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

Article type: Research Article

Authors: Rembach, Alana | Stingo, Francesco C.b | Peterson, Christinec | Vannucci, Marinad | Do, Kim-Anhb | Wilson, William J.e; f | Macaulay, S. Lanceg | Ryan, Timothy M.a | Martins, Ralph N.i | Ames, Davidh | Masters, Colin L.a | Doecke, James D.e; f; * | the AIBL Research Groupj

Affiliations: [a] The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, VIC, Australia | [b] The MD Anderson Cancer Center, Texas, Houston, USA | [c] Stanford University, Stanford, California, USA | [d] Rice University, Texas, Houston, USA | [e] CSIRO Digital Productivity and Services/Australian e-Health Research Centre, Royal Brisbane and Women's Hospital, Brisbane, QLD, Australia | [f] Cooperative Research Centre for Mental Health, Parkville, VIC, Australia | [g] Department of Psychiatry, St George's Hospital, University of Melbourne, VIC, Australia | [h] National Ageing Research Institute, Parkville, VIC, Australia | [i] Sir James McCusker Alzheimer's Disease Research Unit, Health Department of WA, Perth, WA, Australia | [j] http://aibl.csiro.au/

Correspondence: [*] Correspondence to: James D. Doecke, 5 CSIRO Digital Productivity and Services/Australian e-Health Research Centre, Royal Brisbane and Women's Hospital, Brisbane, QLD, 4029, Australia. E-mail: james.doecke@csiro.au.

Keywords: Alzheimer's disease, Bayesian, biomarkers, graphical networks, imputation

DOI: 10.3233/JAD-141497

Journal: Journal of Alzheimer's Disease, vol. 44, no. 3, pp. 917-925, 2015

Accepted 1 October 2014
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Published: 6 February 2015
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Supplementary Materials:

Supplementary Table 1.

Supplementary Table 2.

Supplementary Figure 1.

Supplementary Figure 2.

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Abstract

With 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.

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