>% arrange(desc(vocabulary)) %>% unique()
knitr::kable(tab, caption = "Disease attributes for Schizophrenia")
vocabulary | code | disease_name | type | diseaseClasses_UMLS_ST | diseaseClasses_HPO | diseaseClasses_DO | diseaseClasses_MSH |
---|---|---|---|---|---|---|---|
UMLS | C0036341 | Schizophrenia | disease | Mental or Behavioral Dysfunction (T048) | Abnormality of the nervous system (00707) | disease of mental health (150) | Mental Disorders (F03) |
OMIM | 181500 | Schizophrenia | disease | Mental or Behavioral Dysfunction (T048) | Abnormality of the nervous system (00707) | disease of mental health (150) | Mental Disorders (F03) |
NCI | C3362 | Schizophrenia | disease | Mental or Behavioral Dysfunction (T048) | Abnormality of the nervous system (00707) | disease of mental health (150) | Mental Disorders (F03) |
MSH | D012559 | Schizophrenia | disease | Mental or Behavioral Dysfunction (T048) | Abnormality of the nervous system (00707) | disease of mental health (150) | Mental Disorders (F03) |
MONDO | 0005090 | Schizophrenia | disease | Mental or Behavioral Dysfunction (T048) | Abnormality of the nervous system (00707) | disease of mental health (150) | Mental Disorders (F03) |
ICD9CM | 295.90 | Schizophrenia | disease | Mental or Behavioral Dysfunction (T048) | Abnormality of the nervous system (00707) | disease of mental health (150) | Mental Disorders (F03) |
ICD9CM | 295.9 | Schizophrenia | disease | Mental or Behavioral Dysfunction (T048) | Abnormality of the nervous system (00707) | disease of mental health (150) | Mental Disorders (F03) |
ICD9CM | 295 | Schizophrenia | disease | Mental or Behavioral Dysfunction (T048) | Abnormality of the nervous system (00707) | disease of mental health (150) | Mental Disorders (F03) |
ICD10 | F20 | Schizophrenia | disease | Mental or Behavioral Dysfunction (T048) | Abnormality of the nervous system (00707) | disease of mental health (150) | Mental Disorders (F03) |
ICD10 | F20.9 | Schizophrenia | disease | Mental or Behavioral Dysfunction (T048) | Abnormality of the nervous system (00707) | disease of mental health (150) | Mental Disorders (F03) |
HPO | HP:0100753 | Schizophrenia | disease | Mental or Behavioral Dysfunction (T048) | Abnormality of the nervous system (00707) | disease of mental health (150) | Mental Disorders (F03) |
DO | 5419 | Schizophrenia | disease | Mental or Behavioral Dysfunction (T048) | Abnormality of the nervous system (00707) | disease of mental health (150) | Mental Disorders (F03) |
Retrieving the UMLS CUIs via other vocabularies
It is possible to obtain the CUIs that map to an identifier of interest (example, ICD9CM, MSH, or OMIM) using the the get_umls_from_vocabulary function.
## Object of class 'DataGeNET.DGN'
## . Search: single
## . Type: disease
## . Database: ALL
## . Score:
## . Term: MSH_D012559
## . Results: 2
The results are shown in Table 13.
VOCABULARIES | code | disease_name |
---|---|---|
MSH | D012559 | Schizophrenia |
UMLS | C0036341 | Schizophrenia |
Finding the CUI associated to the name of a disease of interest
It is possible to obtain the CUIS that correspond to a disease(s) of interest using the the get_umls_from_vocabulary function. For that, we should specify the parameter vocabulary = "NAME"
. Use the the parameter limit
to change the number of CUIs that are retrieved.
## Object of class 'DataGeNET.DGN'
## . Search: single
## . Type: disease
## . Database: ALL
## . Score:
## . Term: long QT
## . Results: 10
The results are shown in Table 14.
tab <-results@qresult
knitr::kable(tab, caption = "List of CUIs that map to long QT", row.names = F)
VOCABULARIES | code | disease_name |
---|---|---|
UMLS | C1141890 | Familial long QT syndrome (disorder) |
UMLS | C2678485 | Long Qt Syndrome 9 |
UMLS | C1832916 | Timothy syndrome |
UMLS | C1867904 | LONG QT SYNDROME 5 |
UMLS | C1859062 | LONG QT SYNDROME 3 |
UMLS | C0023976 | Long QT Syndrome |
UMLS | C2732979 | Acquired long QT syndrome (disorder) |
UMLS | C0152154 | Prolonged labor |
UMLS | C1833154 | Long Qt Syndrome 4 |
UMLS | C0151878 | Prolonged QT interval |
Exploring the evidences associated to a disease
To explore the evidences supporting the associations for Schizophrenia use the function disease2evidence.
results <- disease2evidence( disease = "UMLS_C0036341",
type = "GDA",
database = "CURATED",
score = c( 0.8,1 ) )
results
## Object of class 'DataGeNET.DGN'
## . Search: single
## . Type: disease-evidence
## . Database: CURATED
## . Score: 0.8-1
## . Term: UMLS_C0036341
## . Results: 424
A selection of evidences is shown in Table 15.
tab <- results@qresult
tab <-tab[tab$reference_type == "PMID" & tab$pmYear > 2013 & tab$source =="PSYGENET", ]
tab <- tab[ order(-tab$pmYear), c("gene_symbol","source", "associationType", "sentence", "reference", "pmYear")][1:5,]
tab <- tab %>% dplyr::rename(Gene = gene_symbol, Year=pmYear, Sentence = sentence, pmid = reference)
tab %>% dplyr::mutate(
pmid = kableExtra::cell_spec(pmid, link = paste0("https://pubmed.ncbi.nlm.nih.gov/", pmid) )) %>%
knitr::kable(format = 'markdown', row.names = F, caption = "Evidences supporting the association for Schizophrenia" )
Gene | source | associationType | Sentence | pmid | Year |
---|---|---|---|---|---|
MAGI2 | PSYGENET | Biomarker | One of the rare CNVs found in SZ cohorts is the duplication of Synaptic Scaffolding Molecule (S-SCAM, also called MAGI-2), which encodes a postsynaptic scaffolding protein controlling synaptic AMPA receptor levels, and thus the strength of excitatory synaptic transmission. | 25653350 | 2015 |
PPARA | PSYGENET | Biomarker | We report significant increases in PPAR?, SREBP1, IL-6 and TNF?, and decreases in PPAR? and C/EPB? and mRNA levels from patients with schizophrenia, with additional BMI interactions, characterizing dysregulation of genes relating to metabolic-inflammation in schizophrenia. | 25433960 | 2015 |
NOTCH4 | PSYGENET | Biomarker | Our data indicate that NOTCH4 polymorphism can influence clinical symptoms in Slovenian patients with schizophrenia. | 25529856 | 2015 |
GRIN2A | PSYGENET | Biomarker | GRIN2A (GT)21 may play a significant role in the etiology of schizophrenia among the Chinese Han population of Shaanxi. | 25958346 | 2015 |
GRM7 | PSYGENET | Biomarker | In summary, our results indicate that the GRM7 SNPs rs13353402 and rs1531939 might be associated with schizophrenia in Chinese Han population. | 26254163 | 2015 |
Additionally, you can explore the evidences for a specific gene-disease pair by specifying the gene identifier using the argument gene
.
results <- disease2evidence( disease = "UMLS_C0036341",
gene = c("DRD2", "DRD3"),
type = "GDA",
database = "ALL",
score = c( 0.5,1 ) )
results
## Object of class 'DataGeNET.DGN'
## . Search: single
## . Type: disease-evidence
## . Database: ALL
## . Score: 0.5-1
## . Term: UMLS_C0036341
## . Results: 721
The more recent papers are shown in the Table 16.
tab <- results@qresult
tab <- tab %>%
filter(reference_type == "PMID") %>%
select(gene_symbol, associationType, reference, sentence, pmYear) %>% arrange(desc(pmYear)) %>% head(10)
tab <- tab %>% dplyr::rename(Gene = gene_symbol, Year=pmYear, Sentence = sentence, pmid = reference)
tab %>% dplyr::mutate(
pmid = kableExtra::cell_spec(pmid, link = paste0("https://pubmed.ncbi.nlm.nih.gov/", pmid) )) %>%
knitr::kable(format = 'markdown', row.names = F, caption = "Evidences supporting the association between C0036341 & DRD2,DRD3" )
Gene | associationType | pmid | Sentence | Year |
---|---|---|---|---|
DRD2 | CausalOrContributing | 39730881 | Dysfunction of dopamine systems has long been considered a hallmark of schizophrenia, and nearly all current first-line medication treatments block dopamine D2 receptors. | 2025 |
DRD2 | GeneticVariation | 39993143 | In addition, the DRD2 rs1800497 genotype GA showed a reduced risk of schizophrenia in the male subgroup and the late-onset subgroup (>27 years of age). | 2025 |
DRD2 | AlteredExpression | 40665271 | These results suggest that hypermethylation and low expression of the DRD2 gene may be related to SCZ risk. | 2025 |
DRD2 | GeneticVariation | 40681517 | Here, we show that the selective dopamine D2 receptor deletion from parvalbumin interneurons, a mutation that results in schizophrenia-like phenotypes, causes intrinsic metabolic and immune defects in mice, in a similar way to what is described in schizophrenia patients. | 2025 |
DRD2 | CausalOrContributing | 40056428 | While antipsychotics blocking D2 DA receptors can be effective for positive symptoms of schizophrenia, none are approved by regulatory authorities for predominant negative or cognitive symptoms. | 2025 |
DRD3 | GeneticVariation | 39993143 | For DRD3 polymorphisms, the rs7631540 TC genotype was associated with schizophrenia in the female subgroup. | 2025 |
DRD2 | CausalOrContributing | 37422511 | We focus on schizophrenia and the dopamine D2 receptor (DRD2), hot flashes and the neurokinin B receptor (TACR3), cigarette smoking and receptors bound by nicotine (CHRNA5, CHRNA3, CHRNB4), and alcohol use and enzymes that help to break down alcohol (ADH1B, ADH1C, ADH7). | 2024 |
DRD2 | GeneticVariation | 38421437 | Our significant polymorphism findings, mainly those in DRD2 (rs1800497, rs1799978, and rs2734841), HTR2C (rs3813929), and HTR2A (rs6311), were largely consistent with earlier findings (predictors of RIS effectiveness in adult schizophrenia patients), confirming their validity for identifying ASD children with a greater likelihood of core symptom improvement compared to noncarriers/wild types. | 2024 |
DRD2 | CausalOrContributing | 38114631 | The Drd2 gene, encoding the dopamine D2 receptor (D2R), was recently indicated as a potential target in the etiology of lowered sociability (i.e., social withdrawal), a symptom of several neuropsychiatric disorders such as Schizophrenia and Major Depression. | 2024 |
DRD2 | GeneticVariation | 39187246 | DRD2 (rs6276) and DRD3 (rs6280, rs963468) polymorphisms can affect amisulpride tolerability since they are associated with the observed adverse reactions, including cardiac dysfunction and endocrine disorders in Chinese patients with schizophrenia. | 2024 |
Searching multiple diseases
The disease2gene function also accepts as input a list of diseases (each disease should have the format: IDENT_ID where IDENT is one of UMLS, ICD9CM, ICD10, MESH, OMIM, DO, EFO, NCI, HPO, MONDO, or ORDO), the database (by default, CURATED), and optionally, a value range for the score. In the example, we have selected a list of 10 diseases. Table 17 shows the UMLS CUIs and the corresponding disease names.
UMLS_CUI | Disease_Name |
---|---|
C0036341 | Schizophrenia |
C0036341 | Alzheimer’s Disease |
C0030567 | Parkinson Disease |
C0005586 | Bipolar Disorder |
Creating the vector with the list of diseases.
