Exploring the Genetic Heterogeneity of Alzheimer’s Disease: Evidence for Genetic Subtypes

Article type: Research Article

Authors: Elman, Jeremy A.^{a; b; *} | Schork, Nicholas J.^{a; c} | Rangan, Aaditya V.^d | for the Alzheimer’s Disease Neuroimaging Initiative¹

Affiliations: [a] Department of Psychiatry, University of California San Diego, La Jolla, CA, USA | [b] Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA, USA | [c] The Translational Genomics Research Institute, Quantitative Medicine and Systems Biology, Phoenix, AZ, USA | [d] Department of Mathematics, New York University, New York, NY, USA

Correspondence: [*] Correspondence to: Jeremy A. Elman, PhD, UCSD Department of Psychiatry, 9500 Gilman Drive (MC 0738), La Jolla, CA 92093, USA. Tel.: +1 858 534 6842; Fax: +1 858 822 5856; E-mail: jaelman@health.ucsd.edu.

Note: [1] Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf

Abstract: Background:Alzheimer’s disease (AD) exhibits considerable phenotypic heterogeneity, suggesting the potential existence of subtypes. AD is under substantial genetic influence, thus identifying systematic variation in genetic risk may provide insights into disease origins. Objective:We investigated genetic heterogeneity in AD risk through a multi-step analysis. Methods:We performed principal component analysis (PCA) on AD-associated variants in the UK Biobank (AD cases = 2,739, controls = 5,478) to assess structured genetic heterogeneity. Subsequently, a biclustering algorithm searched for distinct disease-specific genetic signatures among subsets of cases. Replication tests were conducted using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset (AD cases = 500, controls = 470). We categorized a separate set of ADNI individuals with mild cognitive impairment (MCI; n = 399) into genetic subtypes and examined cognitive, amyloid, and tau trajectories. Results:PCA revealed three distinct clusters (“constellations”) driven primarily by different correlation patterns in a region of strong LD surrounding the MAPT locus. Constellations contained a mixture of cases and controls, reflecting disease-relevant but not disease-specific structure. We found two disease-specific biclusters among AD cases. Pathway analysis linked bicluster-associated variants to neuron morphogenesis and outgrowth. Disease-relevant and disease-specific structure replicated in ADNI, and bicluster 2 exhibited increased cerebrospinal fluid p-tau and cognitive decline over time. Conclusions:This study unveils a hierarchical structure of AD genetic risk. Disease-relevant constellations may represent haplotype structure that does not increase risk directly but may alter the relative importance of other genetic risk factors. Biclusters may represent distinct AD genetic subtypes. This structure is replicable and relates to differential pathological accumulation and cognitive decline over time.

Keywords: Alzheimer’s disease, biclustering, genetic risk, genetic subtypes, genotyping

DOI: 10.3233/JAD-231252

Journal: Journal of Alzheimer's Disease, vol. 100, no. 4, pp. 1209-1226, 2024