ArticleBMJ open2015
Detection for pathway effect contributing to disease in systems epidemiology with a case-control design.
Article in BMJ open, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- MCC-SP: a powerful integration method for identification of causal pathways from genetic variants to complex disease.BMC genetics · 2020Article
- A novel path-specific effect statistic for identifying the differential specific paths in systems epidemiology.BMC genetics · 2020Article
- PMINR: Pointwise Mutual Information-Based Network Regression - With Application to Studies of Lung Cancer and Alzheimer's Disease.Frontiers in genetics · 2020Article
- A Machine Learning Method for Identifying Critical Interactions Between Gene Pairs in Alzheimer's Disease Prediction.Frontiers in neurology · 2019Article
- A new insight into underlying disease mechanism through semi-parametric latent differential network model.BMC bioinformatics · 2018Article
- REDESIGN: RDF-based Differential Signaling Framework for Precision Medicine Analytics.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2018Article
- JDINAC: joint density-based non-parametric differential interaction network analysis and classification using high-dimensional sparse omics data.Bioinformatics (Oxford, England) · 2017Article
- A powerful weighted statistic for detecting group differences of directed biological networks.Scientific reports · 2016Article
- A powerful score-based statistical test for group difference in weighted biological networks.BMC bioinformatics · 2016Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesIdentification of pathway effects responsible for specific diseases has been one of the essential tasks in systems epidemiology. Despite some advance in procedures for distinguishing specific pathway (or network) topology between different disease status, statistical inference at a population level remains unsolved and further development is still needed. To identify the specific pathways contributing to diseases, we attempt to develop powerful statistics which can capture the complex relationship among risk factors. SETTING AND
participantsAcute myeloid leukaemia (AML) data obtained from 133 adults (98 patients and 35 controls; 47% female).
resultsSimulation studies indicated that the proposed Pathway Effect Measures (PEM) were stable; bootstrap-based methods outperformed the others, with bias-corrected bootstrap CI method having the highest power. Application to real data of AML successfully identified the specific pathway (Treg→TGFβ→Th17) effect contributing to AML with p values less than 0.05 under various methods and the bias-corrected bootstrap CI (-0.214 to -0.020). It demonstrated that Th17-Treg correlation balance was impaired in patients with AML, suggesting that Th17-Treg imbalance potentially plays a role in the pathogenesis of AML.
conclusionsThe proposed bootstrap-based PEM are valid and powerful for detecting the specific pathway effect contributing to disease, thus potentially providing new insight into the underlying mechanisms and ways to study the disease effects of specific pathways more comprehensively.
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