Abstract
Introduction—We sought to determine whether a systems biology approach may identify novel late-onset Alzheimer's disease (LOAD) loci.
Methods—We performed gene-wide association analyses and integrated results with human protein-protein interaction data using network analyses. We performed functional validation on novel genes using a transgenic Caenorhabditis elegans Aβ proteotoxicity model and evaluated novel genes using brain expression data from people with LOAD and other neurodegenerative conditions.
Results—We identified 13 novel candidate LOAD genes outside chromosome 19. Of those, RNA interference knockdowns of the C. elegans orthologs of UBC, NDUFS3, EGR1, and ATP5H were associated with Aβ toxicity, and NDUFS3, SLC25A11, ATP5H, and APP were differentially expressed in the temporal cortex.
Discussion—Network analyses identified novel LOAD candidate genes. We demonstrated a functional role for four of these in a C. elegans model and found enrichment of differentially expressed genes in the temporal cortex.
Document Type
Article
Publication Date
10-2017
Digital Object Identifier (DOI)
https://doi.org/10.1016/j.jalz.2017.01.016
Funding Information
The European Alzheimer’s Disease Initiative was supported by the Labex (laboratory of excellence program investment for the future) DISTALZ grant, Inserm, Institut Pasteur de Lille, Université de Lille 2, and the Lille University Hospital. GERAD was supported by the Medical Research Council (grant number 503480), Alzheimer’s Research UK (grant number 503176), the Wellcome Trust (grant number 082604/2/07/Z), and German Federal Ministry of Education and Research (BMBF): Competence Network Dementia (CND) grant numbers 01GI0102, 01GI0711, 01GI0420. Cohorts for Heart and Aging Research in Genomic Epidemiology was partly supported by the NIH/NIA grant R01 AG033193 and the NIA AG081220 and AGES contract N01-AG-12100, the NHLBI grant R01 HL105756, the Icelandic Heart Association, and the Erasmus Medical Center and Erasmus University. Alzheimer’s Disease Genetics Consortium (ADGC) was supported by the NIH/NIA grants: U01 AG032984, U24 AG021886, U01 AG016976, and the Alzheimer’s Association grant ADGC-10-196728. S.M. and P.K.C. were supported by NIH grants U01AG006781, U01HG006375, and R01AG042437. S.M. was also supported by Amazon Web Services in Education Research Grant Award. This work was supported by NIH grant R01AG038518 to M.R.K. and the UW Nathan Shock Center of Excellence in the Basic Biology of Aging (NIH grant P30AG013280). This work was also supported by NIH R01AG032990, R01NS080820, U01AG046139, P50AG16574 (NET).
Related Content
Refer to Web version on PubMed Central for supplementary material.
Repository Citation
Mukherjee, Shubhabrata; Russell, Joshua C.; Carr, Daniel T.; Burgess, Jeremy D.; Allen, Mariet; Serie, Daniel J.; Boehme, Kevin L.; Kauwe, John S. K.; Naj, Adam C.; Fardo, David W.; Dickson, Dennis W.; Montine, Thomas J.; Ertekin-Taner, Nilufer; Kaeberlein, Matt R.; and Crane, Paul K., "Systems Biology Approach to Late-Onset Alzheimer's Disease Genome-Wide Association Study Identifies Novel Candidate Genes Validated Using Brain Expression Data and Caenorhabditis elegans Experiments" (2017). Biostatistics Faculty Publications. 42.
https://uknowledge.uky.edu/biostatistics_facpub/42
Supplementary Material
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Notes/Citation Information
Published in Alzheimer's & Dementia, v. 13, issue 10, p. 1133-1142.
© 2017 The Alzheimer's Association. Published by Elsevier Inc. All rights reserved.
This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/.
The document available for download is the author's post-peer-review final draft of the article.