Polygenic scores and Mendelian randomization identify plasma proteins causally implicated in Alzheimer’s disease - Cammann, Davis B.; Lu, Yimei; Rotter, Jerome I.; Wood, Alexis C.; Chen, Jingchun (Frontiers in Neuroscience, 2024)
Background: An increasing body of evidence suggests that neuroinflammation is one of the key drivers of Late-Onset Alzheimer’s disease (LOAD) pathology. Due to increased permeability of the blood-brain barrier (BBB) in older age, peripheral plasma proteins can infiltrate the central nervous system (CNS) and drive neuroinflammation through interactions with neurons and glia cells. Because these inflammatory factors are heritable, a greater understanding of their genetic relationship with LOAD could identify new biomarkers that contribute to LOAD pathology or protection against it. Methods: We used a GWAS of 90 different plasma proteins (n=17,747) to create polygenic scores (PGSs) in an independent discovery (Cases=1,852, Controls=1,990) and replication (Cases=799, Controls=778) cohort. Multivariate logistic regression was used to associate the plasma protein PGSs with LOAD diagnosis while controlling for age, sex, principal components 1-2, and the number of APOE e4 alleles as covariates. After meta-analyzing the PGS-LOAD associations between the two cohorts, we next performed a two-sample Mendelian Randomization (MR) analysis using the summary statistics of significant plasma protein level PGSs in the meta-analysis as an exposure, and a GWAS of clinically-diagnosed LOAD (Cases=21,982, Controls=41,944) as an outcome to explore possible causal relationships between the two. Results: We identified 4 plasma protein level PGSs that were significantly associated (FDR adjusted P < 0.05) with LOAD in a meta-analysis of the discovery and replication cohorts: CX3CL1, HGF, TIE2, and MMP-3. When these 4 plasma proteins were used as exposures in MR with LOAD liability as the outcome, plasma levels of HGF were inferred to have a negative causal relationship with the disease when SNPs used as instrumental variables were not restricted to cis-variants (OR/95%CI=0.945/0.906-0.984, P=0.005). Conclusion: Our results show that plasma HGF has a negative causal relationship with LOAD liability that is driven by pleiotropic SNPs possibly involved in other pathways. These findings suggest a low transferability between PGS and MR approaches, and future research should explore ways in which LOAD and the plasma proteome may interact.