In the example, we will search in CURATED data, using a score range of 0.8-1.
results <- disease2gene(
disease = diseasesOfInterest,
database = "CURATED",
score =c(0.8,1),
verbose = TRUE )
## Your query has 5 pages.
In table 18, the top 10 genes associated to the list of diseases.
tab <- unique(results@qresult[ ,c("gene_symbol", "disease_name","score", "yearInitial", "yearFinal")] ) %>% dplyr::arrange(desc(score), yearInitial, desc(yearFinal))
knitr::kable(tab[1:10,], caption = "Top Genes associated to a list of diseases")
gene_symbol | disease_name | score | yearInitial | yearFinal |
---|---|---|---|---|
GBA1 | Parkinson Disease | 1 | 1987 | 2021 |
SNCA | Parkinson Disease | 1 | 1989 | 2021 |
APP | Alzheimer’s Disease | 1 | 1990 | 2023 |
PRKN | Parkinson Disease | 1 | 1993 | 2022 |
PSEN1 | Alzheimer’s Disease | 1 | 1993 | 2022 |
LRRK2 | Parkinson Disease | 1 | 1993 | 2021 |
APOE | Alzheimer’s Disease | 1 | 1993 | 2020 |
MAPT | Alzheimer’s Disease | 1 | 1993 | 2020 |
PSEN2 | Alzheimer’s Disease | 1 | 1993 | 2020 |
GRN | Alzheimer’s Disease | 1 | 1993 | 2020 |
Visualizing the genes associated to multiple diseases
The default plot of the results of querying DISGENET with a list of diseases produces a Gene-Disease Network
where the blue nodes are diseases, the pink nodes are genes, and the width of the edges is proportional to the score of the association (Figure 16).
Figure 16: The Gene-Disease Network associated to a list of diseases
To visualize the results as a Gene-Disease Heatmap
(Figure 17) change the argument class
to “Heatmap”. In this plot, the scale of colors is proportional to the score of the GDA. The argument limit
can be used to limit the number of rows to the top scoring GDAs when the results are large. By default, the plot shows the 50 highest scoring GDAs.
## [1] "Dataframe of 405 rows has been reduced to 20 rows."
Figure 17: The Gene-Disease Heatmap for genes associated to a list of diseases
A third visualization option is a Protein Class-Disease Heatmap
(Figure 18), in which genes are grouped by protein class. This plot is obtained by setting the class
argument to “ProteinClass”. In this case, the color of the heatmap is proportional to the percentage of genes for each disease in each protein class. This heatmap displays the protein classes associated to each disease.
Figure 18: The Protein Class-Disease Heatmap for genes associated to a list of diseases
A Protein Class-Disease Network
visualization is also possible (Figure 19).
Figure 19: The Protein Class-Disease Network for genes associated to a list of diseases
To explore the evidences supporting the associations, use the function disease2evidence.
results <- disease2evidence( disease = diseasesOfInterest,
type = "GDA",
score=c(0.5,1),
database = "CURATED" )
results
## Object of class 'DataGeNET.DGN'
## . Search: list
## . Type: disease-evidence
## . Database: CURATED
## . Score: 0.5-1
## . Term: UMLS_C0036341 ... UMLS_C0005586
## . Results: 3461
To visualize the results use the argument Points
(Figure 20).
Figure 20: The Evidences plot for a list of diseases
Searching by disease and chemical
The disease2gene function can also be used to retrieve genes mentioned in the context of a specific disease and chemical (Table 19)
results <- disease2gene( disease = "UMLS_C0030567",
database = "TEXTMINING_HUMAN",
chemical = "CHEMBL_CHEMBL1009")
results
## Object of class 'DataGeNET.DGN'
## . Search: single
## . Type: chemical-gda
## . Database: TEXTMINING_HUMAN
## . Score: 0-1
## . Term: UMLS_C0030567
## . Results: 85
tab <- results@qresult
tab <-tab%>% dplyr::select(gene_symbol, disease_name, chemical_name, score) %>% dplyr::arrange(desc(score))
knitr::kable(tab[1:10,], caption = "Top GDAs associated to Parkinson and levodopa")
gene_symbol | disease_name | chemical_name | score |
---|---|---|---|
BDNF | Parkinson Disease | Levodopa | 1 |
GBA1 | Parkinson Disease | Levodopa | 1 |
GDNF | Parkinson Disease | Levodopa | 1 |
MAOB | Parkinson Disease | Levodopa | 1 |
MAPT | Parkinson Disease | Levodopa | 1 |
PRKN | Parkinson Disease | Levodopa | 1 |
SNCA | Parkinson Disease | Levodopa | 1 |
TH | Parkinson Disease | Levodopa | 1 |
PINK1 | Parkinson Disease | Levodopa | 1 |
LRRK2 | Parkinson Disease | Levodopa | 1 |
To visualize the results use the function plot (Figure 20)
Figure 21: The Gene Disease Chemical Network for a disease and a drug
Retrieving the chemicals associated to a disease
To retrieve the chemicals mentioned in the GDAs involving a specific disease, we can use the disease2chemical function.
results <- disease2chemical( disease = "UMLS_C0030567",
database = "TEXTMINING_HUMAN" , score = c(0.5,1))
results
## Object of class 'DataGeNET.DGN'
## . Search: single
## . Type: disease-chemical
## . Database: TEXTMINING_HUMAN
## . Score: 0.5-1
## . Term: UMLS_C0030567
## . Results: 372
tab <- results@qresult
tab <-tab%>% dplyr::filter(reference_type == "PMID") %>% dplyr::select(gene_symbol, chemical_name, chemical_effect, sentence, reference, pmYear)
tab <- tab %>% dplyr::rename(Gene = gene_symbol, Chemical = chemical_name,
`Chemical Effect` = chemical_effect, Year=pmYear, Sentence = sentence, pmid = reference) %>% dplyr::arrange(desc(Year))
tab[1:10,] %>% dplyr::mutate(
pmid = kableExtra::cell_spec(pmid, link = paste0("https://pubmed.ncbi.nlm.nih.gov/", pmid))) %>%
knitr::kable(format = 'markdown', row.names = F, caption = "Top Chemicals associated to Parkinson" )
Gene | Chemical | Chemical Effect | Sentence | pmid | Year |
---|---|---|---|---|---|
SNCA | CALCIUM | other | Serine-129 phosphorylated α-synuclein drives mitochondrial dysfunction and calcium dysregulation in Parkinson’s disease model. | 40230488 | 2025 |
SNCA | LIXISENATIDE | other | Neuroprotective effects of lixisenatide against propagation of α-synuclein pathology in Parkinson’s disease. | 40145958 | 2025 |
SNCA | Dopamine | therapeutic | Short-Chain Fatty Acid Aggregates Alpha-Synuclein Accumulation and Neuroinflammation via GPR43-NLRP3 Signaling Pathway in a Model Parkinson’s Disease. Parkinson’s disease (PD) is characterized by the aggregation of α-synuclein (α-syn) and the loss of dopaminergic (DA) neurons, with growing evidence suggesting a significant role of gut microbiota and their metabolites in the disease’s pathogenesis. | 39904963 | 2025 |
SNCA | CINPANEMAB | other|other | Notably, this approach enables the isolation of conformation-specific antibodies against tau fibrils (Alzheimer’s disease) and α-synuclein fibrils (Parkinson’s disease) with combinations of high affinity, high conformational specificity and, in some cases, low off-target binding that rival or exceed those of clinical-stage antibodies specific for tau (zagotenemab) and α-synuclein (cinpanemab). | 40301692 | 2025 |
SNCA | ZAGOTENEMAB | other|other | Notably, this approach enables the isolation of conformation-specific antibodies against tau fibrils (Alzheimer’s disease) and α-synuclein fibrils (Parkinson’s disease) with combinations of high affinity, high conformational specificity and, in some cases, low off-target binding that rival or exceed those of clinical-stage antibodies specific for tau (zagotenemab) and α-synuclein (cinpanemab). | 40301692 | 2025 |
SNCA | Dopamine | therapeutic | This review comprehensively summarizes recent advances in biosensors for the early detection of PD, with a particular focus on the detection of two key biomarkers: dopamine (DA) and α-synuclein (α-syn). | 40422019 | 2025 |
TH | Dopamine | therapeutic | In contrast to the loss of dopaminergic neurons in the midbrain, we observed an increase in tyrosine hydroxylase-positive neurons in the Parkinson’s disease olfactory bulb, suggesting a potential role for dopamine in the hyposmia associated with the condition. | 40024917 | 2025 |
BDNF | Levodopa | therapeutic | Levodopa-synergistic CBT intervention improves Parkinson’s disease with anxiety disorder by regulating the BDNF/PI3K/AKT pathway. | 39937424 | 2025 |
SNCA | Luteolin | other|other|other | In vitro investigation indicated that Quercetin is the most effective for treating AD and PD due to its considerable inhibition of alpha-synuclein production, whereas Luteolin is the favorable one for preventing both diseases by mitigating effects during Rotenone treatment. | 39959708 | 2025 |
SNCA | Quercetin | other|other|other | In vitro investigation indicated that Quercetin is the most effective for treating AD and PD due to its considerable inhibition of alpha-synuclein production, whereas Luteolin is the favorable one for preventing both diseases by mitigating effects during Rotenone treatment. | 39959708 | 2025 |
To visualize the results use the function plot
Figure 22: The Network plot for chemicals associated to Parkinson Disease
Retrieving Variant-Disease Associations from DISGENET
Searching by variant
The variant2disease function receives a variant, or a list of variants as input, identified by the dbSNP identifier. It produces an object DataGeNET.DGN
, with Type = "variant-disease"
.
## Object of class 'DataGeNET.DGN'
## . Search: single
## . Type: variant-disease
## . Database: CURATED
## . Score: 0.2-1
## . Term: rs113488022
## . Results: 13
The results are shown in Table 21.
tab <- unique(results@qresult[ ,c("variantid", "disease_name","score", "yearInitial", "yearFinal")] ) %>% dplyr::arrange(desc(score), yearInitial, desc(yearFinal))
knitr::kable(tab[1:10,], caption = "Top diseases associated to variant rs113488022")
variantid | disease_name | score | yearInitial | yearFinal |
---|---|---|---|---|
rs113488022 | Colorectal Carcinoma | 0.7 | 1993 | 2024 |
rs113488022 | melanoma | 0.7 | 2002 | 2021 |
rs113488022 | Non-Small Cell Lung Carcinoma | 0.7 | 2002 | 2019 |
rs113488022 | Papillary thyroid carcinoma | 0.7 | 2002 | 2018 |
rs113488022 | Colon Carcinoma | 0.6 | 2002 | 2020 |
rs113488022 | RASopathy | 0.6 | 2011 | 2018 |
rs113488022 | Nephroblastoma | 0.6 | ||
rs113488022 | Multiple Myeloma | 0.6 | ||
rs113488022 | ASTROCYTOMA, LOW-GRADE, SOMATIC | 0.4 | 2002 | 2018 |
rs113488022 | Nongerminomatous Germ Cell Tumor | 0.4 | 2002 | 2018 |
Visualizing the diseases associated to a single variant
The disgenet2r package offers several options to visualize the results of querying DISGENET for a single variant: a Variant-Disease Network
(Figure 23) showing the diseases associated to the variant of interest, a Variant-Gene-Disease Network
showing the genes, diseases, and variant of interest, and a network showing the MeSH Disease Classes of the diseases associated to the variant (Variant-Disease Class Network
, Figure 24). These graphics can be obtained by changing the class
argument in the plot function.
By default, the plot function produces a Variant-Disease Network
on a DataGeNET.DGN
object (Figure 23). In the Variant-Disease Network
the blue nodes are diseases, the yellow nodes are variants, the blue nodes are diseases, and the width of the edges is proportional to the score of the association.
