Shared Genetic Background Between Cerebrospinal Fluid Biomarkers and Risk for Alzheimer's Disease: A Two-Sample
Soyeon Kim1, Kiwon Kim2, Kwangsik Nho3
1Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea.
Background:
Whether the epidemiological association of amyloid-β (Aβ) and tau pathology in late-onset Alzheimer's disease (LOAD) is causal remains unclear.
Objective:
We aimed to investigate the shared genetic background between the cerebrospinal fluid (CSF) biomarkers for Aβ and tau pathology and the risk of LOAD.
Methods:
We conducted a two-sample Mendelian randomization (MR) analysis. We used summary statistics of genome-wide association studies for CSF biomarkers (Aβ1-42 [Aβ], phosphorylated tau181 [p-tau], and total tau [t-tau]) in 3,146 individuals and for LOAD in 21,982 cases and 41,944 controls. We tested the association between changes in the genetically predicted CSF biomarkers and LOAD risk.
Results:
We found a decrease in LOAD risk per one-standard-deviation (SD) increase in the genetically predicted CSF Aβ (odds ratio [OR], 2.87×10-3 for AD; 95%confidence interval [CI], 1.54×10-4-0.05; p = 8.91×10-5). Conversely, we observed an increase in LOAD risk per one-SD increase in the genetically predicted CSF p-tau (OR, 19.46; 95%CI, 1.50-2.52×102; p = 0.02) and t-tau (OR, 33.80; 95%CI, 1.57-7.29×102; p = 0.02). However, only the association between p-tau and the risk for LOAD remained significant after the exclusion of the APOE variant (rs769449).
Conclusion:
We found the causal association between CSF biomarkers and the risk for LOAD. Our results suggest that the etiology of LOAD involves multiple biological processes, including the pathways of Aβ and tau proteins. Further MR studies using large-scale data of multiple candidate biomarkers are needed to elucidate the pathophysiology of LOAD.
More Related Videos
07:26High-resolution Melting PCR for Complement Receptor 1 Length Polymorphism Genotyping: An Innovative Tool for Alzheimer's Disease Gene Susceptibility Assessment
Published on: July 18, 2017
04:41Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration
Published on: January 9, 2020
