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. 2025 Sep;21(9):e70376.
doi: 10.1002/alz.70376.

Harmonizing genotype array data to understand genetic risk for brain amyloid burden in the AMYPAD PNHS Consortium

Emma S Luckett  1   2   3   4 , Yasmina Abakkouy  4 , Luigi Lorenzini  1   2 , Lyduine E Collij  1   2   5 , David Vallez Garcia  1   2   6 , Pieter Jelle Visser  7   8   9   10 , Anouk den Braber  7   8   11 , Craig Ritchie  12   13 , Mercè Boada  14   15 , Patricia Genius  6   16   17 , Natàlia Vilor-Tejedor  6   16   17   18 , Juan Domingo Gispert  6   15   16   19 , Rik Vandenberghe  3   20 , Frederik Barkhof  1   21 , Isabelle Cleynen  4 ; AMYPAD Consortium
Affiliations

Harmonizing genotype array data to understand genetic risk for brain amyloid burden in the AMYPAD PNHS Consortium

Emma S Luckett et al. Alzheimers Dement. 2025 Sep.

Abstract

Introduction: We sought to harmonize genotype data from the predementia AMYPAD (Amyloid Imaging to Prevent Alzheimer's Disease) Consortium, compute polygenic risk scores (PRS), and determine their association with global amyloid deposition.

Methods: Genetic data from five AMYPAD parent cohorts were harmonized, and PRS were computed for Alzheimer's disease (AD) susceptibility, cerebrospinal fluid (CSF) amyloid beta (Aβ)42, and CSF phosphorylated tau181. Cross-sectional amyloid (Centiloid [CL]) burden was available for all participants, and regression models determined if PRS were associated with CL burden.

Results: After harmonization, data for 867 participants showed that high CL burden was most strongly predicted by CSF Aβ42 PRS compared to traditional AD susceptibility PRS.

Discussion: This work emphasizes the importance of data harmonization and pooling of cohorts for large-powered studies. Findings suggest a genetic predisposition to amyloid pathology that may predispose individuals early in the AD continuum. This validates the potential use of PRS in clinical (trial) settings as a non-invasive tool to assess AD risk.

Highlights: We developed a robust harmonization pipeline for multi-cohort genotype array data. Cerebrospinal fluid amyloid beta (Aβ)-specific polygenic risk scores (PRS) more strongly predicted global Aβ positron emission tomography burden than other PRS. Results suggest a strong genetic predisposition to early Aβ pathology. This work highlights the need for robust data harmonization and data pooling. This work also validates the potential use of PRS as a non-invasive tool to assess Alzheimer's disease risk.

Keywords: Alzheimer's disease; Amyloid Imaging to Prevent Alzheimer's Disease; amyloid; genotype data harmonization; polygenic risk scores; predementia.

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Conflict of interest statement

This communication reflects the views of the authors and neither IMI nor the European Union and EFPIA are liable for any use that may be made of the information contained herein. Research of Alzheimer Center Amsterdam is part of the neurodegeneration research program of Amsterdam Neuroscience. Alzheimer Center Amsterdam is supported by Stichting Alzheimer Nederland and Stichting Steun Alzheimercentrum Amsterdam. EMIF‐Twins‐60+ is supported by the EU/EFPIA Innovative Medicines Initiative Joint Undertaking EMIF grant agreement no. 115372, Alzheimer Nederland and Stichting Dioraphte. P.J.V. is a coinventor on a patent of CSF proteomic subtypes (published under patent no. US2022196683A1, owner VUmc Foundation). C.R. is the founder, CEO, and majority shareholder of Scottish Brain Sciences; has received compensation for study‐related activities from AC Immune SA (paid to institution); has received consulting fees from Biogen, Eisai, MSD, Actinogen, Roche, Eli Lilly, and Novo Nordisk; has received payment or honoraria from Roche, Eisai, and Eli Lilly; and participates on a data safety monitoring board for Novo Nordisk. M.B. is an employee of the Ace Alzheimer Center and an advisory board member for Grifols, Roche, Eli Lilly, Araclon Biotech, Merck, Zambon, Biogen, Novo Nordisk, Bioiberica, Eisai, Servier, and Schwabe Pharma. J.D.G. has received research support from GE HealthCare, Roche Diagnostics, Hoffmann La Roche, and Life‐MI; has participated in symposia sponsored by Biogen, Philips Nederlands, Life‐MI, and Esteve; acted as a consultant for Roche Diagnostics; and served on the molecular neuroimaging advisory board of Prothena Biosciences. J.D.G. is founder, co‐owner, and member of the board of directors of Betascreen SL. J.D.G. is currently a full‐time employee of AstraZeneca. RV's institution has Clinical Research Agreements (RV as PI) with Alector, AviadoBio, Biogen, BMS, J&J, and UCB. R.V.’s institution has consultancy agreements (R.V. as DSMB chair or member) with AC Immune and Novartis. F.B. is a steering committee or data safety monitoring board member for Biogen, Merck, Eisai, and Prothena; advisory board member for Combinostics, Scottish Brain Sciences, and Alzheimer Europe; and consultant for Roche, Celltrion, Rewind Therapeutics, Merck, and Bracco. F.B. has research agreements with ADDI, Merck, Biogen, GE Healthcare, and Roche, and is co‐founder and shareholder of Queen Square Analytics LTD. E.S.L., Y.A., L.L., L.E.C., D.V.G., A.dB., M.B., P.G., N.VT., and I.C. have no disclosures. Author disclosures are available in the supporting information.