Figure 23: The Variant-Disease Network for the variant rs113488022
Figure 24: The Variant-Disease Class Network for the variant rs113488022
Exploring the evidences associated to a variant
You can extract the evidences associated to a particular variant using the function variant2evidence. Additionally, you can explore the evidences for a specific variant-disease pair by specifying the argument disease
.
results <- variant2evidence( variant = "rs10795668",
disease ="UMLS_C0009402",
database = "ALL",
score =c(0,1))
results
## Object of class 'DataGeNET.DGN'
## . Search: single
## . Type: variant-evidence
## . Database: ALL
## . Score: 0-1
## . Term: rs10795668
## . Results: 9
The results are shown in table 22.
results <- results@qresult
results <- results %>% dplyr::filter(reference_type =="PMID") %>% select(associationType, reference, pmYear, sentence) %>% head(10)
results <- results %>% dplyr::rename(Year=pmYear, Sentence = sentence, pmid=reference) %>% dplyr::arrange(desc(Year))
results %>% dplyr::mutate(
pmid = kableExtra::cell_spec(pmid, link = paste0("https://pubmed.ncbi.nlm.nih.gov/", pmid) )) %>%
knitr::kable(format = 'markdown', row.names = F, caption ="Evidences supporting the association between C0009402 & rs10795668")
associationType | pmid | Year | Sentence |
---|---|---|---|
GeneticVariation | 40479638 | 2025 | Our findings validated rs10795668 (LOC10537640), rs4939827 (SMAD7), rs6066825 (PREX1), and rs6983267 (CCAT2) polymorphisms in CRC risk among Brazilians and suggest that lower Asian and African ancestries might influence CRC susceptibility. |
GeneticVariation | 36653562 | 2023 | |
GeneticVariation | 30194776 | 2019 | In conclusion, some variants associated with CRC risk (rs10505477, rs6983267, rs10795668 and rs11255841) are also involved in the susceptibility to CRA and specific subtypes. |
GeneticVariation | 24801760 | 2015 | The CRC SNPs accounted for 4.3% of the variation in multiple adenoma risk, with three SNPs (rs6983267, rs10795668, rs3802842) explaining 3.0% of the variation. |
GeneticVariation | 24968322 | 2014 | . rs4631962 and rs10795668 contribute to CRC risk in the Taiwanese and East Asian populations, and the newly identified rs1338565 was specifically associated with CRC, supporting the ethnic diversity of CRC-susceptibility SNPs. |
GeneticVariation | 24066093 | 2013 | We genotyped four variants previously associated with CRC: rs10795668, rs16892766, rs3802842 and rs4939827. |
GeneticVariation | 22363440 | 2012 | We observed an association between the low colorectal cancer risk allele (A) for rs10795668 at 10p14 and increased expression of ATP5C1 (q = 0.024) and between the colorectal cancer high risk allele (C) for rs4444235 at 14q22.2 and increased expression of DLGAP5 (q = 0.041), both in tumor samples. |
GeneticVariation | 22367214 | 2012 | We used meta-analysis of an efficient empirical-Bayes estimator to detect potential multiplicative interactions between each of the SNPs [rs16892766 at 8q23.3 (EIF3H/UTP23), rs6983267 at 8q24 (MYC), rs10795668 at 10p14 (FLJ3802842), rs3802842 at 11q23 (LOC120376), rs4444235 at 14q22.2 (BMP4), rs4779584 at 15q13 (GREM1), rs9929218 at 16q22.1 (CDH1), rs4939827 at 18q21 (SMAD7), rs10411210 at 19q13.1 (RHPN2), and rs961253 at 20p12.3 (BMP2)] and select major CRC risk factors (sex, body mass index, height, smoking status, aspirin/nonsteroidal anti-inflammatory drug use, alcohol use, and dietary intake of calcium, folate, red meat, processed meat, vegetables, fruit, and fiber). |
GeneticVariation | 18372905 | 2008 | In addition to the previously reported 8q24, 15q13 and 18q21 CRC risk loci, we identified two previously unreported associations: rs10795668, located at 10p14 (P = 2.5 x 10(-13) overall; P = 6.9 x 10(-12) replication), and rs16892766, at 8q23.3 (P = 3.3 x 10(-18) overall; P = 9.6 x 10(-17) replication), which tags a plausible causative gene, EIF3H. |
The results can be visualized using the plot function with the argument Points
. This will show the number of publications per year associated to this variant. It is important to set the parameter limit
to 10,000 in order to include all the results in the plot.
## Object of class 'DataGeNET.DGN'
## . Search: single
## . Type: variant-evidence
## . Database: ALL
## . Score: 0-1
## . Term: rs1800629
## . Results: 2135
Figure 25: The Evidence plot for the variant rs1800629
Exploring the information associated to a variant
The variant2attribute function receives a variant, or a list of variants as input, identified by the dbSNP identifier. It produces an object DataGeNET.DGN
with attributes of the variant(s) such as the allelic frequency according to GNOMAD data, the most severe consequence type from the Variant Effect Predictor and the DPI, and DSI.
## Object of class 'DataGeNET.DGN'
## . Search: single
## . Type: variant
## . Database: ALL
## . Score:
## . Term: rs113488022
The results are shown in table 23.
tab <- unique(results@qresult )
tab <- tab %>% dplyr::select(-threeletterID,-source, -var_gene_symbol)
knitr::kable(tab, caption = "Attributes for variant rs113488022")
variantid | ref | alt | polyphen_score | sift_score | chromosome | coord | mostSevereConsequences | geneid | geneEnsemblID | gene_symbol | dbsnpclass | variantDSI | variantDPI | exome |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
rs113488022 | A | C | 0.958 | 0 | 7 | 140753336 | missense_variant | 673 | ENSG00000157764 | BRAF | snv | 0.356 | 0.061 | |
rs113488022 | A | G | 0.958 | 0 | 7 | 140753336 | missense_variant | 673 | ENSG00000157764 | BRAF | snv | 0.356 | 0.061 | |
rs113488022 | A | T | 0.958 | 0 | 7 | 140753336 | missense_variant | 673 | ENSG00000157764 | BRAF | snv | 0.356 | 0.061 | 1.4e-06 |
Searching multiple variants
The variant2disease function retrieves the information in DISGENET for a list of variants identified by the dbSNP identifier. The function also requires the user to specify the source database using the argument database
. By default, variant2disease function uses as source database CURATED.
results <- variant2disease(
variant = c("rs121913013", "rs1060500621",
"rs199472709", "rs72552293",
"rs74315445", "rs199472795"),
database = "ALL")
results
## Object of class 'DataGeNET.DGN'
## . Search: list
## . Type: variant-disease
## . Database: ALL
## . Score: 0-1
## . Term: rs121913013 ... rs199472795
## . Results: 18
In table 24, the top 10 diseases associated to the list of variants.
tab <- unique(results@qresult[ ,c("variantid", "disease_name","score", "yearInitial", "yearFinal")] )%>% dplyr::arrange(desc(score), desc(yearFinal))
knitr::kable(tab[1:10,], caption = "Top diseases associated to the list of variants")
variantid | disease_name | score | yearInitial | yearFinal |
---|---|---|---|---|
rs74315445 | LONG QT SYNDROME 5 | 0.6 | 1993 | 2024 |
rs74315445 | Jervell And Lange-Nielsen Syndrome 2 | 0.6 | 1993 | 2024 |
rs199472795 | Romano-Ward Syndrome | 0.6 | 1993 | 2022 |
rs199472709 | Romano-Ward Syndrome | 0.6 | 1993 | 2022 |
rs72552293 | Brugada Syndrome 2 | 0.6 | 1993 | 2007 |
rs74315445 | Familial long QT syndrome (disorder) | 0.4 | 1997 | 2024 |
rs74315445 | Jervell-Lange Nielsen Syndrome | 0.4 | 1993 | 2024 |
rs74315445 | Long QT Syndrome | 0.4 | 1997 | 2024 |
rs74315445 | Arrhythmogenic Right Ventricular Dysplasia | 0.4 | 1997 | 2024 |
rs74315445 | Jervell And Lange-Nielsen Syndrome 1 | 0.4 | 1993 | 2024 |
Visualizing the diseases associated to multiple variants
The results of querying DISGENET with a list of variants can be visualized as a Variant-Disease Network
(Figure 26), as a Variant-Gene-Disease Network
(Figure 27), as Variant-Disease Heatmap
(Figure 28), as Variant-Disease Class Network
(Figure 29) and as a Variant-Disease Class Heatmap
(Figure 30).
Figure 26: The Variant-Disease Network for a list of variants
To obtain the Variant-Gene-Disease Network
(Figure 27), change the showGenes
argument to “TRUE”.
Figure 27: The Variant-Gene-Disease Network for a list of variants
The results of querying DISGENET variant information with a list of diseases can be visualized as a Variant-Disease Network
by changing the type
argument to Heatmap
(Figure 28).
Figure 28: The Variant-Disease Heatmap for a list of variants
The results of querying DISGENET variant information with a list of diseases can also be visualized as a Variant-Disease Class Network
by changing the class
argument to DiseaseClass
(Figure 29).
Figure 29: The Variant-Disease Class Network for a list of variants
The results of querying DISGENET variant information with a list of diseases can also be visualized as a Variant-Disease Class Heatmap
by changing the type
argument to Heatmap
(Figure 30).
Figure 30: The Variant-Disease Class Heatmap for a list of variants
Searching by disease
The disease2variant function allows to retrieve the variants associated to a disease, or a list of diseases. The function uses as input the disease, or list of diseases of interest (each disease should have the format: IDENT_ID where IDENT is one of UMLS, ICD9CM, ICD10, MESH, OMIM, DO, EFO, NCI, HPO, MONDO, or ORDO) and the database (by default, CURATED
). A threshold value for the score can be set, like in the gene2disease function.
## Object of class 'DataGeNET.DGN'
## . Search: single
## . Type: disease-variant
## . Database: CLINVAR
## . Score: 0-1
## . Term: UMLS_C1832916
## . Results: 176
In Table 25, the variants associated to Timothy syndrome according to ClinVar database.
tab <- unique(results@qresult[ ,c("variantid", "disease_name","score", "yearInitial", "yearFinal")] ) %>% dplyr::arrange(desc(score), yearInitial, desc(yearFinal))
knitr::kable(tab[1:10,], caption = " Variants associated to Timothy syndrome according to ClinVar")
variantid | disease_name | score | yearInitial | yearFinal |
---|---|---|---|---|
rs79891110 | Timothy syndrome | 0.7 | 1993 | 2016 |
rs786205745 | Timothy syndrome | 0.7 | 1993 | 2004 |
rs549476254 | Timothy syndrome | 0.6 | 1993 | 2019 |
rs786205753 | Timothy syndrome | 0.6 | 1993 | 2019 |
rs786205748 | Timothy syndrome | 0.5 | 1993 | 2020 |
rs374528680 | Timothy syndrome | 0.5 | 1993 | 2015 |
rs80315385 | Timothy syndrome | 0.5 | 1993 | 2015 |
rs1057517711 | Timothy syndrome | 0.5 | 1993 | 2015 |
rs797044881 | Timothy syndrome | 0.5 | 1993 | 2015 |
rs587782933 | Timothy syndrome | 0.5 | 1993 | 1993 |
The results can be further restricted to keep variants predicted to be deleterious by SIFT and PolyPhen scores, by passing ranges of these scores to the function, using sift
and polyphen
arguments, like in the example below. Remember that genetic variants with SIFT scores smaller than 0.05 are predicted to be deleterious, while values of PolyPhen greater than 0.908 are classified as Probably Damaging.
results <- disease2variant(disease = c("UMLS_C1832916"),
database = "CLINVAR", sift = c(0,0.05), polyphen = c(0.9,1) )
results
## Object of class 'DataGeNET.DGN'
## . Search: single
## . Type: disease-variant
## . Database: CLINVAR
## . Score: 0-1
## . Term: UMLS_C1832916
## . Results: 94
In Table 26, the deleterious variants associated to Timothy syndrome repored in ClinVar database.
tab <- unique(results@qresult[ ,c("variantid", "disease_name","score", "polyphen_score", "sift_score", "yearInitial", "yearFinal")] ) %>% dplyr::arrange(desc(score), yearInitial, desc(yearFinal))
knitr::kable(tab[1:10,], caption = "Deleterious variants associated to Timothy syndrome according to ClinVar")
variantid | disease_name | score | polyphen_score | sift_score | yearInitial | yearFinal |
---|---|---|---|---|---|---|
rs79891110 | Timothy syndrome | 0.7 | 1.000 | 0.00 | 1993 | 2016 |
rs786205745 | Timothy syndrome | 0.7 | 1.000 | 0.01 | 1993 | 2004 |
rs549476254 | Timothy syndrome | 0.6 | 0.999 | 0.00 | 1993 | 2019 |
rs786205753 | Timothy syndrome | 0.6 | 0.999 | 0.00 | 1993 | 2019 |
rs786205748 | Timothy syndrome | 0.5 | 1.000 | 0.00 | 1993 | 2020 |
rs80315385 | Timothy syndrome | 0.5 | 1.000 | 0.00 | 1993 | 2015 |
rs1057517711 | Timothy syndrome | 0.5 | 0.999 | 0.00 | 1993 | 2015 |
rs797044881 | Timothy syndrome | 0.5 | 1.000 | 0.00 | 1993 | 2015 |
rs587782933 | Timothy syndrome | 0.5 | 1.000 | 0.00 | 1993 | 1993 |
rs762091177 | Timothy syndrome | 0.5 | 0.995 | 0.00 | 1993 | 1993 |
Visualizing the variants associated to a single disease
The results of querying DISGENET variant information with a list of diseases can be visualized as a Variant-Disease Network
(Figure 31).
