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 |
---|---|---|
CLINGEN | GDAs | The Clinical Genome Resource |
ORPHANET | GDAs | The portal for rare diseases and orphan drugs |
PSYGENET | GDAs | Psychiatric disorders Gene association NETwork |
HPO | GDAs | Human Phenotype Ontology |
MGD_HUMAN | GDAs | Mouse Genome Database, human data |
MGD_MOUSE | GDAs | Mouse Genome Database, mouse data |
RGD_HUMAN | GDAs | Rat Genome Database, human data |
RGD_RAT | GDAs | Rat Genome Database, rat data |
UNIPROT | GDAs/VDAs | The Universal Protein Resource |
CLINVAR | GDAs/VDAs | ClinVar Database |
GWASCAT | GDAs/VDAs | The NHGRI-EBI GWAS Catalog |
PHEWASCAT | GDAs/VDAs | The PHEWAS Catalog |
UK BIOBANK | GDAs/VDAs | UK Biobank GWAS data |
FINNGEN | GDAs/VDAs | FinnGen 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 |
CLINICAL TRIALS | GDAs | Data from Clinicaltrials.org |
CURATED | GDAs/VDAs | Human curated sources: ClinGen, UniProt, Orphanet, PsyGeNET, ClinVar, MGD Human, RGD Huma |
INFERRED | GDAs | Inferred data from the HPO and the GWAS and PHEWAS Catalogs, and from UK and FinnGen biobanks |
MODELS | GDAs | Data from animal models: MGD MOUSE, RGD RAT, and TEXT MINING MODELS |
ALL | GDAs/VDAs | All data sources |
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’, ‘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 336 pages.
## By using the default n_pags (100), your query of 336 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 PsyGeNET, ClinGen, ClinVar, MGD Human data, 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 0.42 0.875 8.8607e-05
## 2 LEPR 3953 ENSG00000116678 protein-coding 0.42 0.875 8.8607e-05
## 3 LEPR 3953 ENSG00000116678 protein-coding 0.42 0.875 8.8607e-05
## uniprotids protein_classid protein_class_name
## 1 Q4G138, P48357 DTO_05007599 Signaling
## 2 P48357, Q4G138 DTO_05007599 Signaling
## 3 P48357, Q4G138 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 genetic disease (630), disease of metabolism (0014667)
## 3 genetic disease (630), disease of metabolism (0014667)
## 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)
## numCTsupportingAssociation numPMIDs chemsIncludedInEvidenceBySource
## 1 16 14 NULL
## 2 2 5 NULL
## 3 3 1 NULL
## numChemsIncludedInEvidences numPMIDSWithChemsIncludedInEvidences
## 1 NA NA
## 2 NA NA
## 3 NA NA
## numberChemsFiltered numberPmidsWithChemsFiltered score yearInitial yearFinal
## 1 NA NA 1.0 1986 2023
## 2 NA NA 1.0 2010 2024
## 3 NA NA 0.9 2003 2003
## evidence_level evidence_index diseaseid
## 1 NA 0.8702595 C0028754
## 2 NA 0.9126984 C0011860
## 3 NA 0.8260870 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: 401
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: 401
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: 92
In Table 2 are shown the top 20 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 | 2024 |
LEPR | Diabetes Mellitus, Non-Insulin-Dependent | 1.00 | 1966 | 2024 |
LEPR | Diabetes Mellitus | 0.90 | 1981 | 2023 |
LEPR | Hyperphagia | 0.85 | 1986 | 2023 |
LEPR | Hyperinsulinism | 0.85 | 1986 | 2022 |
LEPR | Hypertensive disease | 0.85 | 1998 | 2022 |
LEPR | Morbid obesity | 0.85 | 1995 | 2024 |
LEPR | Insulin Resistance | 0.80 | 1999 | 2024 |
LEPR | Non-alcoholic Fatty Liver Disease | 0.80 | 2006 | 2024 |
LEPR | Hyperglycemia | 0.80 | 1986 | 2024 |
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 are 2 diseases in the example that do not have annotations to MeSH disease class (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 | Q4G138 | DTO_05007599, DTO , Signaling | protein-coding | 0.42 | 0.875 | 8.86e-05 |
leptin receptor | 3953 | LEPR | ENSG00000116678 | P48357 | DTO_05007599, DTO , Signaling | protein-coding | 0.42 | 0.875 | 8.86e-05 |
Exploring the evidences associated to a gene
You can extract the evidences associated to a particular gene using the function gene2evidence. 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: 18
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 |
---|---|---|---|
37140700 | GeneticVariation | 2023 | In conclusion, we reported ten new patients with leptin and leptin receptor deficiencies and identified six novel LEPR variants expanding the mutational spectrum of this rare disorder. |
33922961 | GeneticVariation | 2021 | Recently, we discovered a spontaneous compound heterozygous mutation within the leptin receptor, resulting in a considerably more obese phenotype than described for the homozygous leptin receptor deficient mice. |
29158088 | AlteredExpression | 2018 | In this study, we demonstrate that leptin receptor activation directly affects iron metabolism by the finding that serum levels of hepcidin, the master regulator of iron in the whole body, were significantly lower in leptin-deficient (ob/ob) and leptin receptor-deficient (db/db) mice. |
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. |
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 | 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. |
12031989 | AlteredExpression | 2002 | These data demonstrate that leptin is not needed for ObR gene expression, and they suggest that leptin plays a role in receptor downregulation because sObR levels are negatively correlated with leptin levels and BMI in control subjects, whereas sObR levels are not depressed in obese leptin-deficient or leptin receptor-deficient individuals. |
9860295 | GeneticVariation | 1998 | Transmission disequilibrium and sequence variants at the leptin receptor gene in extremely obese German children and adolescents. |
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. |
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: 43
In Table 6, the top 20 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 | 1.00 | 1970 | 2024 |
KCNH2 | Cardiac Arrhythmia | 1.00 | 1975 | 2024 |
KCNE1 | Jervell-Lange Nielsen Syndrome | 1.00 | 1993 | 2024 |
KCNE2 | Long QT Syndrome | 1.00 | 1999 | 2024 |
KCNH2 | Long Qt Syndrome 2 | 0.95 | 1986 | 2024 |
KCNE2 | Cardiac Arrhythmia | 0.90 | 1999 | 2024 |
KCNH2 | Sudden Cardiac Death | 0.90 | 2000 | 2024 |
KCNE1 | Long QT Syndrome | 0.90 | 1975 | 2024 |
KCNE1 | LONG QT SYNDROME 5 | 0.90 | 1991 | 2024 |
KCNH2 | Short QT Syndrome 1 | 0.90 | 1999 | 2022 |
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 23 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("IL3", "IL4", "IL5", "IL6", "IL0"),
database = "CLINICALTRIALS", verbose = TRUE )
## Your query has 160 pages.
## Notice that your query has a maximum of 160 pages.
## By using the default n_pags (100), your query of 160 pages has been reduced to 100 pages.
## Warning in gene2evidence(gene = c("IL3", "IL4", "IL5", "IL6", "IL0"), database = "CLINICALTRIALS", :
## One or more of the genes in the list is not in DISGENET ('CLINICALTRIALS'): IL0
To visualize the results set the argument class=Points
(Figure ??).
