Background:
The etiology of Alzheimer’s disease remains poorly understood at the mechanistic
level, and genome-wide network-based genetics have the potential to provide new insights into the
disease mechanisms.
Objective:
The study aimed to explore the collective effects of multiple genetic association signals on an
AV-45 PET measure, which is a well-known Alzheimer’s disease biomarker, by employing a networ kassisted
strategy.
Method:
First, we took advantage of a dense module search algorithm to identify modules enriched by
genetic association signals in a protein-protein interaction network. Next, we performed statistical evaluation
to the modules identified by dense module search, including a normalization process to adjust the
topological bias in the network, a replication test to ensure the modules were not found randomly , and a
permutation test to evaluate unbiased associations between the modules and amyloid imaging phenotype.
Finally, topological analysis, module similarity tests and functional enrichment analysis were performed
for the identified modules.
Results:
We identified 24 consensus modules enriched by robust genetic signals in a genome-wide association
analysis. The results not only validated several previously reported AD genes (APOE, APP,
TOMM40, DDAH1, PARK2, ATP5C1, PVRL2, ELAVL1, ACTN1 and NRF1), but also nominated a few
novel genes (ABL1, ABLIM2) that have not been studied in Alzheimer’s disease but have shown associations
with other neurodegenerative diseases.
Conclusion:
The identified genes, consensus modules and enriched pathways may provide important
clues to future research on the neurobiology of Alzheimer’s disease and suggest potential therapeutic
targets.
Only one independent genetic association with rheumatoid arthritis within the KIAA1109-TENR-IL2-IL21 locus in Caucasian sample sets: confirmation of association of rs6822844 with rheumatoid arthritis at a genome-wide level of significance
