Biomarker Prioritisation and Power Estimation Using Ensemble Gene Regulatory Network Inference

Aziz, Furqan; Acharjee, Animesh; Williams, John A.; Russ, Dominic; Bravo-Merodio, Laura; Gkoutos, Georgios V.

doi:10.3390/ijms21217886

Cited by 4 publications

(3 citation statements)

References 46 publications

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“…This study was limited to viewing causality through the lens of Mendelian randomization alone. In our previous work, we have used gene expression data to identify causal regulatory networks using Bayesian approaches [ 37 ]. Opportunities exist to combine these approaches, creating multimodal graphs of gene regulatory networks from that approach with the multitrait networks created in this work.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

confidence: 99%

A Causal Web between Chronotype and Metabolic Health Traits

Williams

Russ

Bravo-Merodio

et al. 2021

Genes

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“…Each GEO dataset was downloaded and loaded into R (version 4.0.3) by using the getGEO function in the GEOquery package (version 2.58.0) [ 18 ]. All datasets, apart from GSE151158, were already normalised.…”

Section: Methodsmentioning

confidence: 99%

Machine Learning-Based Identification of Potentially Novel Non-Alcoholic Fatty Liver Disease Biomarkers

et al. 2021

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Non-alcoholic fatty liver disease (NAFLD) is a chronic liver disease that presents a great challenge for treatment and prevention.. This study aims to implement a machine learning approach that employs such datasets to identify potential biomarker targets. We developed a pipeline to identify potential biomarkers for NAFLD that includes five major processes, namely, a pre-processing step, a feature selection and a generation of a random forest model and, finally, a downstream feature analysis and a provision of a potential biological interpretation. The pre-processing step includes data normalising and variable extraction accompanied by appropriate annotations. A feature selection based on a differential gene expression analysis is then conducted to identify significant features and then employ them to generate a random forest model whose performance is assessed based on a receiver operating characteristic curve. Next, the features are subjected to a downstream analysis, such as univariate analysis, a pathway enrichment analysis, a network analysis and a generation of correlation plots, boxplots and heatmaps. Once the results are obtained, the biological interpretation and the literature validation is conducted over the identified features and results. We applied this pipeline to transcriptomics and lipidomic datasets and concluded that the C4BPA gene could play a role in the development of NAFLD. The activation of the complement pathway, due to the downregulation of the C4BPA gene, leads to an increase in triglyceride content, which might further render the lipid metabolism. This approach identified the C4BPA gene, an inhibitor of the complement pathway, as a potential biomarker for the development of NAFLD.

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“…The study of complex network allows scientists to investigate different properties of complex systems. Complex network has many applications in diverse disciplines, including; text analysis (1), computer vision (2), chemoinformatics (3), biological network analysis (4), and social network analysis (5; 6). Once a system has been represented as a complex network, a number of existing graph-based methods can be used to understand the complex structure of the underlying system and cater decisions or improvements.…”

Section: Introductionmentioning

confidence: 99%