Figure 31: The Variant-Disease Network for a single disease
The Variant-Disease Network
can be displayed as a Variant-Disease-Gene Network
, by setting the showGenes
parameter to TRUE
(Figure 32).
Figure 32: The Variant-Gene-Disease Network for a single disease
Explore the evidences associated to a single disease
To explore the evidences supporting the VDAs for Timothy syndrome, run the disease2evidence function. You can use the argument variant to inspect the evidences for a particular variant and Timothy syndrome.
results <- disease2evidence( disease = "UMLS_C1832916",
type = "VDA",
database = "ALL",
score = c( 0.5,1 ) )
results
## Object of class 'DataGeNET.DGN'
## . Search: single
## . Type: disease-evidence
## . Database: ALL
## . Score: 0.5-1
## . Term: UMLS_C1832916
## . Results: 75
results <- results@qresult
results <- results %>% dplyr::filter(reference_type =="PMID") %>%
select(reference, associationType, pmYear, sentence) %>% dplyr::arrange(desc(pmYear)) %>% head(10)
results <- results %>% dplyr::rename(Year=pmYear, Sentence = sentence, pmid = reference)
results %>% dplyr::mutate(
pmid = kableExtra::cell_spec(pmid, link = paste0("https://pubmed.ncbi.nlm.nih.gov/", pmid) )) %>%
knitr::kable(format = 'markdown', row.names = F, caption ="Evidences supporting associations")
pmid | associationType | Year | Sentence |
---|---|---|---|
39079396 | GeneticVariation | 2024 | In this study, we generated a human induced pluripotent stem cell (iPSC) line from a Timothy syndrome infant carrying heterozygous CACNA1C mutation (transcript variant NM_000719.7c.1216G>A: p.G406R). |
38826393 | GeneticVariation | 2024 | Furthermore, it has remained underexplored whether individuals harboring canonical Gly406Arg variants in mutually exclusive exon 8A (Timothy syndrome 1) or exon 8 (Timothy syndrome 2) have additional symptoms. |
38968219 | GeneticVariation | 2024 | Long QT Syndrome type 8 (LQT8) is a cardiac arrhythmic disorder associated with Timothy Syndrome, stemming from mutations in the CACNA1C gene, particularly the G406R mutation. |
39580446 | GeneticVariation | 2024 | It remains underexplored whether individuals with the canonical Gly406Arg variants in mutually exclusive exon 8A (Timothy syndrome 1) or exon 8 (Timothy syndrome 2) exhibit overlapping symptoms. |
38968219 | GeneticVariation | 2024 | Long QT Syndrome type 8 (LQT8) is a cardiac arrhythmic disorder associated with Timothy Syndrome, stemming from mutations in the CACNA1C gene, particularly the G406R mutation. |
39580446 | GeneticVariation | 2024 | It remains underexplored whether individuals with the canonical Gly406Arg variants in mutually exclusive exon 8A (Timothy syndrome 1) or exon 8 (Timothy syndrome 2) exhibit overlapping symptoms. |
39079396 | GeneticVariation | 2024 | In this study, we generated a human induced pluripotent stem cell (iPSC) line from a Timothy syndrome infant carrying heterozygous CACNA1C mutation (transcript variant NM_000719.7c.1216G>A: p.G406R). |
39420001 | GeneticVariation | 2024 | The canonical G406R mutation that increases Ca2+ influx through the CACNA1C-encoded CaV1.2 Ca2+ channel underlies the multisystem disorder Timothy syndrome (TS), characterized by life-threatening arrhythmias. |
38826393 | GeneticVariation | 2024 | Furthermore, it has remained underexplored whether individuals harboring canonical Gly406Arg variants in mutually exclusive exon 8A (Timothy syndrome 1) or exon 8 (Timothy syndrome 2) have additional symptoms. |
39420001 | GeneticVariation | 2024 | The canonical G406R mutation that increases Ca2+ influx through the CACNA1C-encoded CaV1.2 Ca2+ channel underlies the multisystem disorder Timothy syndrome (TS), characterized by life-threatening arrhythmias. |
If you want to inspect the evidences for Schizophrenia, and all the variants in a particular gene, use the argument gene
.
results <- disease2evidence( disease = "UMLS_C1832916",
gene = "775", vocabulary = "ENTREZ",
type = "VDA", database = "TEXTMINING_HUMAN",
score = c( 0.7,1 ) )
results
## Object of class 'DataGeNET.DGN'
## . Search: single
## . Type: disease-evidence
## . Database: TEXTMINING_HUMAN
## . Score: 0.7-1
## . Term: UMLS_C1832916
## . Results: 22
results <- results@qresult
results <- results %>% dplyr::filter(reference_type =="PMID")%>%
select(reference, associationType, pmYear, sentence) %>% dplyr::arrange(desc(pmYear))%>% head(10)
results <- results %>% dplyr::rename(Year=pmYear, Sentence = sentence, pmid = reference)
results %>% dplyr::mutate(
pmid = kableExtra::cell_spec(pmid, link = paste0("https://pubmed.ncbi.nlm.nih.gov/", pmid) )) %>%
knitr::kable(format = 'markdown', row.names = F, caption ="Selection of evidences supporting associations between C0036341 & CACNA1C")
pmid | associationType | Year | Sentence |
---|---|---|---|
39079396 | GeneticVariation | 2024 | In this study, we generated a human induced pluripotent stem cell (iPSC) line from a Timothy syndrome infant carrying heterozygous CACNA1C mutation (transcript variant NM_000719.7c.1216G>A: p.G406R). |
38826393 | GeneticVariation | 2024 | Furthermore, it has remained underexplored whether individuals harboring canonical Gly406Arg variants in mutually exclusive exon 8A (Timothy syndrome 1) or exon 8 (Timothy syndrome 2) have additional symptoms. |
38968219 | GeneticVariation | 2024 | Long QT Syndrome type 8 (LQT8) is a cardiac arrhythmic disorder associated with Timothy Syndrome, stemming from mutations in the CACNA1C gene, particularly the G406R mutation. |
39580446 | GeneticVariation | 2024 | It remains underexplored whether individuals with the canonical Gly406Arg variants in mutually exclusive exon 8A (Timothy syndrome 1) or exon 8 (Timothy syndrome 2) exhibit overlapping symptoms. |
38968219 | GeneticVariation | 2024 | Long QT Syndrome type 8 (LQT8) is a cardiac arrhythmic disorder associated with Timothy Syndrome, stemming from mutations in the CACNA1C gene, particularly the G406R mutation. |
39580446 | GeneticVariation | 2024 | It remains underexplored whether individuals with the canonical Gly406Arg variants in mutually exclusive exon 8A (Timothy syndrome 1) or exon 8 (Timothy syndrome 2) exhibit overlapping symptoms. |
39079396 | GeneticVariation | 2024 | In this study, we generated a human induced pluripotent stem cell (iPSC) line from a Timothy syndrome infant carrying heterozygous CACNA1C mutation (transcript variant NM_000719.7c.1216G>A: p.G406R). |
39420001 | GeneticVariation | 2024 | The canonical G406R mutation that increases Ca2+ influx through the CACNA1C-encoded CaV1.2 Ca2+ channel underlies the multisystem disorder Timothy syndrome (TS), characterized by life-threatening arrhythmias. |
38826393 | GeneticVariation | 2024 | Furthermore, it has remained underexplored whether individuals harboring canonical Gly406Arg variants in mutually exclusive exon 8A (Timothy syndrome 1) or exon 8 (Timothy syndrome 2) have additional symptoms. |
39420001 | GeneticVariation | 2024 | The canonical G406R mutation that increases Ca2+ influx through the CACNA1C-encoded CaV1.2 Ca2+ channel underlies the multisystem disorder Timothy syndrome (TS), characterized by life-threatening arrhythmias. |
Searching multiple diseases
results <- disease2variant(
disease = paste0("UMLS_",c("C3150943", "C1859062", "C1832916", "C4015695")),
database = "CURATED",
score = c(0.6, 1) )
results
## Object of class 'DataGeNET.DGN'
## . Search: list
## . Type: disease-variant
## . Database: CURATED
## . Score: 0.6-1
## . Term: UMLS_C3150943 ... UMLS_C4015695
## . Results: 152
Table 29 shows the variants associated to a list of Long QT syndromes in the curated data in DISGENET.
tab <- unique(results@qresult[ ,c("variantid", "disease_name","score", "yearInitial", "yearFinal")] ) %>% dplyr::arrange(desc(score), yearInitial, desc(yearFinal))
tab[is.na(tab)] <- ""
knitr::kable(tab[1:10,], caption = "Variants associated to a list of Long QT syndromes")
variantid | disease_name | score | yearInitial | yearFinal |
---|---|---|---|---|
rs137854601 | LONG QT SYNDROME 3 | 0.8 | 1993 | 2022 |
rs137854600 | LONG QT SYNDROME 3 | 0.8 | 1993 | 2022 |
rs121912507 | Long Qt Syndrome 2 | 0.7 | 1993 | 2022 |
rs28937317 | LONG QT SYNDROME 3 | 0.7 | 1993 | 2022 |
rs9333649 | Long Qt Syndrome 2 | 0.7 | 1993 | 2022 |
rs79891110 | Timothy syndrome | 0.7 | 1993 | 2016 |
rs786205745 | Timothy syndrome | 0.7 | 1993 | 2004 |
rs199472916 | Long Qt Syndrome 2 | 0.7 | ||
rs76420733 | Long Qt Syndrome 2 | 0.6 | 1990 | 2022 |
rs199473099 | LONG QT SYNDROME 3 | 0.6 | 1991 | 2015 |
Visualizing the variants associated to multiple diseases
The results of querying DISGENET variant information with a list of diseases can be visualized as a Variant-Disease Network
, or as a Variant-Disease Heatmap
(Figure 33), by changing the class
argument from “Network” to “Heatmap”.
Figure 33: The Variant-Disease Network for a list of diseases
The results can be visualized as a Heatmap (Figure 34).
Figure 34: The Variant-Disease Heatmap for a list of diseases
Searching by gene
## Object of class 'DataGeNET.DGN'
## . Search: single
## . Type: variant-disease
## . Database: CURATED
## . Score: 0-1
## . Term: APP
## . Results: 17
Table 30 shows the top variants associated to the APP gene in the curated data in DISGENET.
tab <- unique(results@qresult[ ,c("variantid", "gene_symbols", "disease_name","score", "yearInitial", "yearFinal")] ) %>% dplyr::arrange(desc(score), yearInitial, desc(yearFinal))
knitr::kable(tab[1:10,], caption = "Top variants associated to APP")
variantid | gene_symbols | disease_name | score | yearInitial | yearFinal |
---|---|---|---|---|---|
rs63750264 | APP | Alzheimer’s Disease | 0.7 | 1991 | 2020 |
rs63750579 | APP | Alzheimer’s Disease | 0.6 | 1990 | 2020 |
rs63750579 | APP | CEREBRAL AMYLOID ANGIOPATHY, APP-RELATED | 0.6 | 1990 | 2019 |
rs63750264 | APP | ALZHEIMER DISEASE, FAMILIAL, 1 | 0.6 | 1991 | 2020 |
rs63749964 | APP | ALZHEIMER DISEASE, FAMILIAL, 1 | 0.6 | 1991 | 2020 |
rs63750066 | APP | Alzheimer’s Disease | 0.6 | 1992 | 2020 |
rs63751039 | APP | ALZHEIMER DISEASE, FAMILIAL, 1 | 0.6 | 1992 | 2020 |
rs63750671 | APP | ALZHEIMER DISEASE, FAMILIAL, 1 | 0.6 | 1992 | 2020 |
rs63750734 | APP | Alzheimer’s Disease | 0.6 | 1993 | 2020 |
rs63750734 | APP | ALZHEIMER DISEASE, FAMILIAL, 1 | 0.6 | 1993 | 2020 |
Visualizing the variant-disease associations retrieved for a gene
The results of querying DISGENET variant information with a gene can be visualized as a Variant-Disease Network
, or as a Variant-Disease Heatmap
(Figure 35), if the input is a list of genes, by changing the class
argument from Network to Heatmap. The genes can be shown by setting the showGenes
argument to “TRUE”.