Figures

FIGURE 1
FIGURE 1
Principal component analysis plot including parent cohorts and European individuals minus the Fins from 1000G. Each cohort is represented by a different color with 1000G in gray. For visualization purposes, the AMYPAD PNHS data were projected onto the principal component analysis space from the 1000G dataset after post‐imputation QC was performed prior to the calculation of PRS. 1000G, 1000 Genomes Project; ALFA+, Alzheimer's and Families study; AMYPAD, Amyloid Imaging to Prevent Alzheimer's Disease consortium; EMIF‐AD 60++, European Medical Information Framework for Alzheimer's Disease 60++; EPAD LCS, European Prevention of Alzheimer's Disease Longitudinal Cohort Study; FACEHBI, Fundació ACE Healthy Brain Initiative; FPACK, Flemish Prevent AD Cohort KU Leuven; PNHS, Prognostic and Natural History Study; PRS, polygenic risk score; QC, quality control.
FIGURE 2
FIGURE 2
Representative PRS distributions for each AMYPAD PNHS Parent Cohort. All distributions show PRS at the genome‐wide significance threshold for SNP inclusion (pT = 5 × 10−8). The top row shows PRS including the APOE region for (A) PRSamyloid, (B) PRStau, and (C) PRSKunkle. The bottom row shows PRS excluding the APOE region for (D) PRSamyloid‐noAPOE, (E) PRStau‐noAPOE, and (F) PRSKunkle‐noAPOE. Note that a lower PRSamyloid(noAPOE) is indicative of higher genetic predisposition to lower levels of CSF Aβ42. Aβ, amyloid beta; ALFA+, Alzheimer's and Families study; AMYPAD, Amyloid Imaging to Prevent Alzheimer's Disease consortium; APOE, apolipoprotein E; CSF, cerebrospinal fluid; EMIF‐AD 60++, European Medical Information Framework for Alzheimer's Disease 60++; EPAD LCS, European Prevention of Alzheimer's Disease Longitudinal Cohort Study; FACEHBI, Fundació ACE Healthy Brain Initiative; FPACK, Flemish Prevent AD Cohort KU Leuven; PNHS, Prognostic and Natural History Study; PRS, polygenic risk score; p‐tau, phosphorylated tau; SNP, single nucleotide polymorphism.
FIGURE 3
FIGURE 3
PRS SNP set size. A log scale is used on the y axis to enable all set sizes to be visualized given the wide range. The table on the right shows the actual SNP set size for each PRS. APOE, apolipoprotein E; CSF, cerebrospinal fluid; PRS, polygenic risk score; SNP, single nucleotide polymorphism.
FIGURE 4
FIGURE 4
PRS distributions across phenotypes and P value thresholds. Each row represents PRS computed using a different set of summary statistics at the three thresholds for SNP inclusion. Scores with and without the APOE region are shown. Data points are colored based on the number of APOE ε4 alleles a participant carries, where darker colors represent the presence of more risk alleles. Note that a lower PRSamyloid(noAPOE) is indicative of higher genetic predisposition to lower levels of CSF Aβ42. Aβ, amyloid beta; APOE, apolipoprotein E; CSF, cerebrospinal fluid; PRS, polygenic risk score; SNP, single nucleotide polymorphism.
FIGURE 5
FIGURE 5
Correlation matrix illustrating the correlation coefficient for each pair of PRS. The color of the circles is based on the size of the correlation coefficient, and the size of the circles on the P value significance. Non‐significant correlations are shown as blank circles. Note that a lower PRSamyloid(noAPOE) is indicative of higher genetic predisposition to lower levels of CSF Aβ42. Aβ, amyloid beta; CSF, cerebrospinal fluid; PRS, polygenic risk score.
FIGURE 6
FIGURE 6
Association between PRS and global amyloid burden. The forest plots illustrate the standardized betas and confidence intervals from the primary linear regression models, with corresponding Bonferroni‐corrected P values. Each panel is an individual parent cohort, with the bottom right panel being the harmonized AMYPAD PNHS cohort. * = P < 0.05; ** = P < 0.005; *** = P < 0.001. Note that a lower PRSamyloid(noAPOE) is indicative of higher genetic predisposition to lower levels of CSF Aβ42. Aβ, amyloid beta; ALFA+, Alzheimer's and Families study; AMYPAD, Amyloid Imaging to Prevent Alzheimer's Disease consortium; CSF, cerebrospinal fluid; EMIF‐AD 60++, European Medical Information Framework for Alzheimer's Disease 60++; EPAD LCS, European Prevention of Alzheimer's Disease Longitudinal Cohort Study; FACEHBI, Fundació ACE Healthy Brain Initiative; FPACK, Flemish Prevent AD Cohort KU Leuven; PNHS, Prognostic and Natural History Study; PRS, polygenic risk score.
FIGURE 7
FIGURE 7
Effect of high, medium, and low PRS risk on high amyloid burden. The forest plot illustrates the odds ratios and confidence intervals from the logistic regression models, with corresponding Bonferroni‐corrected P values. The dashed line at OR = 1 indicates no effect. High amyloid burden was defined as CL > 30. * = P < 0.05; ** = P < 0.005; *** = P < 0.001. CL, Centiloid; CSF, cerebrospinal fluid; OR, odds ratio; PRS, polygenic risk score; ptau, phosphorylated tau.
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