EpiGraphDB: a database and data mining platform for health data science

Liu, Yi; Elsworth, Benjamin; Erola, Pau; Haberland, Valeriia; Hemani, Gibran; Lyon, M.; Zheng, Jie; Lloyd, O. C.; Vabistsevits, Marina; Gaunt, Tom R.

doi:10.1093/bioinformatics/btaa961

Cited by 37 publications

(16 citation statements)

References 38 publications

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“…To support the existence of the confounders indicated by LHC-MR for 38 trait pairs, we used EpiGraphDB [28,29] to systematically identify those potential confounders. The database provided for each potential confounder a causal effect on trait X and Y ( r 1, and r 3 in their notation), the ratio of which ( r 3 /r 1 ) was compared to our LHC-MR estimated t y /t x values representing the strength of the confounder acting on the two traits.…”

Section: Resultsmentioning

confidence: 99%

Simultaneous estimation of bi-directional causal effects and heritable confounding from GWAS summary statistics

Darrous

Mounier

Kutalik

2020

Preprint

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Mendelian Randomisation (MR), an increasingly popular method that estimates the causal effects of risk factors on complex human traits, has seen several extensions that relax its basic assumptions. However, most of these extensions suffer from two major limitations; their under-exploitation of genome-wide markers, and sensitivity to the presence of a heritable confounder of the exposure-outcome relationship. To overcome these limitations, we propose a Latent Heritable Confounder MR (LHC-MR) method applicable to association summary statistics, which estimates bi-directional causal effects, direct heritability, and confounder effects while accounting for sample overlap. We demonstrate that LHC-MR outperforms several existing MR methods in a wide range of simulation settings and apply it to summary statistics of 13 complex traits. Besides several concordant results, LHC-MR unravelled new mechanisms (how being diagnosed for certain diseases might lead to improved lifestyle) and revealed potential false positive findings of standard MR methods (apparent causal effect of body mass index on educational attainment may be driven by a strong ignored confounder). Phenome-wide search to identify LHC-implied heritable confounders showed remarkable agreement between the LHC-estimated causal effects of the latent confounder and those for the potentially identified ones. Finally, LHC-MR naturally decomposes genetic correlation to causal effect-driven and confounder-driven contributions, demonstrating that the genetic correlation between systolic blood pressure and diabetes is predominantly confounder-driven.

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Section: Resultsmentioning

confidence: 99%

Simultaneous estimation of bi-directional causal effects and heritable confounding from GWAS summary statistics

Darrous

Mounier

Kutalik

2020

Preprint

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“…A general workflow for this study is depicted in Figure 1 . Using the EpiGraphDB R API v1.0 [ 9 ], results of pre-computed Mendelian randomization studies were obtained with the following parameters: exposure trait either “Morning/evening person (chronotype)” or “Chronotype”, with a p -value threshold of 5 × 10 −8 (GWAS genome-wide significance). Results were filtered to significant random-effects inverse variance-weighted multi-SNP meta-analyses (IVW), with 120 significant associations retained, and additionally manually curated to keep associations between chronotype and mental or cardiometabolic health (see Table S2 for SNP characteristics).…”

Section: Methodsmentioning

confidence: 99%

A Causal Web between Chronotype and Metabolic Health Traits

Williams

Russ

Bravo-Merodio

et al. 2021

Genes

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Observational and experimental evidence has linked chronotype to both psychological and cardiometabolic traits. Recent Mendelian randomization (MR) studies have investigated direct links between chronotype and several of these traits, often in isolation of outside potential mediating or moderating traits. We mined the EpiGraphDB MR database for calculated chronotype–trait associations (p-value < 5 × 10−8). We then re-analyzed those relevant to metabolic or mental health and investigated for statistical evidence of horizontal pleiotropy. Analyses passing multiple testing correction were then investigated for confounders, colliders, intermediates, and reverse intermediates using the EpiGraphDB database, creating multiple chronotype–trait interactions among each of the the traits studied. We revealed 10 significant chronotype–exposure associations (false discovery rate < 0.05) exposed to 111 potential previously known confounders, 52 intermediates, 18 reverse intermediates, and 31 colliders. Chronotype–lipid causal associations collided with treatment and diabetes effects; chronotype–bipolar associations were mediated by breast cancer; and chronotype–alcohol intake associations were impacted by confounders and intermediate variables including known zeitgebers and molecular traits. We have reported the influence of chronotype on several cardiometabolic and behavioural traits, and identified potential confounding variables not reported on in studies while discovering new associations to drugs and disease.

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“…In this project we used the knowledge graph EpiGraphDB [26], available at https://epigraphdb.org, to extract the human interactome of protein-protein interactions (PPI) originally obtained from StringDB [27]. Only high confidence interactions (interaction score > 700) were included in the database.…”

Section: Data Sources and Data Integrationmentioning

confidence: 99%

The network of SARS-CoV-2—cancer molecular interactions and pathways

Erola

Martin

Gaunt

2022

Preprint

Self Cite

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BackgroundRelatively little is known about the long-term impacts of SARS-CoV-2 biology, including whether it increases the risk of cancer. This study aims to identify the molecular interactions between COVID-19 infections and cancer processes.Materials and MethodsWe integrated recent data on SARS-CoV-2 – host protein interactions, risk factors for critical illness, known oncogenes, tumor suppressor genes and cancer drivers in EpiGraphDB, a database of disease biology and epidemiology. We used these data to reconstruct the network of molecular links between SARS-CoV-2 infections and cancer processes in various tissues expressing the angiotensin-converting enzyme 2 (ACE2) receptor. We applied community detection algorithms and Gene Set Enrichment Analysis (GSEA) to identify cancer-relevant pathways that may be perturbed by SARS-CoV-2 infection.ResultsIn lung tissue, the results showed that 4 oncogenes are potentially targeted by SARS-CoV-2, and 92 oncogenes interact with other human genes targeted by SARS-CoV-2. We found evidence of potential SARS-CoV-2 interactions with Wnt and hippo signaling pathways, telomere maintenance, DNA replication, protein ubiquitination and mRNA splicing. Some of these pathways were potentially affected in multiple tissues.ConclusionsThe long-term implications of SARS-CoV-2 infection are still unknown, but our results point to the potential impact of infection on pathways relevant to cancer affecting cell proliferation, development and survival, favoring DNA degradation, preventing the repair of damaging events and impeding the translation of RNA into working proteins. This highlights the need for further research to investigate whether such effects are transient or longer lasting. Our results are openly available in the EpiGraphDB platform at https://epigraphdb.org/covid-cancer and the repository https://github.com/MRCIEU/covid-cancer (https://doi.org/10.5281/zenodo.6391588).

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EpiGraphDB: a database and data mining platform for health data science

Cited by 37 publications

References 38 publications

Simultaneous estimation of bi-directional causal effects and heritable confounding from GWAS summary statistics

Simultaneous estimation of bi-directional causal effects and heritable confounding from GWAS summary statistics

A Causal Web between Chronotype and Metabolic Health Traits

The network of SARS-CoV-2—cancer molecular interactions and pathways

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