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[F1–02–04]: INTEGRATING MULTI‐MODALITY IMAGING AND MULTI‐LAYER ‐OMICS TO ADVANCE THE SYSTEMS BIOLOGY OF ALZHEIMER's DISEASE
Author(s) -
Saykin Andrew J.,
Nho Kwangsik,
Yan Jingwen,
Nudelman Kelly N.H.,
Risacher Shan L.,
Kim Sungeun,
Apostolova Liana G.,
Shen Li,
KaddurahDaouk Rima F.
Publication year - 2017
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1016/j.jalz.2017.07.018
Subject(s) - neuroimaging , alzheimer's disease neuroimaging initiative , epigenome , computational biology , epigenetics , proteome , genomics , biology , omics , systems biology , bioinformatics , disease , alzheimer's disease , neuroscience , dna methylation , medicine , genome , genetics , gene , pathology , gene expression
tomics and plasma proteomics data was available. The EMIF-AD biomarker discovery cohort consists of 1221 AD, MCI and control subjects, selected from the EMIF catalogue. All subjects had existing amyloid measures (CSF Ab or amyloid-PET), structural MRI and clinical data, and furthermore plasma proteomics (targeted and untargeted), CSF proteomics (targeted), metabolomics, genomics and epigenetics data were generated. For both studies univariate and multivariate statistics were utilised to identify candidate biomarkers of AD pathology (neurodegeneration and/or brain amyloid burden), rates of cognitive decline, and MCI progression to dementia. pQTL-eQTL-mQTL analyses, network/pathway analysis, andmultimodal classifiers were employed to detect multimodal signatures. Results: Initial analyses indicate that in the ANM-MMB study a serum and urine derived 15 metabolite classifier predicts MCI progression to AD with 72% accuracy, and the biological significance of the metabolites included in the biomarker panel was identified. Further analyses will examine whether a multimodal classifier is able to predict with even greater accuracy. We will then seek to replicate this in the EMIF-AD biomarker discovery study. Further analyses will also examine single and multimodal biomarker classifiers of other endophenotypes. Conclusions:These two studies could be used to identify novel and replicate previously identified single modality biomarker findings. Furthermore the impact of combining the additional modalities with these findings will be discussed. Computational and technical challenges encountered and the bioinformatics pipeline devised in the multimodal analysis of the ANMMMB cohort will be used to inform the analysis pipeline of the EMIF-AD biomarker discovery study as a replication.