Genetic risk factor clustering within and across neurodegenerative diseases

Overlapping symptoms and copathologies are common in closely related neurodegenerative diseases (NDDs). Investigating genetic risk variants across these NDDs can give further insight into disease manifestations. In this study we have leveraged genome-wide single nucleotide polymorphisms (SNPs) and genome-wide association study (GWAS) summary statistics to cluster patients based on their genetic status across identified risk variants for five NDDs (Alzheimer's disease [AD], Parkinson's disease [PD], amyotrophic lateral sclerosis [ALS], Lewy body dementia [LBD], and frontotemporal dementia [FTD]). The multi-disease and disease-specific clustering results presented here provide evidence that NDDs have more overlapping genetic etiology than previously expected and how neurodegeneration should be viewed as a spectrum of symptomology. These clustering analyses also show potential subsets of patients with these diseases that are significantly depleted for any known common genetic risk factors suggesting environmental or other factors at work. Establishing that NDDs with overlapping pathologies share genetic risk loci, future research into how these variants might have different effects on downstream protein expression, pathology and NDD manifestation in general is important for refining and treating NDDs. ### Competing Interest Statement C.A., K.L., H.L., H.I., D.V., F.F. and M.N.'s participation in this project was part of a competitive contract awarded to Data Tecnica International LLC by the National Institutes of Health to support open science research. M.N. also currently serves on the scientific advisory board for Clover Therapeutics and is an advisor to Neuron23 Inc. ### Funding Statement No funding was received towards this work. ### 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: Data used in the preparation of this article were obtained from the AMP-PD Knowledge Platform. For up-to-date information on the study, visit https://www.amp-pd.org. The results published here are in whole or in part based on data obtained from the AD Knowledge Portal (https://adknowledgeportal.org). 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 and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes All samples for this analysis were obtained from public domain WGS cohorts. A repository containing all code for processing and analysis is publicly available to facilitate replication (https://github.com/NIH-CARD/NDD\_risk\_variant_clustering). In addition, an interactive website has been developed where researchers can further explore the described cluster memberships and results (https://nih-card-ndd-risk-variant-clustering-app-25rr5g.streamlitapp.com/).