Figure 35: The Variant-Disease Network for a gene
Searching by variant and chemical
results <- variant2disease( variant = "rs121434568",
database = "TEXTMINING_HUMAN",
chemical = "CHEMBL_CHEMBL1173655")
results
## Object of class 'DataGeNET.DGN'
## . Search: single
## . Type: variant-disease
## . Database: TEXTMINING_HUMAN
## . Score: 0-1
## . Term: rs121434568
## . Results: 11
Table 31 shows the VDAs associated to rs121434568 and afatinib.
tab <- results@qresult
tab <-tab%>% dplyr::select(variantid, disease_name, chemical_name, score) %>% dplyr::arrange(desc(score))
knitr::kable(tab[1:10,], caption = "VDAs associated to rs121434568 and afatinib")
variantid | disease_name | chemical_name | score |
---|---|---|---|
rs121434568 | Adenocarcinoma of lung (disorder) | Afatinib | 0.7 |
rs121434568 | Carcinoma of lung | Afatinib | 0.7 |
rs121434568 | Non-Small Cell Lung Carcinoma | Afatinib | 0.3 |
rs121434568 | Malignant neoplasm of lung | Afatinib | 0.3 |
rs121434568 | Metastatic malignant neoplasm | Afatinib | 0.2 |
rs121434568 | Metastatic Lung Adenocarcinoma | Afatinib | 0.2 |
rs121434568 | Metastatic malignant neoplasm to brain | Afatinib | 0.2 |
rs121434568 | Metastatic Malignant Neoplasm to the Leptomeninges | Afatinib | 0.2 |
rs121434568 | Dyspnea | Afatinib | 0.2 |
rs121434568 | Coughing | Afatinib | 0.1 |
To visualize the results use the plot function.
Figure 36: VDAs associated to rs121434568 and afatinib
Retrieving the chemicals associated to a variant
The variant2chemical function allows to retrieve the chemicals associated to a variant
results <- variant2chemical( variant = "rs1801133",
database = "TEXTMINING_HUMAN" , score = c(0.3,1))
results
## Object of class 'DataGeNET.DGN'
## . Search: single
## . Type: variant-chemical
## . Database: TEXTMINING_HUMAN
## . Score: 0.3-1
## . Term: rs1801133
## . Results: 66
tab <- results@qresult
tab <-tab%>% dplyr::select( disease_name, chemical_name, chemical_effect, sentence, reference, pmYear)
tab <- tab[1:10, ] %>% dplyr::rename( Disease = disease_name, Chemical = chemical_name,
`Chemical Effect`=chemical_effect, Year=pmYear, Sentence = sentence, pmid = reference) %>% dplyr::arrange(desc(Year))
tab %>% dplyr::mutate(
pmid = kableExtra::cell_spec(pmid, link = paste0("https://pubmed.ncbi.nlm.nih.gov/", pmid) )) %>%
knitr::kable(format = 'markdown', row.names = F, caption = "Chemicals associated to rs1801133" )
Disease | Chemical | Chemical Effect | Sentence | pmid | Year |
---|---|---|---|---|---|
Schizophrenia | Risperidone | therapeutic | C677T Polymorphism in the MTHFR Gene Is Associated With Risperidone-Induced Weight Gain in Schizophrenia. | 32714219 | 2020 |
Diastolic blood pressure | Homocysteine | other | Cholesterol, LDLcalc (LDLcalculaed), and homocysteine blood levels significantly contributed to the effect of MTHFR C677T polymorphism on the DBP trait. | 25165067 | 2015 |
Colorectal Carcinoma | Fluorouracil | therapeutic | This study suggested that MTHFR C677T and A1298C are associated with prognosis in CRC patients undergoing 5-FU-based chemotherapy. | 26111049 | 2015 |
Schizophrenia | Homocysteine | other | The aim was to detect a serum level of Hcy, examine the associations between the level of Hcy, methylenetetrahydrofolate reductase (MTHFR) gene C677T polymorphism and clinical properties for patients with schizophrenia, mood disorders and in a control group. | 23586533 | 2014 |
Schizophrenia | Homocysteine | other | The aim was to examine the serum levels of homocysteine (Hcy) and their associations with the methylenetetrahydrofolate reductase (MTHFR) gene C677T polymorphism in patients with schizophrenia and mood disorders as well as controls. | 23091720 | 2012 |
Systolic Pressure | Benazepril | other | There were no significant interactions or effect modification between MTHFR C677T and MS A2756G gene polymorphisms in models of baseline SBP, baseline DBP or DBP response to Benazepril treatment. | 20445573 | 2011 |
Schizophrenia | Homocysteine | other | Folate, homocysteine, interleukin-6, and tumor necrosis factor alfa levels, but not the methylenetetrahydrofolate reductase C677T polymorphism, are risk factors for schizophrenia. | 19939410 | 2010 |
Schizophrenia | Homocysteine | other|other|other | In addition to the prevalence of the 677C–>T mutation in the 5,10-methylenetetrahydrofolate reductase (MTHFR) gene, we compared plasma and red blood cell (RBC) folate, vitamin B6, vitamin B12, and homocysteine (Hcy) concentrations of 35 schizophrenic patients with those of 104 unrelated controls. | 14572619 | 2003 |
Schizophrenia | VITAMIN B12 | other|other|other | In addition to the prevalence of the 677C–>T mutation in the 5,10-methylenetetrahydrofolate reductase (MTHFR) gene, we compared plasma and red blood cell (RBC) folate, vitamin B6, vitamin B12, and homocysteine (Hcy) concentrations of 35 schizophrenic patients with those of 104 unrelated controls. | 14572619 | 2003 |
Schizophrenia | Pyridoxine | other|other|other | In addition to the prevalence of the 677C–>T mutation in the 5,10-methylenetetrahydrofolate reductase (MTHFR) gene, we compared plasma and red blood cell (RBC) folate, vitamin B6, vitamin B12, and homocysteine (Hcy) concentrations of 35 schizophrenic patients with those of 104 unrelated controls. | 14572619 | 2003 |
To visualize the results use the plot function.
Figure 37: Chemicals associated to rs1801133
Retrieving associations involving Chemicals from DISGENET
Retrieving genes, variants, and diseases associated to chemicals
The chemical2gene function allows to retrieve the GDAS for a specific chemical, or list of chemicals.
## Notice that your query has a maximum of 14 pages.
## By indicating n_pags = 5, your query of 14 pages has been reduced to 5 pages.
## Object of class 'DataGeNET.DGN'
## . Search: single
## . Type: chemical-gene
## . Database: ALL
## . Score: 0-1
## . Term: CHEMBL1009
## . Results: 90
tab <- results@qresult
tab <-tab%>% dplyr::select(gene_symbol,gene_type , chemical_name, pmids_chemical) %>% dplyr::arrange(desc(pmids_chemical))
knitr::kable(tab[1:10,], caption = "Genes associated to levodopa")
gene_symbol | gene_type | chemical_name | pmids_chemical |
---|---|---|---|
COMT | protein-coding | Levodopa | 39 |
GH1 | protein-coding | Levodopa | 25 |
DRD2 | protein-coding | Levodopa | 24 |
PRKN | protein-coding | Levodopa | 19 |
GCH1 | protein-coding | Levodopa | 18 |
SLC6A3 | protein-coding | Levodopa | 17 |
SNCA | protein-coding | Levodopa | 17 |
DDC | protein-coding | Levodopa | 15 |
DRD1 | protein-coding | Levodopa | 15 |
TH | protein-coding | Levodopa | 14 |
The results can be visualized as a Chemical-Gene Network
(Figure 38).
Figure 38: The Chemical-Gene Network for a single chemical
The chemical2disease function allows to retrieve the diseases for a specific chemical, or list of chemicals, and the information cab be extracted from GDAs or VDAs. To specify from where, use the type parameter.
results <- chemical2disease( chemical = "CHEMBL_CHEMBL1009" , type = "GDA", database = "ALL" )
results
## Object of class 'DataGeNET.DGN'
## . Search: single
## . Type: chemical-disease
## . Database: ALL
## . Score: 0-1
## . Term: CHEMBL1009
## . Results: 208
tab <- results@qresult
tab <-tab%>% dplyr::select(diseaseid, disease_name, chemical_name, pmids_chemical) %>% dplyr::arrange(desc(pmids_chemical))
knitr::kable(tab[1:10,], caption = "Diseases associated to levodopa, type GDA", align= "lllc")
diseaseid | disease_name | chemical_name | pmids_chemical |
---|---|---|---|
C0030567 | Parkinson Disease | Levodopa | 283 |
C0013384 | Dyskinetic syndrome | Levodopa | 188 |
C0242422 | Parkinsonian Disorders | Levodopa | 77 |
C0393593 | Dystonia Disorders | Levodopa | 28 |
C0013421 | Dystonia | Levodopa | 25 |
C1851920 | Dopa-Responsive Dystonia | Levodopa | 12 |
C0392702 | Abnormal involuntary movements | Levodopa | 10 |
C0002395 | Alzheimer’s Disease | Levodopa | 8 |
C0020615 | Hypoglycemia | Levodopa | 8 |
C0033975 | Psychotic Disorders | Levodopa | 8 |
Figure 39: The Chemical-Disease Network for a chemical
A DiseaseClass
plot is also available.
Figure 40: The Chemical-Disease Class Network for a chemical
For VDAs
results <- chemical2disease( chemical = "CHEMBL_CHEMBL1282" , type = "VDA", database = "ALL" )
results
## Object of class 'DataGeNET.DGN'
## . Search: single
## . Type: chemical-disease
## . Database: ALL
## . Score: 0-1
## . Term: CHEMBL1282
## . Results: 3
tab <- results@qresult
tab <-tab%>% dplyr::select(diseaseid, disease_name, chemical_name, pmids_chemical) %>% dplyr::arrange(desc(pmids_chemical))
knitr::kable(tab, caption = "Diseases associated to imiquimod, type VDA", align= "lllc")
diseaseid | disease_name | chemical_name | pmids_chemical |
---|---|---|---|
C4721806 | Skin Basal Cell Carcinoma | Imiquimod | 2 |
C0025202 | melanoma | Imiquimod | 1 |
C0151779 | Cutaneous Melanoma | Imiquimod | 1 |
Figure 41: The Chemical-Disease Network for a chemical
The chemical2variant function allows to retrieve the variants for a specific chemical, or list of chemicals.
## Object of class 'DataGeNET.DGN'
## . Search: single
## . Type: chemical-variant
## . Database: ALL
## . Score: 0-1
## . Term:
## . Results: 37
tab <- results@qresult
tab <-tab%>% dplyr::select(variantid, gene_symbols, most_severe_consequence, chemical_name, pmids_chemical) %>% dplyr::arrange(desc(pmids_chemical))
knitr::kable(tab[1:10,], caption = "VDAs for carbamazepine", align= "llllc")
variantid | gene_symbols | most_severe_consequence | chemical_name | pmids_chemical |
---|---|---|---|---|
rs3812718 | SCN1A | splice_donor_5th_base_variant | Carbamazepine | 8 |
rs1045642 | ABCB1 | missense_variant | Carbamazepine | 6 |
rs776746 | ZSCAN25, CYP3A5 | splice_acceptor_variant | Carbamazepine | 5 |
rs1801133 | MTHFR | missense_variant | Carbamazepine | 4 |
rs2298771 | SCN1A , LOC102724058 | missense_variant | Carbamazepine | 4 |
rs2032582 | ABCB1 | missense_variant | Carbamazepine | 3 |
rs1389503611 | EPHX1 | missense_variant | Carbamazepine | 2 |
rs15524 | ZSCAN25, CYP3A5 | 3_prime_UTR_variant | Carbamazepine | 2 |
rs1801131 | MTHFR | missense_variant | Carbamazepine | 2 |
rs2234922 | EPHX1 | missense_variant | Carbamazepine | 2 |
The chemical2variant function can also receive as a parameter sift
and polyphen
scores to restrict the results to variants predicted as probably deleterious.
results <- chemical2variant( chemical = "CHEMBL_CHEMBL108", database = "ALL", sift = c(0,0.05), polyphen = c(0.9,1) )
results
## Object of class 'DataGeNET.DGN'
## . Search: single
## . Type: chemical-variant
## . Database: ALL
## . Score: 0-1
## . Term:
## . Results: 11
tab <- results@qresult
tab <-tab%>% dplyr::select(variantid, gene_symbols, sift_score, polyphen_score, chemical_name, pmids_chemical) %>% dplyr::arrange(desc(pmids_chemical))
knitr::kable(tab[1:10,], caption = "VDAs for carbamazepine", align= "llllc")
variantid | gene_symbols | sift_score | polyphen_score | chemical_name | pmids_chemical |
---|---|---|---|---|---|
rs1045642 | ABCB1 | 0.02 | 0.998 | Carbamazepine | 6 |
rs1389503611 | EPHX1 | 0.01 | 0.995 | Carbamazepine | 2 |
rs762468188 | TMEM63A, EPHX1 | 0.00 | 1.000 | Carbamazepine | 2 |
rs1051740 | EPHX1 | 0.00 | 0.987 | Carbamazepine | 1 |
rs121912438 | SOD1 | 0.00 | 0.967 | Carbamazepine | 1 |
rs1451636751 | UGT2B7 | 0.01 | 0.998 | Carbamazepine | 1 |
rs1553491169 | SCN1A-AS1, SCN9A | 0.00 | 0.956 | Carbamazepine | 1 |
rs1555085798 | KCNA1 | 0.00 | 1.000 | Carbamazepine | 1 |
rs211037 | GABRG2 | 0.02 | 0.977 | Carbamazepine | 1 |
rs796052508 | GABRG2 | 0.03 | 0.997 | Carbamazepine | 1 |
Figure 42: The Chemical-Variant Network for carbamazepine
Retrieving GDAs and VDAs associated to chemicals
Exploring the GDAs of a chemical
The chemical2gda function allows to retrieve the GDAS for a specific chemical, or list of chemicals.
