Tissue-specific genetic features inform prediction of drug side effects in clinical trials

Duffy, Áine; Verbanck, Marie; Dobbyn, Amanda; Won, Hong-Hee; Rein, Joshua L.; Forrest, Iain S; Nadkarni, Girish N.; Rocheleau, Ghislain; Do, Ron

doi:10.1126/sciadv.abb6242

Cited by 45 publications

(37 citation statements)

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“…To further demonstrate functionality of the phenome-wide map, we tested for enrichment of colocalized genes and phenotypes in the phenome-wide map with drug side effects in corresponding drug target genes in an adapted integrated clinical trial dataset of 1,780 drugs [25] (see Methods ) in each of the 48 GTEx tissues, adjusting for co-cluster grouping. In almost all tissues, we observed marked enrichments of reported drug side effects with colocalized target genes and phenotypes ( Figure 4 ; Table S5, Additional file 1 ), with the strongest associated odds ratio (OR) observed in “Minor Salivary Gland” (OR = 86.43, 95% confidence interval (CI) = (11.596 – 11,078.85), p-value = 6.12 × 10 −11 ), in “Breast - Mammary Tissue” (OR = 10.98, 95% CI = (3.514 – 54.897), p-value = 2.19 × 10 −6 ) and in “Cells - EBV-transformed lymphocytes” (OR = 7.03, 95% CI = (2.070 - 28.367), p-value = 1.43 × 10 −3 ).…”

Section: Resultsmentioning

confidence: 99%

Section: Drug Side Effects Enrichmentmentioning

confidence: 99%

“…To test if the identified co-clusters were enriched with target genes with reported drug side effects for relevant phenotypes, we used our integrated clinical-genetic drug side effect dataset described and published in [25]. This consists of 1,780 drugs and their gene targets with side effect and coloc2 results mapped to 48 GTEx terms (see [25] for mapping approach). To combine this dataset with our biLouvain co-clusters, we recorded the side effect, colocalized phenotype and tissue for each drug-gene pair (rather than collapse all gene targets per drug as in [25]).…”

Section: Drug Side Effects Enrichmentmentioning

confidence: 99%

“…This consists of 1,780 drugs and their gene targets with side effect and coloc2 results mapped to 48 GTEx terms (see [25] for mapping approach). To combine this dataset with our biLouvain co-clusters, we recorded the side effect, colocalized phenotype and tissue for each drug-gene pair (rather than collapse all gene targets per drug as in [25]). We restricted the dataset to drug genes present in each tissue-level network by mapping these genes to their corresponding co-cluster for that tissue.…”

Section: Drug Side Effects Enrichmentmentioning

confidence: 99%

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A tissue-level phenome-wide network map of colocalized genes and phenotypes in the UK Biobank

Rocheleau

Forrest

Duffy

et al. 2021

Preprint

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BackgroundPhenome-wide association studies conducted in electronic health record (EHR)-linked biobanks have uncovered a large number of genomic loci associated with traits and diseases. However, interpretation of the complex relationships of associated genes and phenotypes is challenging.ResultsWe constructed a tissue-level phenome-wide network map of colocalized genes and phenotypes. First, we generated colocalized expression quantitative trait loci from 48 tissues of the Genotype-Tissue Expression project and from publicly available genome-wide association study summary statistics from the UK Biobank. We identified 9,151 colocalized genes for 1,411 phenotypes across 48 tissues. Then, we constructed a bipartite network using the colocalized signals to establish links between genes and phenotypes in each tissue. The majority of links are observed in a single tissue whereas only a few are present in all tissues. Finally, we applied the biLouvain clustering algorithm in each tissue-specific bipartite network to identify co-clusters of non-overlapping genes and phenotypes. The majority of co-clusters contains a small number of genes and phenotypes, and 88.6% of co-clusters are found in only one tissue. To demonstrate functionality of the phenome-wide map, we tested if these co-clusters were enriched with known biological and functional gene classes and observed several significant enrichments. Furthermore, we observed that tissue-specific co-clusters are enriched with reported drug side effects for the corresponding drug target genes in clinical trial data.ConclusionsThe phenome-wide map provides links between genes, phenotypes and tissues across a wide spectrum of biological classes and can yield biological and clinical discoveries. The phenome-wide map is publicly available at https://rstudio-connect.hpc.mssm.edu/biPheMap/.

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Section: Resultsmentioning

confidence: 99%

Section: Drug Side Effects Enrichmentmentioning

confidence: 99%

Section: Drug Side Effects Enrichmentmentioning

confidence: 99%

Section: Drug Side Effects Enrichmentmentioning

confidence: 99%

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A tissue-level phenome-wide network map of colocalized genes and phenotypes in the UK Biobank

Rocheleau

Forrest

Duffy

et al. 2021

Preprint

Self Cite

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“…Previous phenome-wide association study in combination with tissue-specific gene expression and eQTL showed that the presence of eQTL in multiple tissues resulted in more unique phenotypes driven by genome-wide association loci and drugs delivered to multiple tissues can induce several side effects (Duffy et al, 2020). More importantly, recent establishment of an amount of publicly available databases based large-scale omics provided a timely opportunity to understand the effects and safety of target drugs across populations.…”

Section: Discussionmentioning

confidence: 99%

The Effect of Sodium Glucose Cotransporter 2 Inhibitors From a Human Genetic Perspective

Liu

Zhang

2021

Front. Genet.

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Drug contraindications in comorbid diseases: a protein interactome perspective

Karunakaran,

Ganapathiraju,

Jain

et al. 2024

Netw Model Anal Health Inform Bioinforma

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Adverse drug reactions (ADRs) are leading causes of death and drug withdrawals and frequently co-occur with comorbidities. However, systematic studies on the effects of drugs on comorbidities are lacking. Drug interactions with the cellular protein–protein interaction (PPI) network give rise to ADRs. We selected 6 comorbid disease pairs, identified the drugs used in the treatment of the individual diseases ‘A’ and ‘B’– 44 drugs in anxiety and depression, 128 in asthma and hypertension, 48 in chronic obstructive pulmonary disease and heart failure, 58 in type 2 diabetes and obesity, 58 in Parkinson’s disease and schizophrenia, and 84 in rheumatoid arthritis and osteoporosis—and categorized them based on whether they aggravate the comorbid condition. We constructed drug target networks (DTNs) and examined their enrichment among genes in disease A/B PPI networks, expressed across 53 tissues and involved in ~ 1000 pathways. To characterize the biological features of the DTNs, we performed principal component analysis and computed the Euclidean distance between DTN component scores and feature loading values. DTNs of disease A drugs not contraindicated in B were affiliated with proteins common to A/B networks or uniquely found in the B network, similarly regulated common pathways, and disease-B specific pathways and tissues. DTNs of disease A drugs contraindicated in B were affiliated with common proteins or those uniquely found in the A network, differentially regulated common pathways, and disease A-specific pathways and tissues. Hence, DTN enrichment in pathways, tissues, and PPI networks of comorbid diseases will help identify drug contraindications in comorbidities.

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Tissue-specific genetic features inform prediction of drug side effects in clinical trials

Cited by 45 publications

References 36 publications

A tissue-level phenome-wide network map of colocalized genes and phenotypes in the UK Biobank

A tissue-level phenome-wide network map of colocalized genes and phenotypes in the UK Biobank

The Effect of Sodium Glucose Cotransporter 2 Inhibitors From a Human Genetic Perspective

Drug contraindications in comorbid diseases: a protein interactome perspective

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