Multi-class Modeling Identifies Shared Genetic Risk for Late-onset Epilepsy and Alzheimer’s Disease

Background Previous studies have established a strong link between late-onset epilepsy (LOE) and Alzheimer’s disease (AD). However, their shared genetic risk beyond the APOE gene remains unclear. Our study sought to examine the shared genetic factors of AD and LOE, interpret the biological pathways involved, and evaluate how AD onset may be mediated by LOE and shared genetic risks. Methods We defined phenotypes using phecodes mapped from diagnosis codes, with patients’ records aged 60-90. A two-step Least Absolute Shrinkage and Selection Operator (LASSO) workflow was used to identify shared genetic variants based on prior AD GWAS integrated with functional genomic data. We calculated an AD-LOE shared risk score and used it as a proxy in a causal mediation analysis. We used electronic health records from an academic health center (UCLA Health) for discovery analyses and validated our findings in a multi-institutional EHR database (All of Us). Results The two-step LASSO method identified 34 shared genetic loci between AD and LOE, including the APOE region. These loci were mapped to 65 genes, which showed enrichment in molecular functions and pathways such as tau protein binding and lipoprotein metabolism. Individuals with high predicted shared risk scores have a higher risk of developing AD, LOE, or both in their later life compared to those with low-risk scores. LOE partially mediates the effect of AD-LOE shared genetic risk on AD (15% proportion mediated on average). Validation results from All of Us were consistent with findings from the UCLA sample. Conclusions We employed a machine learning approach to identify shared genetic risks of AD and LOE. In addition to providing substantial evidence for the significant contribution of the APOE-TOMM40-APOC1 gene cluster to shared risk, we uncovered novel genes that may contribute. Our study is one of the first to utilize All of Us genetic data to investigate AD, and provides valuable insights into the potential common and disease-specific mechanisms underlying AD and LOE, which could have profound implications for the future of disease prevention and the development of targeted treatment strategies to combat the co-occurrence of these two diseases. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement MF and TSC was supported by the National Institutes of Health (NIH) National Institute of Aging (NIA) grant K08AG065519-01A1 and the Fineberg Foundation. KV was supported by NIH grants R01 NS033310, R01 AG058820, R01 AG075955, and R56 AG074473. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: This study was considered human subject research exempt because all genetic and EHRs were de-identified (UCLA IRB# 21-000435). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The datasets generated and/or analyzed during the current study are not publicly available because individual electronic health record data are not publicly available due to patient confidentiality and security concerns, but are available from the corresponding author (Timothy S Chang, timothychang@mednet.ucla.edu) on reasonable request. * ACME : Average Causal Mediation Effect AD : Alzheimer’s Disease ADE : Average Direct Effect APOE : Apolipoprotein E AUC : Area Under the Receiver Operating Characteristic Curve AUPRC : Area Under the Precision-Recall Curve CADD : Combined Annotation-Dependent Depletion CI : Confidence Interval EHR : Electronic Health Record eQTL : Expression Quantitative Trait Loci GIA : Genetic Inferred Ancestry GWAS : Genome-Wide Association Study ICD-10 : International Classification of Diseases, Tenth Revision LASSO : Least Absolute Shrinkage and Selection Operator LD : Linkage Disequilibrium LOE : Late-Onset Epilepsy MCC : Matthews Correlation Coefficient OR : Odds Ratio PC : Principal Components PCA : Principal Component Analysis PRS : Polygenic Risk Score QC : Quality Control SNP : Single Nucleotide Polymorphisms