## Object of class 'DataGeNET.DGN'
## . Search: single
## . Type: chemical-gda
## . Database: ALL
## . Score: 0-1
## . Term: CHEMBL809
## . Results: 338
tab <- results@qresult
tab <-tab%>% dplyr::select(gene_symbol, disease_name, chemical_name, score, pmids_chemical)
knitr::kable(tab[1:10,], caption = "GDAs for sertraline")
gene_symbol | disease_name | chemical_name | score | pmids_chemical |
---|---|---|---|---|
HTT | Huntington Disease | Sertraline | 1 | 1 |
CRP | Inflammation | Sertraline | 1 | 2 |
BDNF | Mental Depression | Sertraline | 1 | 8 |
IL1B | Inflammation | Sertraline | 1 | 1 |
SLC6A4 | Depressive disorder | Sertraline | 1 | 3 |
SLC6A4 | Mental Depression | Sertraline | 1 | 2 |
CRP | Coronary heart disease | Sertraline | 1 | 1 |
CCL2 | Inflammation | Sertraline | 1 | 1 |
SLC6A4 | Anxiety Disorders | Sertraline | 1 | 2 |
IL10 | Inflammation | Sertraline | 1 | 1 |
To visualize the results use the plot function.
Figure 43: Network for LEPR and metformin
Exploring the VDAs of a chemical
The chemical2vda function allows to retrieve the VDAS for a specific chemical, or list of chemicals.
## Object of class 'DataGeNET.DGN'
## . Search: single
## . Type: chemical-vda
## . Database: ALL
## . Score: 0-1
## . Term: CHEMBL2010601
## . Results: 19
The chemical2vda function can also receive as a parameter sift
and polyphen
scores to restrict the results to variants predicted as probably deleterious.
results <- chemical2vda( chemical = "CHEMBL_CHEMBL2010601",
database = "ALL",
sift = c(0,0.05) , polyphen = c(0.9,1) )
results
## Object of class 'DataGeNET.DGN'
## . Search: single
## . Type: chemical-vda
## . Database: ALL
## . Score: 0-1
## . Term: CHEMBL2010601
## . Results: 15
tab <- results@qresult
tab <-tab%>% dplyr::select(variantid, disease_name, chemical_name, score,pmids_chemical)
knitr::kable(tab[1:10,], caption = "VDAs associated ivacaftor")
variantid | disease_name | chemical_name | score | pmids_chemical |
---|---|---|---|---|
rs75527207 | Cystic Fibrosis | Ivacaftor | 0.9 | 47 |
rs78655421 | Cystic Fibrosis | Ivacaftor | 0.9 | 4 |
rs368505753 | Cystic Fibrosis | Ivacaftor | 0.8 | 1 |
rs121909005 | Cystic Fibrosis | Ivacaftor | 0.8 | 2 |
rs74503330 | Cystic Fibrosis | Ivacaftor | 0.8 | 2 |
rs121908757 | Cystic Fibrosis | Ivacaftor | 0.6 | 2 |
rs397508442 | Cystic Fibrosis | Ivacaftor | 0.6 | 1 |
rs75527207 | Lung diseases | Ivacaftor | 0.2 | 3 |
rs75527207 | Paranasal Sinus Disorder | Ivacaftor | 0.1 | 1 |
rs75527207 | Rhinosinusitis | Ivacaftor | 0.1 | 1 |
To visualize the results use the plot function.
Figure 44: Network of VDAs
Exploring the GDA evidences of a chemical
The chemical2evidence function allows to retrieve the evidences for the GDAS or VDAs for a specific chemical, or list of chemicals.
results <- chemical2evidence( chemical = "CHEMBL_CHEMBL1069", type = "GDA" , database = "ALL" )
results
## Object of class 'DataGeNET.DGN'
## . Search: single
## . Type: chemical-gda
## . Database: ALL
## . Score: 0-1
## . Term: CHEMBL1069
## . Results: 810
tab <- results@qresult
tab <-tab%>% dplyr::select(gene_symbol, disease_name, chemical_name, sentence,chemical_effect, reference, pmYear)
tab <- tab %>% dplyr::rename(Gene = gene_symbol, Disease = disease_name, Chemical = chemical_name, `Chemical Effect` =chemical_effect, Year=pmYear, Sentence = sentence, pmid = reference)
tab <- tab[ order(-tab$Year),]
tab[1:10, ] %>% dplyr::mutate(
pmid = kableExtra::cell_spec(pmid, link = paste0("https://pubmed.ncbi.nlm.nih.gov/", pmid) )) %>%
knitr::kable(format = 'markdown', row.names = F, caption = "Evidences for Valsartan" )
Gene | Disease | Chemical | Sentence | Chemical Effect | pmid | Year |
---|---|---|---|---|---|---|
NPPA | Heart failure | Valsartan | We measured plasma mature BNP, proBNP and total BNP (mature BNP+proBNP) levels with our immunochemiluminescent assay as well as NT-proBNP, A-type natriuretic peptide (ANP) and BNPcom with conventional assays in 54 patients with heart failure, before (baseline) and after 2, 4, 8 and 12 weeks of sacubitril/valsartan administration. | therapeutic|therapeutic | 39988342 | 2025 |
NPPB | Heart failure | Valsartan | We measured plasma mature BNP, proBNP and total BNP (mature BNP+proBNP) levels with our immunochemiluminescent assay as well as NT-proBNP, A-type natriuretic peptide (ANP) and BNPcom with conventional assays in 54 patients with heart failure, before (baseline) and after 2, 4, 8 and 12 weeks of sacubitril/valsartan administration. | therapeutic|therapeutic | 39988342 | 2025 |
NPPB | Heart failure | Valsartan | The authors examined the efficacy of sacubitril/valsartan according to NT-proBNP levels in patients with reduced, mildly reduced, and preserved left ventricular ejection fraction (LVEF) enrolled in PARADIGM-HF (Prospective Comparison of Angiotensin Receptor-Neprilysin Inhibitor with Angiotensin-Converting-Enzyme Inhibitor to Determine Impact on Global Mortality and Morbidity in Heart Failure Trial) and PARAGON-HF (Prospective Comparison of Angiotensin Receptor-Neprilysin Inhibitor with Angiotensin-Receptor Blockers Global Outcomes in HF with Preserved Ejection Fraction). | therapeutic|therapeutic | 40088233 | 2025 |
NPPA | Congestive heart failure | Valsartan | We measured plasma mature BNP, proBNP and total BNP (mature BNP+proBNP) levels with our immunochemiluminescent assay as well as NT-proBNP, A-type natriuretic peptide (ANP) and BNPcom with conventional assays in 54 patients with heart failure, before (baseline) and after 2, 4, 8 and 12 weeks of sacubitril/valsartan administration. | therapeutic|other | 39988342 | 2025 |
NPPB | Congestive heart failure | Valsartan | The authors examined the efficacy of sacubitril/valsartan according to NT-proBNP levels in patients with reduced, mildly reduced, and preserved left ventricular ejection fraction (LVEF) enrolled in PARADIGM-HF (Prospective Comparison of Angiotensin Receptor-Neprilysin Inhibitor with Angiotensin-Converting-Enzyme Inhibitor to Determine Impact on Global Mortality and Morbidity in Heart Failure Trial) and PARAGON-HF (Prospective Comparison of Angiotensin Receptor-Neprilysin Inhibitor with Angiotensin-Receptor Blockers Global Outcomes in HF with Preserved Ejection Fraction). | therapeutic|other | 40088233 | 2025 |
NPPB | Congestive heart failure | Valsartan | We measured plasma mature BNP, proBNP and total BNP (mature BNP+proBNP) levels with our immunochemiluminescent assay as well as NT-proBNP, A-type natriuretic peptide (ANP) and BNPcom with conventional assays in 54 patients with heart failure, before (baseline) and after 2, 4, 8 and 12 weeks of sacubitril/valsartan administration. | other|therapeutic | 39988342 | 2025 |
NPPB | Reduced ejection fraction | Valsartan | The authors examined the efficacy of sacubitril/valsartan according to NT-proBNP levels in patients with reduced, mildly reduced, and preserved left ventricular ejection fraction (LVEF) enrolled in PARADIGM-HF (Prospective Comparison of Angiotensin Receptor-Neprilysin Inhibitor with Angiotensin-Converting-Enzyme Inhibitor to Determine Impact on Global Mortality and Morbidity in Heart Failure Trial) and PARAGON-HF (Prospective Comparison of Angiotensin Receptor-Neprilysin Inhibitor with Angiotensin-Receptor Blockers Global Outcomes in HF with Preserved Ejection Fraction). | other|other | 40088233 | 2025 |
NPPB | Fibrosis | Valsartan | In contrast to sST2, NT-proBNP is also associated with fibrosis, suggesting that both biomarkers unveil distinct mechanisms during CR in patients treated with sacubitril/valsartan. | other|other | 39889435 | 2025 |
MME | Diabetic Cardiomyopathies | Valsartan | Sacubitril/Valsartan effectively reduced plasma neprilysin activity in both diabetic patients with pre-clinical HFpEF from the PARABLE trial (baseline (Val n = 25; Sac/Val n = 35) and 3 months after treatment (Val n = 21/25; Sac/Val n = 33/35)) and DbCM (high-fat diet and streptozotocin) mice. | other|other|other | 40369551 | 2025 |
PLA2G4A | Kidney Failure, Chronic | Valsartan | The disordered metabolism may reduce the sensitivity of patients to sacubitril/valsartan treatment, and PLA2G4A targeted inhibitors may be a promising therapeutic strategy to improve the sensitivity of patients with ESRD and HF to sacubitril/valsartan treatment. | other|therapeutic | 40058083 | 2025 |
To visualize the results use the plot function.
