Literature DB >> 25720397

An Integrated Bioinformatics Approach for Identifying Genetic Markers that Predict Cerebrospinal Fluid Biomarker p-tau181/Aβ1-42 Ratio in ApoE4-Negative Mild Cognitive Impairment Patients.

Ying Sun1, Anders Bresell2, Mattias Rantalainen3, Kina Höglund4, Thibaud Lebouvier5, Hugh Salter1.   

Abstract

Alzheimer's disease (AD) is the most common form of dementia, with no disease-modifying treatment yet available. Early detection of patients at risk of developing AD is of central importance. Blood-based genetic signatures can serve as early detection and as population-based screening tools. In this study, we aimed to identify genetic markers and gene signatures associated with cerebrospinal fluid (CSF) biomarkers levels of t-tau, p-tau181, and with the two ratios t-tau/Aβ1-42 and p-tau181/Aβ1-42 in the context of progression from mild cognitive impairment (MCI) to AD, and to identify a panel of genetic markers that can predict CSF biomarker p-tau181/Aβ1-42 ratio with consideration of APOE ε4 stratification. We analyzed genome-wide the Alzheimer's Disease Neuroimaging Initiative dataset with up to 48 months follow-up. In the first part of the analysis, the main effect of single nucleotide polymorphisms (SNPs) under an additive genetic model was assessed for each of the four CSF biomarkers. In the second part of the analysis, we performed an integrated analysis of genome-wide association study results with pathway enrichment analysis, predictive modeling and network analysis in the subgroup of ApoE4-negative subjects. We identified a panel of five SNPs, rs6766238, rs1143960, rs1249963, rs11975968, and rs4836493, that are predictive for p-tau181/Aβ1-42 ratio (high/low) with a sensitivity of 66% and a specificity of 70% (AUC 0.74). These results suggest that a panel of SNPs is a potential prognostic biomarker in ApoE4-negative MCI patients.

Entities:  

Keywords:  Alzheimer's disease; cerebrospinal fluid; genome-wide association study; mild cognitive impairment; multivariate analysis; pathway analysis; predictive model

Mesh:

Substances:

Year:  2015        PMID: 25720397     DOI: 10.3233/JAD-142118

Source DB:  PubMed          Journal:  J Alzheimers Dis        ISSN: 1387-2877            Impact factor:   4.472


  4 in total

1.  Tau Diagnostics and Clinical Studies.

Authors:  Illana Gozes; Günter Höglinger; James P Quinn; Nigel M Hooper; Kina Höglund
Journal:  J Mol Neurosci       Date:  2017-10       Impact factor: 3.444

Review 2.  Specific protein biomarker patterns for Alzheimer's disease: improved diagnostics in progress.

Authors:  Illana Gozes
Journal:  EPMA J       Date:  2017-09-04       Impact factor: 6.543

3.  Analyzing the genes related to Alzheimer's disease via a network and pathway-based approach.

Authors:  Yan-Shi Hu; Juncai Xin; Ying Hu; Lei Zhang; Ju Wang
Journal:  Alzheimers Res Ther       Date:  2017-04-27       Impact factor: 6.982

Review 4.  Potential fluid biomarkers for pathological brain changes in Alzheimer's disease: Implication for the screening of cognitive frailty.

Authors:  Qingwei Ruan; Grazia D'Onofrio; Daniele Sancarlo; Antonio Greco; Zhuowei Yu
Journal:  Mol Med Rep       Date:  2016-08-09       Impact factor: 2.952

  4 in total

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