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: 4
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 | Polycystic Ovary Syndrome | 0.45 |
Metformin | LEPR | Steatohepatitis | 0.35 |
Metformin | LEPR | Schizophrenia | 0.20 |
Metformin | LEPR | 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: 28
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 | Myricetin | In addition to mutations in KIT and PDGFRA, many other genetic alterations have been described in gastrointestinal stromal tumors (GISTs), including amplifications of C-MYC and EGFR, which are often associated with increased protein expression. | 39636317 | 2025 | |
Gastrointestinal Stromal Tumors | ADENOSINE DIPHOSPHATE RIBOSE | Representative candidate drugs for genome-matched therapies in KIT/PDGFRA-mutated and wild-type GISTs were as follows: pembrolizumab for tumor mutation burden-high in one and two patients, respectively; poly-adenosine diphosphate ribose polymerase inhibitors for alterations related to homologous recombination deficiency in 12 and one patient, respectively; NTRK inhibitor for ETV6-NTRK3 fusion in one with KIT/PDGFRA wild-type GIST; and human epidermal growth factor receptor 2-antibody-drug conjugate in one with KIT/PDGFRA-mutated GIST. | 39447098 | 2024 | |
Gastrointestinal Stromal Tumors | Pembrolizumab | Representative candidate drugs for genome-matched therapies in KIT/PDGFRA-mutated and wild-type GISTs were as follows: pembrolizumab for tumor mutation burden-high in one and two patients, respectively; poly-adenosine diphosphate ribose polymerase inhibitors for alterations related to homologous recombination deficiency in 12 and one patient, respectively; NTRK inhibitor for ETV6-NTRK3 fusion in one with KIT/PDGFRA wild-type GIST; and human epidermal growth factor receptor 2-antibody-drug conjugate in one with KIT/PDGFRA-mutated GIST. | 39447098 | 2024 | |
Gastrointestinal Stromal Tumors | Ripretinib | Ripretinib, a broad-spectrum inhibitor of the KIT and PDGFRA receptor tyrosine kinases, is designated as a fourth-line treatment for gastrointestinal stromal tumor (GIST). | 38973363 | 2024 | |
Gastrointestinal Stromal Tumors | Avapritinib | 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 | 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 | Sorafenib | Low Dose Sorafenib in Gastric Gastrointestinal Stromal Tumour with PDGFRA p.1843-D846 Deletion in an 88-Year-Old Male. | 38576303 | 2024 | |
Gastrointestinal Stromal Tumors | Avapritinib | 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 | Imatinib | We report two cases of rare GISTs in the same family: A male patient with the V561D mutation in exon 12 of the PDGFRA gene, who has been taking the targeted drug imatinib since undergoing surgery, and a female patient diagnosed with wild-type GIST, who has been taking imatinib for 3 years since undergoing surgery. | 39350996 | 2024 | |
Gastrointestinal Stromal Tumors | Avapritinib | 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 |
To visualize the results use the plot function.
Figure 11: 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: 137
In Table 9, the top 20 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 |
HTR2A | Schizophrenia | 1 | 2004 | 2008 |
RTN4R | Schizophrenia | 1 | 2004 | 2017 |
COMT | Schizophrenia | 1 | 2005 | 2010 |
MTHFR | Schizophrenia | 1 | 2006 | 2009 |
TNF | Schizophrenia | 1 | 2006 | 2006 |
GRIN2B | Schizophrenia | 1 | 2008 | 2008 |
ZNF804A | Schizophrenia | 1 | 2008 | 2018 |
CHRFAM7A | Schizophrenia | 1 | 2009 | 2009 |
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 12), and a Disease-Protein Class Network
with the genes grouped grouped by the the Drug Target Ontology Protein Class (Figure 13).
Figure 12 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 12: 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 13). 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 13: 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: 137
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: 137
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: 137
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: 137
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: 137
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_C0006142", chemical = "CHEMBL_CHEMBL83",
database = "ALL" , n_pags = 1 )
## Notice that your query has a maximum of 9 pages.
## By indicating n_pags = 1, your query of 9 pages has been reduced to 1 pages.
## Object of class 'DataGeNET.DGN'
## . Search: single
## . Type: chemical-gda
## . Database: ALL
## . Score: 0-1
## . Term: UMLS_C0006142
## . Results: 107
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 cancer")
gene_symbol | disease_name | score | chemical_name | chemicalid |
---|---|---|---|---|
BARD1 | Malignant neoplasm of breast | 1 | Tamoxifen | C-286314 |
BRCA1 | Malignant neoplasm of breast | 1 | Tamoxifen | C-286314 |
BRCA2 | Malignant neoplasm of breast | 1 | Tamoxifen | C-286314 |
CDH1 | Malignant neoplasm of breast | 1 | Tamoxifen | C-286314 |
ESR1 | Malignant neoplasm of breast | 1 | Tamoxifen | C-286314 |
ESR1 | Malignant neoplasm of breast | 1 | Pamidronic acid | C-578377 |
ESR1 | Malignant neoplasm of breast | 1 | BENZOQUINONE | C-88223 |
ESR1 | Malignant neoplasm of breast | 1 | Pterostilbene | C-96644 |
FGFR2 | Malignant neoplasm of breast | 1 | Tamoxifen | C-286314 |
PIK3CA | Malignant neoplasm of breast | 1 | Tamoxifen | C-286314 |
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: 49
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 | Phenobarbital | 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 | BICARBONATE | 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 | Linaclotide | 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 | Iggsorb | Cystic fibrosis (CF) is a genetic disease caused by variants in the gene encoding for the CF transmembrane conductance regulator (CFTR) protein, a chloride and bicarbonate channel. | 39322262 | 2024 | |
CFTR | BICARBONATE | Cystic fibrosis (CF) is a genetic disease caused by variants in the gene encoding for the CF transmembrane conductance regulator (CFTR) protein, a chloride and bicarbonate channel. | 39322262 | 2024 | |
CFTR | Chloride ion | Cystic fibrosis (CF) is a genetic disease caused by variants in the gene encoding for the CF transmembrane conductance regulator (CFTR) protein, a chloride and bicarbonate channel. | 39322262 | 2024 | |
CFTR | Chloride ion | Cystic fibrosis (CF) is a genetic disorder caused by mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) that controls chloride current. | 38347907 | 2024 | |
CFTR | Ivacaftor | While numerous animal models of CF exist, few have a CFTR mutation that is amenable to the triple combination therapy elexacaftor-tezacaftor-ivacaftor (ETI). | 38545546 | 2024 | |
CFTR | Elexacaftor | While numerous animal models of CF exist, few have a CFTR mutation that is amenable to the triple combination therapy elexacaftor-tezacaftor-ivacaftor (ETI). | 38545546 | 2024 | |
CFTR | Tezacaftor | While numerous animal models of CF exist, few have a CFTR mutation that is amenable to the triple combination therapy elexacaftor-tezacaftor-ivacaftor (ETI). | 38545546 | 2024 |
To visualize the results use the plot function.
