Bayesian Graphical Network Analyses Reveal Complex Biological Interactions Specific to Alzheimer's Disease

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dc.citation.firstpage917
dc.citation.issueNumber3
dc.citation.journalTitleJournal of Alzheimer's Disease
dc.citation.lastpage925
dc.citation.volumeNumber44
dc.contributor.authorRembach, Alan
dc.contributor.authorStingo, Francesco C.
dc.contributor.authorPeterson, Christine
dc.contributor.authorVannucci, Marina
dc.contributor.authorDo, Kim-Anh
dc.contributor.authorWilson, William J.
dc.contributor.authorMacaulay, S. Lance
dc.contributor.authorRyan, Timothy M.
dc.contributor.authorMartins, Ralph N.
dc.contributor.authorAmes, David
dc.contributor.authorMasters, Colin L.
dc.contributor.authorDoecke, James D.
dc.contributor.authorThe AIBL Research Group
dc.date.accessioned2017-06-06T19:07:23Z
dc.date.available2017-06-06T19:07:23Z
dc.date.issued2015
dc.description.abstractWith different approaches to finding prognostic or diagnostic biomarkers for Alzheimer's disease (AD), many studies pursue only brief lists of biomarkers or disease specific pathways, potentially dismissing information from groups of correlated biomarkers. Using a novel Bayesian graphical network method, with data from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging, the aim of this study was to assess the biological connectivity between AD associated blood-based proteins. Briefly, three groups of protein markers (18, 37, and 48 proteins, respectively) were assessed for the posterior probability of biological connection both within and between clinical classifications. Clinical classification was defined in four groups: high performance healthy controls (hpHC), healthy controls (HC), participants with mild cognitive impairment (MCI), and participants with AD. Using the smaller group of proteins, posterior probabilities of network similarity between clinical classifications were very high, indicating no difference in biological connections between groups. Increasing the number of proteins increased the capacity to separate both hpHC and HC apart from the AD group (0 for complete separation, 1 for complete similarity), with posterior probabilities shifting from 0.89 for the 18 protein group, through to 0.54 for the 37 protein group, and finally 0.28 for the 48 protein group. Using this approach, we identified beta-2 microglobulin (β2M) as a potential master regulator of multiple proteins across all classifications, demonstrating that this approach can be used across many data sets to identify novel insights into diseases like AD.
dc.identifier.citationRembach, Alan, Stingo, Francesco C., Peterson, Christine, et al.. "Bayesian Graphical Network Analyses Reveal Complex Biological Interactions Specific to Alzheimer's Disease." <i>Journal of Alzheimer's Disease,</i> 44, no. 3 (2015) IOS Press: 917-925. https://doi.org/10.3233/JAD-141497.
dc.identifier.doihttps://doi.org/10.3233/JAD-141497
dc.identifier.urihttps://hdl.handle.net/1911/94819
dc.language.isoeng
dc.publisherIOS Press
dc.rightsThis is an author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by IOS.
dc.subject.keywordAlzheimer's disease
dc.subject.keywordBayesian
dc.subject.keywordbiomarkers
dc.subject.keywordgraphical networks
dc.subject.keywordimputation
dc.titleBayesian Graphical Network Analyses Reveal Complex Biological Interactions Specific to Alzheimer's Disease
dc.typeJournal article
dc.type.dcmiText
dc.type.publicationpost-print
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