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The Impact of Complement Genes on the Risk of Late-Onset Alzheimer’s Disease
Late-onset Alzheimer’s disease (LOAD), the most common cause of dementia, and a huge global health challenge, is a neurodegenerative disease of uncertain aetiology. To deliver effective diagnostics and therapeutics, understanding the molecular basis of the disease is essential. Contemporary large ge...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8003605/ https://www.ncbi.nlm.nih.gov/pubmed/33804666 http://dx.doi.org/10.3390/genes12030443 |
Sumario: | Late-onset Alzheimer’s disease (LOAD), the most common cause of dementia, and a huge global health challenge, is a neurodegenerative disease of uncertain aetiology. To deliver effective diagnostics and therapeutics, understanding the molecular basis of the disease is essential. Contemporary large genome-wide association studies (GWAS) have identified over seventy novel genetic susceptibility loci for LOAD. Most are implicated in microglial or inflammatory pathways, bringing inflammation to the fore as a candidate pathological pathway. Among the most significant GWAS hits are three complement genes: CLU, encoding the fluid-phase complement inhibitor clusterin; CR1 encoding complement receptor 1 (CR1); and recently, C1S encoding the complement enzyme C1s. Complement activation is a critical driver of inflammation; changes in complement genes may impact risk by altering the inflammatory status in the brain. To assess complement gene association with LOAD risk, we manually created a comprehensive complement gene list and tested these in gene-set analysis with LOAD summary statistics. We confirmed associations of CLU and CR1 genes with LOAD but showed no significant associations for the complement gene-set when excluding CLU and CR1. No significant association with other complement genes, including C1S, was seen in the IGAP dataset; however, these may emerge from larger datasets. |
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