Figure 14: 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 <- unique(results@qresult )
knitr::kable(tab[1:10,], caption = "Disease attributes for Schizophrenia")
vocabulary | code | disease_name | type | diseaseClasses_UMLS_ST | diseaseClasses_HPO | diseaseClasses_DO | diseaseClasses_MSH |
---|---|---|---|---|---|---|---|
MSH | D012559 | 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) |
OMIM | 181500 | 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) |
HPO | HP:0100753 | 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) |
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) |
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 | C2732979 | Acquired long QT syndrome (disorder) |
UMLS | C0023976 | Long QT Syndrome |
UMLS | C0152154 | Prolonged labor |
UMLS | C1833154 | Long Qt Syndrome 4 |
UMLS | C5687394 | Long QT syndrome type 6 |
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: 388
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 |
---|---|---|---|---|---|
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 |
NOTCH4 | PSYGENET | Biomarker | Our data indicate that NOTCH4 polymorphism can influence clinical symptoms in Slovenian patients with schizophrenia. | 25529856 | 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 |
MAPK3 | PSYGENET | Biomarker | Both single-gene and gene-set enrichment analyses in genome-wide association data from the largest schizophrenia sample to date of 13,689 cases and 18,226 controls show significant association of HIST1H1E and MAPK3, and enrichment of our PSD proteome. | 25048004 | 2015 |
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 |
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: 571
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 | CausalorOrContributing | 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 |
DRD3 | 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 |
DRD2 | GeneticVariation | 38810489 | Six loci including neurexin-1(NRXN1) (rs1045881), dopamine D1 receptor (DRD1) (rs686, rs4532), chitinase-3-like protein 1 (CHI3L1) (rs4950928), velocardiofacial syndrome (ARVCF) (rs165815), dopamine D2 receptor (DRD2) (rs1076560) were identified higher expression with significant difference in individuals converted into schizophrenia after two years. | 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 |
DRD2 | GeneticVariation | 38598465 | Adult patients with schizophrenia will be randomized (2: 1) to receive PGx-assisted treatment (drug and regimen selection depending on the results of single-nucleotide polymorphisms in genes DRD2, HTR1A, HTR2C, ABCB1, CYP2D6, CYP3A5, and CYP1A2) or the standard of care. | 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 | CausalorOrContributing | 39127265 | According to the well-documented dysregulation of endocannabinoid and dopaminergic system genes in schizophrenia, we investigated DNA methylation cannabinoid type 1 receptor (CNR1) and dopamine D2 receptor (DRD2) genes in saliva samples from psychotic subjects using pyrosequencing. | 2024 |
DRD2 | CausalorOrContributing | 39036710 | TAAR1 agonists may be less efficacious than dopamine D 2 receptor antagonists already licensed for schizophrenia. | 2024 |
DRD3 | PostTranslationalModification | 38648100 | Schizophrenia subjects exhibited thousands of neuronal and non-neuronal epigenetic differences at regions that included several susceptibility genetic loci, such as NRG1, DISC1, and DRD3. | 2024 |
DRD2 | CausalorOrContributing | 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 |
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 4 pages.
In table 18, the top 20 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 |
APP | Alzheimer’s Disease | 1 | 1989 | 2023 |
SNCA | Parkinson Disease | 1 | 1989 | 2021 |
PSEN1 | Alzheimer’s Disease | 1 | 1993 | 2022 |
LRRK2 | Parkinson Disease | 1 | 1993 | 2021 |
GRN | Alzheimer’s Disease | 1 | 1993 | 2020 |
APOE | Alzheimer’s Disease | 1 | 1993 | 2020 |
MAPT | Alzheimer’s Disease | 1 | 1993 | 2020 |
PSEN2 | Alzheimer’s Disease | 1 | 1993 | 2020 |
PRKN | Parkinson Disease | 1 | 1998 | 2022 |
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 15).
Figure 15: The Gene-Disease Network associated to a list of diseases
To visualize the results as a Gene-Disease Heatmap
(Figure 16) 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 365 rows has been reduced to 20 rows."
Figure 16: The Gene-Disease Heatmap for genes associated to a list of diseases
A third visualization option is a Protein Class-Disease Heatmap
(Figure 17), 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 17: The Protein Class-Disease Heatmap for genes associated to a list of diseases
A Protein Class-Disease Network
visualization is also possible (Figure 18).
Figure 18: 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: 3478
To visualize the results use the argument Points
(Figure 19).
