DGIdb 3.0: a redesign and expansion of the drug–gene interaction database

Cotto, Kelsy C.; Wagner, Alex H.; Feng, Yangyang; Kiwala, Susanna; Coffman, Adam; Spies, Gregory C.; Wollam, Alex; Spies, Nicholas C.; Griffith, Obi L.

doi:10.1093/nar/gkx1143

Cited by 731 publications

(625 citation statements)

References 26 publications

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“…We selected the GWS genes from the gene-based analysis (conducted using MAGMA) of PCL-total, filtered the list by those genes that were also GWS in the GWAS for this trait (including CRHR1, which was part of a large LD block in Chr17 and also was prominently implicated in the PrediXcan-S analyses, noted above), and added METTL15 and AUTS2 which were GWS in the case-control analysis. We then imported this list of 10 genes (MAD1L1, METTL15, AUTS2, TSNARE1, EXD3, PLEKHM1, TCF4, TRAIP, C19orf60, CRHR1) into the Drug Gene Interaction Database v3.0 (dgidb.genome.wustl.edu) (Cotto et al, 2018) to determine if there were interactions with available drug treatments that might indicate potential novel drug strategies for PTSD.…”

Section: H Drug Repositioning Analysismentioning

confidence: 99%

Genomic Characterization of Posttraumatic Stress Disorder in a Large US Military Veteran Sample

Stein

Levey

Cheng

et al. 2019

Preprint

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Individuals vary in their liability to develop Posttraumatic Stress Disorder (PTSD), the symptoms of which are highly heterogeneous, following exposure to life-threatening trauma. Understanding genetic factors that contribute to the biology of PTSD is critical for refining diagnosis and developing new treatments. Using genetic data from more than 250,000 participants in the Million Veteran Program, genomewide association analyses were conducted using a validated electronic health record-based algorithmically-defined PTSD diagnosis phenotype (48,221 cases and 217,223 controls), and PTSD quantitative symptom phenotypes (212,007 individuals). We identified several genome-wide significant loci in the case-control analyses, and numerous such loci in the quantitative trait analyses, including some (e.g., MAD1L1; TCF4; CRHR1) that were associated with multiple symptom sub-domains and total symptom score, and others that were more specific to certain symptom sub-domains (e.g., CAMKV to reexperiencing; SOX6 to hyperarousal). Genetic correlations between all pairs of symptom sub-domains and their total were very high (rg 0.93 -0.98) supporting validity of the PTSD diagnostic construct. We also demonstrate strong shared heritability with a range of traits, show that heritability persists when conditioned on other major psychiatric disorders, present independent replication results, provide support for one of the implicated genes in postmortem brain of individuals with PTSD, and use this information to identify potential drug repositioning candidates. These results point to the utility of genetics to inform and validate the biological coherence of the PTSD syndrome despite considerable heterogeneity at the symptom level, and to provide new directions for treatment development.

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Section: H Drug Repositioning Analysismentioning

confidence: 99%

Genomic Characterization of Posttraumatic Stress Disorder in a Large US Military Veteran Sample

Stein

Levey

Cheng

et al. 2019

Preprint

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“…A p-value <.05 was considered to be statistically significant. Finally, the inclusion of identified genes in the druggable genome was checked using DGIdb (Cotto et al, 2018), 3 | RESULTS…”

Section: Statistical Analysesmentioning

confidence: 99%

A pharmacogenetic risk score for the evaluation of major depression severity under treatment with antidepressants

Kanders

Pisanu

Bandstein

et al. 2019

Drug Development Research

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The severity of symptoms as well as efficacy of antidepressants in major depressive disorder (MDD) is modified by single nucleotide polymorphisms (SNPs) in different genes, which may contribute in an additive or synergistic fashion. We aimed to investigate depression severity in participants with MDD under treatment with antidepressants in relation to the combinatory effect of selected genetic variants combined using a genetic risk score (GRS). The sample included 150 MDD patients on regular AD therapy from the population‐based Swiss PsyCoLaus cohort. We investigated 44 SNPs previously associated with antidepressant response by ranking them with regard to their association to the Center for Epidemiologic Studies Short Depression Scale (CES‐D) score using random forest. The three top scoring SNPs (rs12248560, rs878567, rs17710780) were subsequently combined into an unweighted GRS, which was included in linear and logistic regression models using the CES‐D score, occurrence of a major depressive episode (MDE) during follow‐up and regular antidepressant treatment during the 6 months preceding follow‐up assessment as outcomes. The GRS was associated with MDE occurrence (p = .02) and ln CES‐D score (p = .001). The HTR1A rs878567 variant was associated with ln CES‐D after adjustment for demographic and clinical variables [p = .02, lower scores for minor allele (G) carriers]. Additionally, rs12248560 (CYP2C19) CC homozygotes showed a six‐fold higher likelihood of regular AD therapy at follow‐up compared to minor allele homozygotes [TT; ultrarapid metabolizers (p = .03)]. Our study suggests that the cumulative consideration of pharmacogenetic risk variants more reliably reflects the impact of the genetic background on depression severity than individual SNPs.

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“…Additionally, to enable hypergeometric significant testing with the universal network as the background, only UC and CD genes present in the universal network are included in the analyses. eQTL datasets for CD were retrieved from (69) while the list of targets related to drug-interactions was downloaded from (123).…”

Section: Ibd and Drug Target Associated Genesmentioning

confidence: 99%

Regulatory network analysis of Paneth cell and goblet cell enriched gut organoids using transcriptomics approaches

Treveil

Sudhakar

Matthews

et al. 2019

Preprint

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The epithelial lining of the small intestine consists of multiple cell types, including Paneth cells and goblet cells, that work in cohort to maintain gut health. 3D in vitro cultures of human primary epithelial cells, called organoids, have become a key model to study the functions of Paneth cells and goblet cells in normal and diseased conditions. Advances in these models include the ability to skew differentiation to particular lineages, providing a useful tool to study cell type specific function/dysfunction in the context of the epithelium. Here, we use comprehensive profiling of mRNA, microRNA and long noncoding RNA expression to confirm that Paneth cell and goblet cell enrichment of murine small intestinal organoids (enteroids) establishes a physiologically accurate model. We employ network analysis to infer the regulatory landscape altered by skewing differentiation, and using knowledge of cell type specific markers, we predict key regulators of cell type specific functions: Cebpa, Jun, Nr1d1 and Rxra specific to Paneth cells, Gfi1b and Myc specific for goblet cells and Ets1, Nr3c1 and Vdr shared between them. Links identified between these regulators and cellular phenotypes of inflammatory bowel disease (IBD) suggest that global regulatory rewiring during or after differentiation of Paneth cells and goblet cells could contribute to IBD aetiology. Future application of cell type enriched enteroids combined with the presented computational workflow can be used to disentangle multifactorial mechanisms of these cell types and propose regulators whose pharmacological targeting could be advantageous in treating IBD patients with Crohn's disease or ulcerative colitis.

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DGIdb 3.0: a redesign and expansion of the drug–gene interaction database

Cited by 731 publications

References 26 publications

Genomic Characterization of Posttraumatic Stress Disorder in a Large US Military Veteran Sample

Genomic Characterization of Posttraumatic Stress Disorder in a Large US Military Veteran Sample

A pharmacogenetic risk score for the evaluation of major depression severity under treatment with antidepressants

Regulatory network analysis of Paneth cell and goblet cell enriched gut organoids using transcriptomics approaches

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