Figure 45: Chemicals associated to Parkinson
Exploring the VDA evidences of a chemical
results <- chemical2evidence( chemical = "CHEMBL_CHEMBL502", type = "VDA" , database = "TEXTMINING_HUMAN" )
results
## Object of class 'DataGeNET.DGN'
## . Search: single
## . Type: chemical-vda
## . Database: TEXTMINING_HUMAN
## . Score: 0-1
## . Term: CHEMBL502
## . Results: 10
tab <- results@qresult
tab <-tab %>% dplyr::select(variantid, disease_name, chemical_name, sentence,chemical_effect, reference, pmYear)
tab <- tab %>% dplyr::rename( Disease = disease_name, Chemical = chemical_name,
`Chemical Effect` =chemical_effect, Year=pmYear, Sentence = sentence, pmid = reference )
tab <- tab[ order(-tab$Year),]
tab[1:10,] %>% dplyr::mutate(
pmid = kableExtra::cell_spec(pmid, link = paste0("https://pubmed.ncbi.nlm.nih.gov/", pmid) )) %>%
knitr::kable(format = 'markdown', row.names = F, caption = "Evidences for Donepezil" )
variantid | Disease | Chemical | Sentence | Chemical Effect | pmid | Year |
---|---|---|---|---|---|---|
rs2177370 | Alzheimer’s Disease | Donepezil | Association of CHAT Gene Polymorphism rs3793790 and rs2177370 with Donepezil Response and the Risk of Alzheimer’s Disease Continuum. | therapeutic | 38894884 | 2024 |
rs3793790 | Alzheimer’s Disease | Donepezil | Association of CHAT Gene Polymorphism rs3793790 and rs2177370 with Donepezil Response and the Risk of Alzheimer’s Disease Continuum. | therapeutic | 38894884 | 2024 |
rs1080985 | Alzheimer’s Disease | Donepezil | The CYP2D6 SNP rs1080985 might be a useful pharmacogenetic marker of the long-term therapeutic response to donepezil in patients with AD. | therapeutic | 34120801 | 2022 |
rs1065852 | Alzheimer’s Disease | Donepezil | This study explored the influence of apolipoprotein E3 and CYP2D6 (rs1065852) gene polymorphisms on therapeutic responses to donepezil in Han Chinese patients with Alzheimer’s disease. | therapeutic | 26768225 | 2016 |
rs1080985 | Alzheimer’s Disease | Donepezil | Recent data have indicated that the rs1080985 single nucleotide polymorphism (SNP) of the cytochrome P450 (CYP) 2D6 and the common apolipoprotein E (APOE) gene may affect the response to donepezil in patients with Alzheimer’s disease (AD). | therapeutic | 25538729 | 2014 |
rs1080985 | Alzheimer’s Disease | Donepezil | We investigated the association between response to donepezil and the rs1080985 single nucleotide polymorphism, the minor allele (G) of which was previously reported to be associated with a poor response to this drug in patients with Alzheimer’s disease. | therapeutic | 23950644 | 2013 |
rs1080985 | Alzheimer’s Disease | Donepezil | In a sample of 415 AD cases, we found evidence of association between rs1080985 and response to donepezil after 6 months of therapy (OR [95% CI]: 1.74 [1.01-3.00], p = 0.04). | therapeutic | 22465999 | 2012 |
rs1045642 | Alzheimer’s Disease | Donepezil | Fifty-four Italian patients diagnosed with probable mild to moderate Alzheimer’s disease, treated with donepezil (37 patients 5 mg/day, 17 patients 10 mg/day) were genotyped for CYP3A4 (1B, 3, and 4), CYP3A5 (2, 3, and 6) and ABCB1 (3435C>T, 2677G>T/A, and 1236C>T) polymorphisms. | therapeutic | 20931330 | 2011 |
rs1128503 | Alzheimer’s Disease | Donepezil | Fifty-four Italian patients diagnosed with probable mild to moderate Alzheimer’s disease, treated with donepezil (37 patients 5 mg/day, 17 patients 10 mg/day) were genotyped for CYP3A4 (1B, 3, and 4), CYP3A5 (2, 3, and 6) and ABCB1 (3435C>T, 2677G>T/A, and 1236C>T) polymorphisms. | therapeutic | 20931330 | 2011 |
rs1080985 | Alzheimer’s Disease | Donepezil | The single nucleotide polymorphism rs1080985 in the CYP2D6 gene may influence the clinical efficacy of donepezil in patients with mild to moderate Alzheimer disease (AD). | therapeutic | 19738170 | 2009 |
To visualize the results use the plot function.
Figure 46: Evidence network
Exploring the attributes of a chemical
The chemical2attribute function allows to retrieve the information for a specific chemical, or list of chemicals.
## Warning: Unknown or uninitialised column: `chemicalid`.
## Unknown or uninitialised column: `chemicalid`.
## Object of class 'DataGeNET.DGN'
## . Search: single
## . Type: chemical
## . Database: ALL
## . Score:
## . Term:
## . Results: 5
tab <-results@qresult %>% select(chemID, chemVocabulariesCrossreferences, chemPrefName)
knitr::kable(tab, caption = "Attributes for Acetylsalic acid")
chemID | chemVocabulariesCrossreferences | chemPrefName |
---|---|---|
CHEMBL25 | CHEMBL_CHEMBL25 | Acetylsalicylic acid |
CHEMBL25 | CHEBI_15365 | Acetylsalicylic acid |
CHEMBL25 | DRUGBANK_DB00945 | Acetylsalicylic acid |
CHEMBL25 | MESH_D001241 | Acetylsalicylic acid |
CHEMBL25 | PUBCHEM_2244 | Acetylsalicylic acid |
Retrieving Disease-Disease Associations from DISGENET
The disgenet2r package also allows to obtain a list of diseases that share genes or variants with a particular disease, or disease list (disease-disease associations, or DDAs).
Searching DDAs by genes for a single disease
To obtain disease-disease associations, use the disease2disease function. This function uses as input a disease, in the same format that in disease2gene, the database to perform the search (by default, CURATED), and the argument relationship
, to indicate the type of relationship of the disease pair. If the relationship
is set to “has_shared_genes”, arguments such as min_genes
, the minimum number of shared genes between the disease(s) of interest, and jg
, the Jaccard Index for genes, can be defined. By default min_genes = 0
. If the relationship
is set to “has_shared_variants”, similar arguments to filter the results of the search can be defined.
The output is a DataGeNET.DGN
object that contains the top diseases that share genes with the disease that has been searched.
The DataGeNET.DGN
object produced by the disease2disease function also contains the Jaccard Index, also known as the Jaccard similarity coefficient for each disease pair. The Jaccard Coefficient is a similarity metric, computed as the size of the intersection divided by the size of the union of two sample sets, in this case, the genes associates to each disease:
We calculate a p value to estimate the significance of the Jaccard coefficient for a list of disease pairs. The p value is estimated using a Fisher exact test. The pvalue
column displays the minus logarithm of the p value for the Jaccard Index, and is available for disease-disease associations by shared genes and by shared variants.
results <- disease2disease(
disease = "UMLS_C0010674", relationship = "has_shared_genes",
database = "CURATED" , min_genes =2 )
results
## Object of class 'DataGeNET.DGN'
## . Search: single
## . Type: disease-disease-gene
## . Database: CURATED
## . Score:
## . Term: UMLS_C0010674
## . Results: 26
Table 43 shows the diseases that share at least a gene with Cystic Fibrosis (UMLS_C0010674) in DISGENET curated.
tab <- unique(results@qresult[ ,c("disease1_Name", "disease2_Name","jaccard_genes","shared_genes", "pvalue_jaccard_genes")] )
knitr::kable(tab[1:10,], caption = "Diseases that share genes with Cystic Fibrosis")
disease1_Name | disease2_Name | jaccard_genes | shared_genes | pvalue_jaccard_genes |
---|---|---|---|---|
Cystic Fibrosis | Chronic Obstructive Airway Disease | 0.11724 | 17 | 22.4 |
Cystic Fibrosis | BRONCHIECTASIS WITH OR WITHOUT ELEVATED SWEAT CHLORIDE 1 | 0.15254 | 9 | 21.6 |
Cystic Fibrosis | Asthma | 0.08065 | 20 | 21.5 |
Cystic Fibrosis | CFTR-related disorder | 0.13559 | 8 | 19.1 |
Cystic Fibrosis | Congenital bilateral aplasia of vas deferens | 0.13333 | 8 | 18.2 |
Cystic Fibrosis | Lupus Erythematosus, Systemic | 0.08383 | 14 | 16.0 |
Cystic Fibrosis | Hereditary pancreatitis | 0.11940 | 8 | 15.0 |
Cystic Fibrosis | Hypertensive disease | 0.04942 | 17 | 14.3 |
Cystic Fibrosis | Alzheimer’s Disease | 0.05512 | 14 | 12.7 |
Cystic Fibrosis | Diabetes Mellitus, Non-Insulin-Dependent | 0.04011 | 15 | 11.2 |
Visualizing the diseases associated to a single disease
The plot function applied to the DataGeNET.DGN
object generated by the disease2disease function results in a Disease-Disease Network
, where the node in dark blue is the disease of interest and nodes in light blue are the diseases that share genes with it (Figure 47). The node size is proportional to the number of genes associated to each disease.
Figure 47: The Disease-Disease Network by shared genes for Cystic Fibrosis
Searching DDAs via genes for multiple diseases
The function disease2disease can also use as an input a list of diseases in any of the previously described vocabularies. It will retrieve the top diseases that share genes with each of the diseases in the input list.
Table 44 shows the disease list selected for illustrating the disease2disease function
UMLS_CUI | Disease_Name |
---|---|
C0162671 | MELAS Syndrome |
C0023264 | Leigh Disease |
C0917796 | Optic Atrophy, Hereditary, Leber |
diseasesOfInterest <- paste0("UMLS_", c("C0162671", "C0023264", "C0917796"))
results <- disease2disease(
disease = diseasesOfInterest, relationship = "has_shared_genes",
database = "CURATED",
min_genes = 20,
order_by = "JACCARD_GENES" )
results
## Object of class 'DataGeNET.DGN'
## . Search: list
## . Type: disease-disease-gene
## . Database: CURATED
## . Score:
## . Term: UMLS_C0162671 ... UMLS_C0917796
## . Results: 19
Table 45 shows the diseases that share at least 20 genes with the diseases of interest.
tab <- unique(results@qresult[ ,c("disease1_Name", "disease2_Name","jaccard_genes","shared_genes", "pvalue_jaccard_genes")] )
knitr::kable(tab[1:10,], caption = "Diseases that share at list 20 genes with the diseases of interest")
disease1_Name | disease2_Name | jaccard_genes | shared_genes | pvalue_jaccard_genes |
---|---|---|---|---|
Optic Atrophy, Hereditary, Leber | MELAS Syndrome | 0.64815 | 35 | 87 |
MELAS Syndrome | Optic Atrophy, Hereditary, Leber | 0.64815 | 35 | 87 |
MELAS Syndrome | Mitochondrial Diseases | 0.18056 | 39 | 68 |
Optic Atrophy, Hereditary, Leber | Neuropathy, Ataxia, and Retinitis Pigmentosa | 0.55319 | 26 | 68 |
Optic Atrophy, Hereditary, Leber | Striatonigral Degeneration, Infantile, Mitochondrial | 0.55319 | 26 | 68 |
Optic Atrophy, Hereditary, Leber | MITOCHONDRIAL COMPLEX V (ATP SYNTHASE) DEFICIENCY, MITOCHONDRIAL TYPE 1 | 0.55319 | 26 | 68 |
Optic Atrophy, Hereditary, Leber | Flexion contracture of proximal interphalangeal joint of finger | 0.54167 | 26 | 66 |
Optic Atrophy, Hereditary, Leber | Wide spaced nipples (finding) | 0.53061 | 26 | 65 |
Optic Atrophy, Hereditary, Leber | Cleft palate and bilateral cleft lip | 0.50980 | 26 | 63 |
Optic Atrophy, Hereditary, Leber | Hypoplasia of scrotum | 0.50980 | 26 | 63 |
To obtain the network, set the class
argument of the plot function to Network
(Figure 48). In this network, the nodes are the diseases of interest, and the node size is proportional to the number of genes associated with them. On the other hand, the edges size is proportional to the number of genes that are shared between the diseases they are connecting.
Figure 48: The Disease-Disease Network by shared genes for a list of diseases
Searching DDAs via semantic relationships
To obtain disease-disease associations via semantic relationships, use the disease2disease function with the argument relationship equal to one of the following types of semantic relations: has_manifestation, has_associated_morphology, manifestation_of, associated_morphology_of, is_finding_of_disease, due_to, has_definitional_manifestation, has_associated_finding, definitional_manifestation_of, disease_has_finding, cause_of, associated_finding_of.