Figure 19: 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: 107
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 |
PRKN | Parkinson Disease | Levodopa | 1 |
SNCA | Parkinson Disease | Levodopa | 1 |
TH | Parkinson Disease | Levodopa | 1 |
PARK7 | Parkinson Disease | Levodopa | 1 |
PINK1 | Parkinson Disease | Levodopa | 1 |
LRRK2 | Parkinson Disease | Levodopa | 1 |
To visualize the results use the function plot (Figure 19)
Figure 20: 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: 509
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 |
---|---|---|---|---|---|
TH | Rotenone | The neuroprotective effect of our nanoformulation is attributed to the upregulation of tyrosine hydroxylase (TH), the PD therapeutic target, with behavioral improvement in animals against rotenone-induced PD deficits. | 38109795 | 2024 | |
GBA1 | Levodopa | Levodopa-carbidopa intestinal gel for advanced Parkinson’s disease: Impact of LRRK2 and GBA1 mutations. | 39208588 | 2024 | |
GBA1 | Carbidopa | Levodopa-carbidopa intestinal gel for advanced Parkinson’s disease: Impact of LRRK2 and GBA1 mutations. | 39208588 | 2024 | |
MAPT | Caffeine | Based on the genetic association and interaction studies, only MAPT, SLC2A13, LRRK2, ApoE, NOS2A, GRIN2A, CYP1A2, and ADORA2A have been shown by at least one study to have a positive caffeine-gene interaction influencing the risk of PD. | 38914264 | 2024 | |
MAPT | THIOUREA | Evaluation of Alpha-Synuclein and Tau Antiaggregation Activity of Urea and Thiourea-Based Small Molecules for Neurodegenerative Disease Therapeutics. Alzheimer’s disease (AD) and Parkinson’s disease (PD) are multifactorial, chronic diseases involving neurodegeneration. | 39436010 | 2024 | |
VPS35 | ESTROGEN | In conclusion, alternative autophagy might be important for maintaining neuronal homeostasis and may be associated with the neuroprotective effect of estrogen in PD with VPS35 D620N. | 38409392 | 2024 | |
SNCA | Gold | The conformational landscapes of αS indicate that uncharged Aun(SCH2OH?) chaperones the native intrinsically disordered conformations of αS, while negatively and positively charged AuNCs greatly increase the likelihood of forming intramolecular β-sheet domains, which are necessary for αS fibrillation and are a hallmark of PD. | 39437152 | 2024 | |
SNCA | RETINAL | Is there any correlation between alpha-synuclein levels in tears and retinal layer thickness in Parkinson’s disease? To determine the total alpha-synuclein (αSyn) reflex tears and its association with retinal layers thickness in Parkinson’s disease (PD). | 37151018 | 2024 | |
SNCA | Cocoa | Targeting protein aggregation using a cocoa-bean shell extract to reduce α-synuclein toxicity in models of Parkinson’s disease. | 39525389 | 2024 | |
SNCA | (E)-4-oxonon-2-enal | 4-Oxo-2-Nonenal- and Agitation-Induced Aggregates of α-Synuclein and Phosphorylated α-Synuclein with Distinct Biophysical Properties and Biomedical Applications. α-Synuclein (α-syn) can form oligomers, protofibrils, and fibrils, which are associated with the pathogenesis of Parkinson’s disease and other synucleinopathies. | 38727274 | 2024 |
To visualize the results use the function plot
Figure 21: The Evidences plot for a list of diseases
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 | Colon Carcinoma | 0.7 | 2002 | 2020 |
rs113488022 | Non-Small Cell Lung Carcinoma | 0.7 | 2002 | 2019 |
rs113488022 | Papillary thyroid carcinoma | 0.7 | 2002 | 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 |
rs113488022 | Vascular anomaly | 0.4 | 2004 | 2021 |
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 22) 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 23). 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 22). 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 22: The Variant-Disease Network for the variant rs113488022
Figure 23: 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: 24
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 | 34676053 | 2021 | Increasing risk of CRC was noted for rs10795668 in log-additive model (OR = 1.45, 95% CI: 1.05-1.99, p = 0.023); for rs1035209 in log-additive model (OR = 1.79, 95% CI: 1.18-2.72, p = 0.003); for rs11190164 in log-additive model (OR = 1.67, 95% CI: 1.17-2.38, p = 0.004). |
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 | 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 | 21071539 | 2011 | We studied the generalizability of the associations with 11 risk variants for CRC on 8q23 (rs16892766), 8q24 (rs6983267), 9p24 (rs719725), 10p14 (rs10795668), 11q23 (rs3802842), 14q22 (rs4444235), 15q13 (rs4779584), 16q22 (rs9929218), 18q21 (rs4939827), 19q13 (rs10411210), and 20p12 (rs961253) in a multiethnic sample of 2,472 CRC cases, 839 adenoma cases and 4,466 controls comprised of European American, African American, Native Hawaiian, Japanese American, and Latino men and women. |
GeneticVariation | 21402474 | 2011 | Our data suggested that rs10795668, a CRC susceptibility variant identified by GWA studies, might be used as a biomarker to identify CRC patients with high risk of recurrence after chemotherapy. |
GeneticVariation | 20530476 | 2010 | These results suggest that rs6983267, rs4939827, rs10795668, rs3802842, and rs961253 SNPs are associated with the risk of CRC in the Chinese population individually and jointly. |
GeneticVariation | 19843678 | 2009 | We studied the role of the 8q24.21 (rs6983267), 18q21.1 (rs12953717), 15q13.3 (rs4779584), 11q23.1 (rs3802842), 8q23.3 (rs16892766), and 10p14 (rs10795668) risk variants in a series of 995 Dutch CRC cases and 1340 controls. |
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: 1945
Figure 24: 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 | variantDSI | variantDPI | dbsnpclass | exome |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
rs113488022 | A | C | 0.958 | 0 | 7 | 140753336 | missense_variant | 673 | ENSG00000157764 | BRAF | 0.338 | 0.045 | snv | |
rs113488022 | A | G | 0.958 | 0 | 7 | 140753336 | missense_variant | 673 | ENSG00000157764 | BRAF | 0.338 | 0.045 | snv | |
rs113488022 | A | T | 0.958 | 0 | 7 | 140753336 | missense_variant | 673 | ENSG00000157764 | BRAF | 0.338 | 0.045 | snv | 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: 21
In table 24, the top 20 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 | 0.7 | 1993 | 2024 |
rs74315445 | Jervell And Lange-Nielsen Syndrome 2 | 0.6 | 1993 | 2024 |
rs199472709 | Romano-Ward Syndrome | 0.6 | 1993 | 2022 |
rs199472795 | Romano-Ward Syndrome | 0.6 | 1993 | 2022 |
rs72552293 | Brugada Syndrome 2 | 0.6 | 1993 | 2007 |
rs74315445 | Jervell-Lange Nielsen Syndrome | 0.4 | 1993 | 2024 |
rs74315445 | Long QT Syndrome | 0.4 | 1997 | 2024 |
rs74315445 | Sudden death, cause unknown | 0.4 | 1997 | 2024 |
rs74315445 | Familial long QT syndrome (disorder) | 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 25), as a Variant-Gene-Disease Network
(Figure 26), as Variant-Disease Heatmap
(Figure 27), as Variant-Disease Class Network
(Figure 28) and as a Variant-Disease Class Heatmap
(Figure 29).
Figure 25: The Variant-Disease Network for a list of variants
To obtain the Variant-Gene-Disease Network
(Figure 26), change the showGenes
argument to “TRUE”.
