Polygenic Scores of Alzheimer’s Disease Risk Genes Add Only Modestly to APOE in Explaining Variation in Amyloid PET Burden

Article type: Research Article

Authors: Ramanan, Vijay K.^{a; *} | Heckman, Michael G.^b | Przybelski, Scott A.^c | Lesnick, Timothy G.^c | Lowe, Val J.^d | Graff-Radford, Jonathan^a | Mielke, M.^{a; c} | Jack Jr., Clifford R.^d | Knopman, David S.^a | Petersen, Ronald C.^{a; c} | Ross, Owen A.^{e; f} | Vemuri, Prashanthi^{d; *} | for the Alzheimer’s Disease Neuroimaging Initiative¹

Affiliations: [a] Department of Neurology, Mayo Clinic-Minnesota, Rochester, MN, USA | [b] Department of Quantitative Health Sciences, Mayo Clinic-Florida, Jacksonville, FL, USA | [c] Department of Quantitative Health Sciences, Mayo Clinic-Minnesota, Rochester, MN, USA | [d] Department of Radiology, Mayo Clinic-Minnesota, Rochester, MN, USA | [e] Department of Neuroscience, Mayo Clinic-Florida, Jacksonville, FL, USA | [f] Department of Clinical Genomics, Mayo Clinic-Florida, Jacksonville, FL, USA

Correspondence: [*] Corresponding authors: Vijay K. Ramanan, MD, PhD, Department of Neurology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA. Tel.: +1 507 538 8437; E-mail: Ramanan.Vijay@mayo.edu and Prashanthi Vemuri, PhD, Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA. Tel.: +1 507 538 0761; E-mail: Vemuri.Prashanthi@mayo.edu.

Note: [1] Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) data-base. 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. Further information about the ADNI can be found at https://adni.loni.usc.edu/. A complete listing of ADNI investigators can be found at: https://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf

Abstract: Background:Brain accumulation of amyloid-β is a hallmark event in Alzheimer’s disease (AD) whose underlying mechanisms are incompletely understood. Case-control genome-wide association studies have implicated numerous genetic variants in risk of clinically diagnosed AD dementia. Objective:To test for associations between case-control AD risk variants and amyloid PET burden in older adults, and to assess whether a polygenic measure encompassing these factors would account for a large proportion of the unexplained variance in amyloid PET levels in the wider population. Methods:We analyzed data from the Mayo Clinic Study of Aging (MCSA) and the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Global cortical amyloid PET burden was the primary outcome. The 38 gene variants from Wightman et al. (2021) were analyzed as predictors, with PRSice-2 used to assess the collective phenotypic variance explained. Results:Known AD risk variants in APOE, PICALM, CR1, and CLU were associated with amyloid PET levels. In aggregate, the AD risk variants were strongly associated with amyloid PET levels in the MCSA (p = 1.51×10–50) and ADNI (p = 3.21×10–64). However, in both cohorts the non-APOE variants uniquely contributed only modestly (MCSA = 2.1%, ADNI = 4.4%) to explaining variation in amyloid PET levels. Conclusion:Additional case-control AD risk variants added only modestly to APOE in accounting for individual variation in amyloid PET burden, results which were consistent across independent cohorts with distinct recruitment strategies and subject characteristics. Our findings suggest that advancing precision medicine for dementia may require integration of strategies complementing case-control approaches, including biomarker-specific genetic associations, gene-by-environment interactions, and markers of disease progression and heterogeneity.

Keywords: Alzheimer’s disease, amyloid, Apolipoprotein E, polygenic risk scores, positron emission tomography

DOI: 10.3233/JAD-220164

Journal: Journal of Alzheimer's Disease, vol. 88, no. 4, pp. 1615-1625, 2022