The output is a DataGeNET.DGN
object that contains the diseases that have the type of relationship defined in the query with the query disease.
results <- disease2disease(
disease = c("UMLS_C0011860", "UMLS_C0028754"),relationship = "has_manifestation", min_sokal = 0.7, order_by = "SOKAL",
database = "CURATED" )
results
## Object of class 'DataGeNET.DGN'
## . Search: list
## . Type: disease-disease-rela
## . Database: CURATED
## . Score:
## . Term: UMLS_C0011860 ... UMLS_C0028754
## . Results: 20
Table 47 shows the diseases associated with Obesity and Diabetes Mellitus non Insulin dependent (NIDDM) by the relation type “has_manifestation”.
tab <- unique(results@qresult[ ,c("disease1_Name", "disease2_Name","ddaRelation","shared_genes", "pvalue_jaccard_genes")] )
knitr::kable(tab , caption = "Diseases associated with Obesity and NIDDM")
disease1_Name | disease2_Name | ddaRelation | shared_genes | pvalue_jaccard_genes |
---|---|---|---|---|
Diabetes Mellitus, Non-Insulin-Dependent | KERATODERMA-ICHTHYOSIS-DEAFNESS SYNDROME, AUTOSOMAL RECESSIVE | has_manifestation | 2 | 2.77 |
Obesity | Obesity, Hyperphagia, and Developmental Delay | has_manifestation | 1 | 1.67 |
Obesity | CHOPS SYNDROME | has_manifestation | 1 | 1.67 |
Obesity | BARDET-BIEDL SYNDROME 18 | has_manifestation | 1 | 1.67 |
Obesity | Pseudohypoparathyroidism Type 1C | has_manifestation | 1 | 1.67 |
Obesity | Bardet-Biedl syndrome 4 | has_manifestation | 1 | 1.67 |
Obesity | SHORT STATURE, BRACHYDACTYLY, IMPAIRED INTELLECTUAL DEVELOPMENT, AND SEIZURES | has_manifestation | 1 | 1.67 |
Obesity | Pseudopseudohypoparathyroidism | has_manifestation | 1 | 1.67 |
Diabetes Mellitus, Non-Insulin-Dependent | MATURITY-ONSET DIABETES OF THE YOUNG, TYPE 13 | has_manifestation | 1 | 1.62 |
Obesity | Bardet-Biedl syndrome 1 | has_manifestation | 2 | 1.45 |
Obesity | MAGEL2-related Prader-Willi-like syndrome | has_manifestation | 1 | 1.37 |
Obesity | CORTISONE REDUCTASE DEFICIENCY 2 | has_manifestation | 1 | 1.37 |
Obesity | Pseudohypoparathyroidism, Type Ia | has_manifestation | 1 | 1.37 |
Obesity | HYPOGONADOTROPIC HYPOGONADISM 27 WITHOUT ANOSMIA | has_manifestation | 1 | 1.37 |
Diabetes Mellitus, Non-Insulin-Dependent | Carboxypeptidase E-related Prader-Willi-like syndrome (disorder) | has_manifestation | 1 | 1.32 |
Obesity | Bardet-Biedl syndrome 2 | has_manifestation | 1 | 1.20 |
Obesity | BARDET-BIEDL SYNDROME 6 | has_manifestation | 1 | 1.20 |
Obesity | WAGR Syndrome | has_manifestation | 1 | 0.91 |
Obesity | KLEEFSTRA SYNDROME 1 | has_manifestation | 1 | 0.85 |
Obesity | DiGeorge Syndrome | has_manifestation | 1 | 0.47 |
Searching diseases similar to a disease of interest
It is possible to obtain the most similar diseases according to the Sokal-Sneath semantic similarity distance using the the get_similar_diseases function. The disease similarity between concepts is computed using the Sokal-Sneath semantic similarity distance (Sánchez and Batet 2011) on the taxonomic relations provided by the Unified Medical Language System Metathesaurus. Only the relationships of type is-a (which describe the taxonomy in any ontology) are taken into account. The get_similar_diseases function uses as input a disease, and as an optional argument min_sokal
, a minimum value for the Sokal distance. By default min_sokal = 0.1
.
## Object of class 'DataGeNET.DGN'
## . Search: single
## . Type: disease-disease-sokal
## . Database: ALL
## . Score:
## . Term: UMLS_C0011860
## . Results: 130
In the Table 48, the top diseases associated to the disease, by Sokal distance
tab <- unique(results@qresult[ ,c("disease1_Name", "disease2_Name","sokal")] )
knitr::kable(tab[1:10,], caption = "Diseases semantically similar to NIDDM")
disease1_Name | disease2_Name | sokal |
---|---|---|
Diabetes Mellitus, Non-Insulin-Dependent | Diabetes Mellitus | 0.830 |
Diabetes Mellitus, Non-Insulin-Dependent | Impaired glucose tolerance (disorder) | 0.821 |
Diabetes Mellitus, Non-Insulin-Dependent | Diabetes Mellitus, Insulin-Dependent | 0.706 |
Diabetes Mellitus, Non-Insulin-Dependent | Hyperglycemia | 0.695 |
Diabetes Mellitus, Non-Insulin-Dependent | Diabetic Retinopathy | 0.687 |
Diabetes Mellitus, Non-Insulin-Dependent | Diabetic Nephropathy | 0.685 |
Diabetes Mellitus, Non-Insulin-Dependent | Gestational Diabetes | 0.684 |
Diabetes Mellitus, Non-Insulin-Dependent | Metabolic Syndrome X | 0.677 |
Diabetes Mellitus, Non-Insulin-Dependent | Insulin Resistance | 0.668 |
Diabetes Mellitus, Non-Insulin-Dependent | Kidney Diseases | 0.609 |
Disease enrichment
The disease_enrichment function performs a disease enrichment (or over-representation) analysis. It determines whether a user-defined set of genes is statistically significantly associated with a disease gene set in DISGENET.
The function takes as input a list of entities, either genes or variants. They are compared against the gene/variant-disease associations in the selected database (by default, ALL
) to determine the diseases associated with the given gene list. The genes can be identified with HGNC, ENSEMBL or Entrez identifiers.
The database
parameter allows users to choose which data source to use: CURATED
for curated gene-disease associations (the default option), CLINICALTRIALS
for associations extracted from ClinicalTrials.gov, or ALL
to include all available databases. The number of genes on the selected data source is used as background or universe of the over-representation test.
The common_entities
parameter sets the minimum number of entities that must be shared with a disease for it to be considered in the analysis; the default is 1
. The max_pvalue
parameter sets a threshold for the p-value from the Fisher test (default is 0.05
).
For genes
Below, an example of how to perform a disease enrichment with a list of genes extracted associated to Autism from the Developmental Brain Disorder Gene Database (Gonzalez-Mantilla et al. 2016).
genes <- c("ADNP", "ANKRD11", "ANKRD17", "ASXL1", "BCKDK", "BRSK2", "CDK13", "CDK8", "CHD2", "CHD7", "CHD8", "CLCN2", "CREBBP", "CSDE1", "CTCF", "CTNNB1", "DDX3X", "FOXP1", "GFER", "H4C3", "HNRNPUL2", "IQSEC2", "ITSN1", "JARID2", "LRP2", "MARK2", "MBOAT7", "MYT1L", "NAA15", "NALCN", "NAV3", "NEXMIF" , "NSD1", "PHF21A", "POGZ", "PRR12", "QRICH1", "SCAF1", "SCN1A", "SCN2A", "SETD5", "SHANK3", "SIN3A", "SOX11", "SOX6", "TANC2", "TBCD", "TCF20" , "TCF4", "TCF7L2", "TRAF7", "TRIP12", "WAC", "WDR26", "ZEB2", "ZMYM2", "ZNF292", "ZSWIM6" )
results <- disease_enrichment(
entities = genes,
common_entities = 5,
vocabulary = "HGNC", database = "CURATED")
## Your query has 1 page.
## Object of class 'DataGeNET.DGN'
## . Search: list
## . Type: disease-enrichment
## . Database: CURATED
## . Score:
## . Term: ADNP ... ZSWIM6
In the Table 49, the top diseases associated to the list of genes.
tab <- unique(results@qresult[ ,c("diseaseName", "geneRatio", "bgRatio","pvalue")] )
knitr::kable(tab[1:10,], caption = "Diseases significantly associated with the list of genes")
diseaseName | geneRatio | bgRatio | pvalue |
---|---|---|---|
Intellectual Disability | 43/58 | 43/14132 | 0 |
Neurodevelopmental Disorders | 37/58 | 37/14132 | 0 |
Neurodevelopmental delay | 24/58 | 24/14132 | 0 |
Non-specific syndromic intellectual disability | 23/58 | 23/14132 | 0 |
Neurodevelopmental abnormality | 14/58 | 14/14132 | 0 |
Autistic Disorder | 21/58 | 21/14132 | 0 |
Autism Spectrum Disorders | 22/58 | 22/14132 | 0 |
Global developmental delay | 19/58 | 19/14132 | 0 |
Developmental Disabilities | 14/58 | 14/14132 | 0 |
Seizures | 15/58 | 15/14132 | 0 |
To visualize the results of the enrichment, use the function plot. Use the argument cutoff
to set a minimum p value threshold, and the argument limit
to reduce the number of records shown (Figure 50). By default, the limit=50
. The node size is proportional to the number of intersection between the user list and the disease.
Figure 50: The Enrichment plot for a list of genes
For variants
Below, an example of how to perform a disease enrichment with a list of variants extracted from the publication Genomic Landscape and Mutational Signatures of Deafness-Associated Genes (Azaiez et al. 2018).
results <- disease_enrichment(
entities = c("rs80338902","rs397516871","rs368341987","rs375050157","rs111033280","rs140884994","rs201076440","rs111033439","rs1296612982","rs41281314","rs397516875","rs143282422","rs142381713","rs35818432","rs111033225","rs200104362","rs201004645","rs34988750","rs373169422","rs397517356","rs188376296","rs199897298","rs200263980","rs200416912","rs184866544","rs397517344","rs41281310","rs727503066","rs727504710","rs143240767","rs145771342","rs376898963","rs397516878","rs181255269","rs188498736","rs111033192","rs117966637","rs914189193","rs181611778","rs111033194","rs111033248","rs111033262","rs111033333","rs111033529","rs146824138","rs483353055","rs528089082","rs747131589","rs111033536","rs45629132","rs371142158","rs727504654","rs192524347","rs527236122","rs111033186","rs111033287","rs139889944","rs200454015","rs397517328","rs111033275","rs150822759","rs200038092","rs201709513","rs370155266","rs45500891","rs111033196","rs111033360","rs397517322","rs111033524","rs727505166","rs79444516","rs35730265","rs45549044","rs111033361","rs370696868","rs727504309","rs533231493"),
vocabulary = "DBSNP", database = "CURATED",)
## Your query has 1 page.
## Object of class 'DataGeNET.DGN'
## . Search: list
## . Type: disease-enrichment
## . Database: CURATED
## . Score:
## . Term: rs80338902 ... rs533231493
In the Table 50, the top diseases associated to the list of variants
tab <- unique(results@qresult[ ,c("diseaseName", "variantRatio", "bgRatio","pvalue")] )
knitr::kable(tab[1:10,], caption = "Diseases significantly associated with the list of variants")
diseaseName | variantRatio | bgRatio | pvalue |
---|---|---|---|
Usher Syndrome, Type I | 26/77 | 26/1630926 | 0 |
USHER SYNDROME, TYPE IIA | 23/77 | 23/1630926 | 0 |
Deafness, Autosomal Recessive 1A | 16/77 | 16/1630926 | 0 |
RETINITIS PIGMENTOSA 39 | 20/77 | 20/1630926 | 0 |
DEAFNESS, AUTOSOMAL RECESSIVE 2 | 13/77 | 13/1630926 | 0 |
Usher Syndrome | 11/77 | 11/1630926 | 0 |
Deafness, Autosomal Dominant 3A | 9/77 | 9/1630926 | 0 |
Deafness, Autosomal Recessive 12 | 11/77 | 11/1630926 | 0 |
USHER SYNDROME, TYPE ID | 11/77 | 11/1630926 | 0 |
Deafness, Autosomal Dominant 11 | 9/77 | 9/1630926 | 0 |
Figure 51 shows the results of the enrichment.
Figure 51: The Enrichment plot for a list of variants
Versions
Get DISGENET data version
## [1] "{ status : OK , payload :{ apiVersion : 1.9.1 , dataVersion : DISGENET v25.3 , lastUpdate : Sept 29 2025 , version : DISGENET v25.3 }, httpStatus :200}"
disgenet2r version
## Version: 1.2.4
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License
disgenet2r is distributed under the GPL-2 license.