Figure 26: 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 27).
Figure 27: 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 28).
Figure 28: 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 29).
Figure 29: 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: 154
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 | 2018 |
rs786205745 | Timothy syndrome | 0.7 | 1993 | 2004 |
rs786205753 | Timothy syndrome | 0.6 | 1993 | 2019 |
rs549476254 | Timothy syndrome | 0.6 | 1993 | 2019 |
rs786205748 | Timothy syndrome | 0.5 | 1993 | 2020 |
rs1057517711 | Timothy syndrome | 0.5 | 1993 | 2015 |
rs797044881 | Timothy syndrome | 0.5 | 1993 | 2015 |
rs374528680 | Timothy syndrome | 0.5 | 1993 | 2015 |
rs80315385 | 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: 86
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 | 2018 |
rs786205745 | Timothy syndrome | 0.7 | 1.000 | 0.01 | 1993 | 2004 |
rs786205753 | Timothy syndrome | 0.6 | 0.999 | 0.00 | 1993 | 2019 |
rs549476254 | Timothy syndrome | 0.6 | 0.999 | 0.00 | 1993 | 2019 |
rs786205748 | Timothy syndrome | 0.5 | 1.000 | 0.00 | 1993 | 2020 |
rs1057517711 | Timothy syndrome | 0.5 | 0.999 | 0.00 | 1993 | 2015 |
rs797044881 | Timothy syndrome | 0.5 | 1.000 | 0.00 | 1993 | 2015 |
rs80315385 | Timothy syndrome | 0.5 | 1.000 | 0.00 | 1993 | 2015 |
rs587782933 | Timothy syndrome | 0.5 | 1.000 | 0.00 | 1993 | 1993 |
rs199473391 | Timothy syndrome | 0.4 | 1.000 | 0.00 | 1993 | 2023 |
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 30).
Figure 30: 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 31).
Figure 31: 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: 72
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 |
---|---|---|---|
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. |
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. |
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. |
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. |
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. |
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. |
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). |
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: 20
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 |
---|---|---|---|
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. |
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. |
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. |
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. |
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. |
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. |
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). |
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: 144
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))
knitr::kable(tab[1:10,], caption = "Variants associated to a list of Long QT syndromes")
variantid | disease_name | score | yearInitial | yearFinal |
---|---|---|---|---|
rs137854600 | LONG QT SYNDROME 3 | 0.8 | 1993 | 2022 |
rs9333649 | Long Qt Syndrome 2 | 0.7 | 1993 | 2022 |
rs199473428 | Long Qt Syndrome 2 | 0.7 | 1993 | 2022 |
rs199472961 | Long Qt Syndrome 2 | 0.7 | 1993 | 2022 |
rs137854601 | LONG QT SYNDROME 3 | 0.7 | 1993 | 2022 |
rs199473524 | Long Qt Syndrome 2 | 0.7 | 1993 | 2022 |
rs79891110 | Timothy syndrome | 0.7 | 1993 | 2018 |
rs786205745 | Timothy syndrome | 0.7 | 1993 | 2004 |
rs199473108 | LONG QT SYNDROME 3 | 0.7 | 1995 | 2018 |
rs199472916 | Long Qt Syndrome 2 | 0.7 |
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 32), by changing the class
argument from “Network” to “Heatmap”.
Figure 32: The Variant-Disease Network for a list of diseases
The results can be visualized as a Heatmap (Figure 33).
Figure 33: 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 |
rs63750066 | APP | Alzheimer’s Disease | 0.6 | 1992 | 2020 |
rs63750734 | APP | Alzheimer’s Disease | 0.6 | 1993 | 2020 |
rs193922916 | APP | Alzheimer’s Disease | 0.6 | 1993 | 2020 |
rs63750579 | APP | CEREBRAL AMYLOID ANGIOPATHY, APP-RELATED | 0.6 | 1990 | 2019 |
rs63749964 | APP | ALZHEIMER DISEASE, FAMILIAL, 1 | 0.6 | 1991 | 2020 |
rs63750264 | APP | ALZHEIMER DISEASE, FAMILIAL, 1 | 0.6 | 1991 | 2020 |
rs63750671 | APP | ALZHEIMER DISEASE, FAMILIAL, 1 | 0.6 | 1992 | 2020 |
rs63751039 | APP | ALZHEIMER DISEASE, FAMILIAL, 1 | 0.6 | 1992 | 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 34), 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 34: 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: 12
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 | Lung Neoplasms | Afatinib | 0.3 |
rs121434568 | Non-Small Cell Lung Carcinoma | Afatinib | 0.3 |
rs121434568 | Malignant neoplasm of lung | Afatinib | 0.3 |
rs121434568 | Metastatic malignant neoplasm to brain | Afatinib | 0.3 |
rs121434568 | Advanced Lung Adenocarcinoma | Afatinib | 0.3 |
rs121434568 | Adenocarcinoma of lung, stage IV | Afatinib | 0.2 |
rs121434568 | Metastatic Lung Adenocarcinoma | Afatinib | 0.2 |
rs121434568 | Metastatic Malignant Neoplasm to the Leptomeninges | Afatinib | 0.2 |
rs121434568 | Metastatic non-small cell lung cancer | Afatinib | 0.2 |
To visualize the results use the plot function.
Figure 35: 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: 119
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 |
---|---|---|---|---|---|
Multiple Sclerosis | Homocysteine | other|therapeutic|other|other | The contents of homocysteine (HCy), cyanocobalamin (vitamin B12), folic acid (vitamin B9), and pyridoxine (vitamin B6) were analyzed and the genotypes of the main gene polymorphisms associated with folate metabolism (C677T and A1298C of the MTHFR gene, A2756G of the MTR gene and A66G of the MTRR gene) were determined in children at the onset of multiple sclerosis (MS) (with disease duration of no more than six months), healthy children under 18 years (control group), healthy adults without neurological pathology, adult patients with MS at the onset of disease, and adult patients with long-term MS. | 38648773 | 2024 |
Multiple Sclerosis | VITAMIN B12 | other|therapeutic|other|other | The contents of homocysteine (HCy), cyanocobalamin (vitamin B12), folic acid (vitamin B9), and pyridoxine (vitamin B6) were analyzed and the genotypes of the main gene polymorphisms associated with folate metabolism (C677T and A1298C of the MTHFR gene, A2756G of the MTR gene and A66G of the MTRR gene) were determined in children at the onset of multiple sclerosis (MS) (with disease duration of no more than six months), healthy children under 18 years (control group), healthy adults without neurological pathology, adult patients with MS at the onset of disease, and adult patients with long-term MS. | 38648773 | 2024 |
Schizophrenia | Homocysteine | other | In this study, we hypothesized that MTHFR C677T polymorphism and homocysteine concentration may play important roles in the development of depressive symptoms in schizophrenia. | 32379616 | 2020 |
Schizophrenia | alpha-Linolenic acid | other | Our results demonstrated no significant differences in MTHFR Ala222Val genotype and allele distributions between the SCZ patients and controls (p > 0.05), but showed a statistical significance in the distribution of Ala/Val genotype between suicide attempters and non-attempters (p < 0.05). | 32193498 | 2020 |
Schizophrenia | Homocysteine | other | Previous studies suggest that elevated total homocysteine levels and the methylenetetrahydrofolate reductase (MTHFR) C677T polymorphism, which correlates with plasma total homocysteine levels, are risk factors for schizophrenia (SCZ). | 27810229 | 2016 |
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 | Previous studies suggest that elevated blood homocysteine levels and the methylenetetrahydrofolate reductase (MTHFR) C677T polymorphism are risk factors for schizophrenia. | 24535549 | 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 |
Schizophrenia | Dopamine | other | A second polymorphism, methylenetetrahydrofolate reductase (MTHFR) 677C –> T (rs1801133), has been associated with overall schizophrenia risk and executive function impairment in patients, and may influence dopamine signaling through mechanisms upstream of COMT effects. | 18988738 | 2008 |
Schizophrenia | Homocysteine | other | The elevated risk of schizophrenia associated with the homozygous genotype of the MTHFR 677C>T polymorphism provides support for causality between a disturbed homocysteine metabolism and risk of schizophrenia. | 16172608 | 2006 |
To visualize the results use the plot function.
Figure 36: 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 16 pages.
## By indicating n_pags = 5, your query of 16 pages has been reduced to 5 pages.
## Warning in chemical2gene(chemical = "CHEMBL_CHEMBL1009", database = "ALL", :
## One or more chemicals in the list is not in DISGENET ( 'ALL' ):
## - CHEMBL_CHEMBL1009
## Object of class 'DataGeNET.DGN'
## . Search: single
## . Type: chemical-gene
## . Database: ALL
## . Score: 0-1
## . Term:
## . Results: 88
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 | 55 |
DDC | protein-coding | Levodopa | 30 |
GH1 | protein-coding | Levodopa | 24 |
SLC6A3 | protein-coding | Levodopa | 20 |
MAOB | protein-coding | Levodopa | 18 |
PRKN | protein-coding | Levodopa | 18 |
DRD2 | protein-coding | Levodopa | 17 |
GCH1 | protein-coding | Levodopa | 15 |
TH | protein-coding | Levodopa | 14 |
SNCA | protein-coding | Levodopa | 13 |
The results can be visualized as a Chemical-Gene Network
(Figure 37).
Figure 37: 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:
## . Results: 230
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 | 324 |
C0013384 | Dyskinetic syndrome | Levodopa | 195 |
C0242422 | Parkinsonian Disorders | Levodopa | 71 |
C0030567 | Parkinson Disease | Dopamine | 49 |
C0393593 | Dystonia Disorders | Levodopa | 27 |
C0013421 | Dystonia | Levodopa | 26 |
C0426980 | motor symptom | Levodopa | 16 |
C0013384 | Dyskinetic syndrome | Dopamine | 15 |
C0013384 | Dyskinetic syndrome | OXIDOPAMINE | 15 |
C0030567 | Parkinson Disease | Carbidopa | 14 |
Figure 38: The Chemical-Disease Network for a chemical
A DiseaseClass
plot is also available.
Figure 39: 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:
## . Results: 5
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 |
C0524910 | Hepatitis C, Chronic | Ribavirin | 1 |
C0524910 | Hepatitis C, Chronic | Polyox WSR-N 60 | 1 |
C0524910 | Hepatitis C, Chronic | Imiquimod | 1 |
C0596263 | Carcinogenesis | Imiquimod | 1 |
Figure 40: 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: 43
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 |
rs776746 | ZSCAN25, CYP3A5 | splice_acceptor_variant | Carbamazepine | 6 |
rs1045642 | ABCB1 | missense_variant | Carbamazepine | 5 |
rs1801133 | MTHFR | missense_variant | Carbamazepine | 4 |
rs2298771 | SCN1A , LOC102724058 | missense_variant | Carbamazepine | 4 |
rs2032582 | ABCB1 | missense_variant | Carbamazepine | 3 |
rs1051740 | EPHX1 | missense_variant | Carbamazepine | 2 |
rs1389503611 | EPHX1 | missense_variant | Carbamazepine | 2 |
rs15524 | ZSCAN25, CYP3A5 | 3_prime_UTR_variant | Carbamazepine | 2 |
rs1801131 | MTHFR | 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: 9
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 | 5 |
rs1051740 | EPHX1 | 0.00 | 0.987 | Carbamazepine | 2 |
rs1389503611 | EPHX1 | 0.01 | 0.995 | Carbamazepine | 2 |
rs762468188 | TMEM63A, EPHX1 | 0.00 | 1.000 | Carbamazepine | 2 |
rs1045642 | ABCB1 | 0.02 | 0.998 | Phenytoin | 1 |
rs1051740 | EPHX1 | 0.00 | 0.987 | CARBAMAZEPINE EPOXIDE | 1 |
rs121912438 | SOD1 | 0.00 | 0.967 | Sod | 1 |
rs121912438 | SOD1 | 0.00 | 0.967 | Carbamazepine | 1 |
rs1553491169 | SCN9A , SCN1A-AS1 | 0.00 | 0.956 | Carbamazepine | 1 |
rs1555085798 | KCNA1 | 0.00 | 1.000 | Carbamazepine | 1 |
Figure 41: 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:
## . Results: 2003
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 |
---|---|---|---|---|
SLC6A4 | Depressive disorder | Serotonin | 1 | 90 |
SLC6A4 | Depressive disorder | demeton-S-methyl | 1 | 5 |
SLC6A4 | Depressive disorder | Cyclohexyl isocyanate | 1 | 2 |
SLC6A4 | Depressive disorder | Norepinephrine | 1 | 6 |
SLC6A4 | Depressive disorder | Mirtazapine | 1 | 1 |
SLC6A4 | Depressive disorder | PAROXETINE HYDROCHLORIDE | 1 | 1 |
SLC6A4 | Depressive disorder | Interferon alfa | 1 | 3 |
SLC6A4 | Depressive disorder | Hydrocortisone | 1 | 4 |
SLC6A4 | Depressive disorder | [3H]CITALOPRAM | 1 | 1 |
SLC6A4 | Depressive disorder | Ethanol | 1 | 2 |
To visualize the results use the plot function.
Figure 42: 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:
## . Results: 56
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:
## . Results: 46
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 | Aztreonam | 0.9 | 1 |
rs75527207 | Cystic Fibrosis | Tobramycin | 0.9 | 1 |
rs75527207 | Cystic Fibrosis | Ivacaftor | 0.9 | 37 |
rs75527207 | Cystic Fibrosis | Colistin | 0.9 | 1 |
rs75527207 | Cystic Fibrosis | Mannitol | 0.9 | 1 |
rs75527207 | Cystic Fibrosis | Chloride ion | 0.9 | 6 |
rs75527207 | Cystic Fibrosis | Genistein | 0.9 | 3 |
rs75527207 | Cystic Fibrosis | Isoprenaline | 0.9 | 1 |
rs75527207 | Cystic Fibrosis | Elexacaftor | 0.9 | 2 |
rs75527207 | Cystic Fibrosis | Resveratrol | 0.9 | 1 |
To visualize the results use the plot function.
Figure 43: 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_CHEMBL3989936", type = "GDA" , database = "ALL" )
results
## Object of class 'DataGeNET.DGN'
## . Search: single
## . Type: chemical-gda
## . Database: ALL
## . Score: 0-1
## . Term:
## . Results: 9
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 Vilaprisan" )
Gene | Disease | Chemical | Sentence | Chemical Effect | pmid | Year |
---|---|---|---|---|---|---|
PGR | Endometriosis | Vilaprisan | Vilaprisan is a highly potent selective progesterone receptor modulator in development for the treatment of symptomatic uterine fibroids and endometriosis. | 34569009 | 2022 | |
PGR | Endometriosis | Vilaprisan | Vilaprisan is a novel selective progesterone receptor modulator for the long-term treatment of uterine fibroids and endometriosis. | 32716091 | 2021 | |
PGR | Uterine Fibroids | Vilaprisan | Vilaprisan is a novel selective progesterone receptor modulator for the long-term treatment of uterine fibroids and endometriosis. | 32716091 | 2021 | |
PGR | Uterine Fibroids | Vilaprisan | Vilaprisan (VPR) is a new orally available selective progesterone receptor modulator (SPRM), with anti-proliferative activity against uterine fibroids (UFs). | 31985366 | 2020 | |
PGR | Renal Insufficiency | Vilaprisan | Pharmacokinetics and Safety of the Novel Selective Progesterone Receptor Modulator Vilaprisan in Participants With Renal Impairment. | other | 32227643 | 2020 |
PGR | Uterine Fibroids | Mifepristone | Selective progesterone receptor modulators (SPRMs), such as Mifepristone, Asoprisnil, Ulipristal acetate (UPA) and Vilaprisan, were tested for their antiproliferative effects on uterine fibroids. | 30845294 | 2018 | |
PGR | Uterine Fibroids | Asoprisnil | Selective progesterone receptor modulators (SPRMs), such as Mifepristone, Asoprisnil, Ulipristal acetate (UPA) and Vilaprisan, were tested for their antiproliferative effects on uterine fibroids. | 30845294 | 2018 | |
PGR | Uterine Fibroids | ULIPRISTAL ACETATE | Selective progesterone receptor modulators (SPRMs), such as Mifepristone, Asoprisnil, Ulipristal acetate (UPA) and Vilaprisan, were tested for their antiproliferative effects on uterine fibroids. | 30845294 | 2018 | |
PGR | Uterine Fibroids | Vilaprisan | Selective progesterone receptor modulators (SPRMs), such as Mifepristone, Asoprisnil, Ulipristal acetate (UPA) and Vilaprisan, were tested for their antiproliferative effects on uterine fibroids. | 30845294 | 2018 | |
NA |
To visualize the results use the plot function.
Figure 44: 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:
## . Results: 14
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 |
---|---|---|---|---|---|---|
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 |
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 |
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 |
rs1135840 | Alzheimer’s Disease | Donepezil | Our results suggests that CYP2D6*10 strongly influences Cpss and there is a trend toward better outcomes of donepezil in patients with AD. | therapeutic | 31564952 | 2019 |
rs1065852 | Alzheimer’s Disease | Donepezil | Our results suggests that CYP2D6*10 strongly influences Cpss and there is a trend toward better outcomes of donepezil in patients with AD. | therapeutic | 31564952 | 2019 |
rs1065852 | Alzheimer’s Disease | Donepezil | The roles of apolipoprotein E3 and CYP2D6 (rs1065852) gene polymorphisms in the predictability of responses to individualized therapy with 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 | Influence of rs1080985 single nucleotide polymorphism of the CYP2D6 gene on response to treatment with donepezil in patients with alzheimer’s disease. | therapeutic | 23950644 | 2013 |
rs1065852 | Alzheimer’s Disease | Donepezil | Effect of CYP2D6*10 and APOE polymorphisms on the efficacy of donepezil in patients with Alzheimer’s disease. | therapeutic | 22986607 | 2013 |
rs1135840 | Alzheimer’s Disease | Donepezil | Effect of CYP2D6*10 and APOE polymorphisms on the efficacy of donepezil in patients with Alzheimer’s disease. | therapeutic | 22986607 | 2013 |
To visualize the results use the plot function.
Figure 45: 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 |
---|---|---|
C-153605 | CHEMBL_CHEMBL25 | Acetylsalicylic acid |
C-153605 | CHEBI_15365 | Acetylsalicylic acid |
C-153605 | DRUGBANK_DB00945 | Acetylsalicylic acid |
C-153605 | MESH_D001241 | Acetylsalicylic acid |
C-153605 | 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: 11
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 | COPD | 0.11724 | 17 | 22.4 |
Cystic Fibrosis | BESC1 | 0.13793 | 8 | 19.2 |
Cystic Fibrosis | SYSTEMIC LUPUS ERYTHEMATOSIS | 0.08589 | 14 | 16.3 |
Cystic Fibrosis | CBAVD | 0.11864 | 7 | 15.8 |
Cystic Fibrosis | Hereditary pancreatitis | 0.12308 | 8 | 15.4 |
Cystic Fibrosis | High blood pressure | 0.04971 | 17 | 14.4 |
Cystic Fibrosis | Alzheimer Disease | 0.05534 | 14 | 12.8 |
Cystic Fibrosis | Adult-Onset Diabetes Mellitus | 0.04043 | 15 | 11.3 |
Cystic Fibrosis | Obstructive azoospermia | 0.05085 | 3 | 6.5 |
Cystic Fibrosis | Cardiomyopathy | 0.02952 | 8 | 5.4 |
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 46). The node size is proportional to the number of genes associated to each disease.
Figure 46: 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: 35
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 |
---|---|---|---|---|
Leber’s optic atrophy | MELAS Syndrome | 0.62963 | 34 | 84 |
MELAS Syndrome | Leber’s optic atrophy | 0.62963 | 34 | 84 |
Encephalomyelopathies, Subacute Necrotizing | Mitochondrial Diseases | 0.23741 | 66 | 83 |
MELAS Syndrome | Mitochondrial Diseases | 0.20652 | 38 | 69 |
Leber’s optic atrophy | MC5DM1 | 0.55319 | 26 | 68 |
Leber’s optic atrophy | NEUROPATHY, ATAXIA, AND RETINITIS PIGMENTOSA | 0.55319 | 26 | 68 |
Leber’s optic atrophy | Camptodactyly of proximal interphalangeal joint | 0.54167 | 26 | 66 |
Leber’s optic atrophy | Wide spaced nipples (finding) | 0.50980 | 26 | 63 |
Leber’s optic atrophy | Scrotal hypoplasia | 0.50980 | 26 | 63 |
Leber’s optic atrophy | postaxial polydactyly hands (physical finding) | 0.50000 | 26 | 63 |
To obtain the network, set the class
argument of the plot function to Network
(Figure 47). 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 47: 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 |
---|---|---|---|---|
Adult-Onset Diabetes Mellitus | KERATODERMA-ICHTHYOSIS-DEAFNESS SYNDROME, AUTOSOMAL RECESSIVE | has_manifestation | 2 | 2.77 |
Obesity | OBESITY, HYPERPHAGIA, AND DEVELOPMENTAL DELAY | has_manifestation | 1 | 1.66 |
Obesity | PHP1C | has_manifestation | 1 | 1.66 |
Obesity | BARDET-BIEDL SYNDROME 18 | has_manifestation | 1 | 1.66 |
Obesity | Bardet-Biedl syndrome 4 | has_manifestation | 1 | 1.66 |
Obesity | SBIDDS | has_manifestation | 1 | 1.66 |
Obesity | Pseudo Pseudohypoparathyroidism | has_manifestation | 1 | 1.66 |
Obesity | CHOPS SYNDROME | has_manifestation | 1 | 1.66 |
Adult-Onset Diabetes Mellitus | MODY, TYPE 13 | has_manifestation | 1 | 1.62 |
Obesity | BBS1 | has_manifestation | 2 | 1.44 |
Obesity | PSEUDOHYPOPARATHYROIDISM, TYPE IA | has_manifestation | 1 | 1.36 |
Obesity | PWLS | has_manifestation | 1 | 1.36 |
Obesity | HYPOGONADOTROPIC HYPOGONADISM 27 WITHOUT ANOSMIA | has_manifestation | 1 | 1.36 |
Obesity | CORTRD2 | has_manifestation | 1 | 1.36 |
Adult-Onset Diabetes Mellitus | IDDHH | has_manifestation | 1 | 1.32 |
Obesity | BARDET-BIEDL SYNDROME 6 | has_manifestation | 1 | 1.19 |
Obesity | Bardet-Biedl syndrome 2 | has_manifestation | 1 | 1.19 |
Obesity | WAGR Syndrome | has_manifestation | 1 | 0.90 |
Obesity | 9q- Syndrome | has_manifestation | 1 | 0.84 |
Obesity | DiGeorge’s syndrome | has_manifestation | 1 | 0.46 |
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: 143
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 |
---|---|---|
Adult-Onset Diabetes Mellitus | Diabetes Mellitus | 0.830 |
Adult-Onset Diabetes Mellitus | Glucose Intolerance | 0.821 |
Adult-Onset Diabetes Mellitus | Diabetes Mellitus, Insulin-Dependent | 0.706 |
Adult-Onset Diabetes Mellitus | Hyperglycemia | 0.695 |
Adult-Onset Diabetes Mellitus | Diabetic Retinopathies | 0.687 |
Adult-Onset Diabetes Mellitus | Diabetic Nephropathies | 0.685 |
Adult-Onset Diabetes Mellitus | Diabetes, Gestational | 0.684 |
Adult-Onset Diabetes Mellitus | Syndrome X, Reaven | 0.677 |
Adult-Onset Diabetes Mellitus | Prediabetic State | 0.677 |
Adult-Onset Diabetes Mellitus | Insulin Resistance | 0.668 |
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 | |
---|---|---|---|---|
1 | Non-specific syndromic intellectual disability | 19/58 | 194/13581 | 0.00e+00 |
2 | Epilepsy | 9/58 | 147/13581 | 2.00e-07 |
3 | Rare genetic syndromic intellectual disability | 5/58 | 51/13581 | 6.44e-05 |
NA | ||||
NA.1 | ||||
NA.2 | ||||
NA.3 | ||||
NA.4 | ||||
NA.5 | ||||
NA.6 |
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 49). By default, the limit=50
. The node size is proportional to the number of intersection between the user list and the disease.
Figure 49: 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 IIA | 28/77 | 1461/696672 | 0 |
Usher Syndrome, Type I | 26/77 | 1282/696672 | 0 |
RETINITIS PIGMENTOSA 39 | 21/77 | 1128/696672 | 0 |
Deafness, Autosomal Recessive 1A | 15/77 | 245/696672 | 0 |
USHER SYNDROME, TYPE ID | 12/77 | 513/696672 | 0 |
DEAFNESS, AUTOSOMAL RECESSIVE 2 | 12/77 | 536/696672 | 0 |
Usher Syndrome | 10/77 | 336/696672 | 0 |
Deafness, Autosomal Dominant 3A | 8/77 | 106/696672 | 0 |
Deafness, Autosomal Recessive 12 | 10/77 | 516/696672 | 0 |
Senter syndrome | 6/77 | 61/696672 | 0 |
Figure 50 shows the results of the enrichment.
Figure 50: The Enrichment plot for a list of variants
Versions
Get DISGENET data version
## [1] "{ status : OK , payload :{ apiVersion : 1.8.0 , dataVersion : DISGENET v25.1 , lastUpdate : March 31 2025 , version : DISGENET v25.1 }, httpStatus :200}"
disgenet2r version
## Version: 1.2.3
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License
disgenet2r is distributed under the GPL-2 license.