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. 2023;95(3):915-929.
doi: 10.3233/JAD-230548.

Pathway-Specific Polygenic Risk Scores Correlate with Clinical Status and Alzheimer's Disease-Related Biomarkers

Nicholas J Schork  1   2 , Jeremy A Elman  2   3 ; Alzheimer’s Disease Neuroimaging Initiative
Affiliations

Pathway-Specific Polygenic Risk Scores Correlate with Clinical Status and Alzheimer's Disease-Related Biomarkers

Nicholas J Schork et al. J Alzheimers Dis. 2023.

Abstract

Background: APOE is the largest genetic risk factor for Alzheimer's disease (AD), but there is a substantial polygenic component. Polygenic risk scores (PRS) can summarize small effects across the genome but may obscure differential risk across molecular processes and pathways that contribute to heterogeneity of disease presentation.

Objective: We examined polygenic risk impacting specific AD-associated pathways and its relationship with clinical status and biomarkers of amyloid, tau, and neurodegeneration (A/T/N).

Methods: We analyzed data from 1,411 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI). We applied pathway analysis and clustering to identify AD-associated "pathway clusters" and construct pathway-specific PRSs (excluding the APOE region). We tested associations with diagnostic status, abnormal levels of amyloid and ptau, and hippocampal volume.

Results: Thirteen pathway clusters were identified, and eight pathway-specific PRSs were significantly associated with AD diagnosis. Amyloid-positivity was associated with endocytosis and fibril formation, response misfolded protein, and regulation protein tyrosine PRSs. Ptau positivity and hippocampal volume were both related to protein localization and mitophagy PRS, and ptau-positivity was also associated with an immune signaling PRS. A global AD PRS showed stronger associations with diagnosis and all biomarkers compared to pathway PRSs.

Conclusions: Pathway PRS may contribute to understanding separable disease processes, but do not add significant power for predictive purposes. These findings demonstrate that AD-phenotypes may be preferentially associated with risk in specific pathways, and defining genetic risk along multiple dimensions may clarify etiological heterogeneity in AD. This approach to delineate pathway-specific PRS can be used to study other complex diseases.

Keywords: Alzheimer’s disease; amyloid; dementia; genetic risk score; hippocampal volume; tau.

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Conflict of interest statement

Conflict of Interest

The authors have no conflict of interest to report.

Figures

Figure 1.
Figure 1.. Contributions of genes and SNPs to one or more pathways.
A) The frequency with which genes were members of one or more pathway clusters for genes that were mapped to at least one pathway. B) The frequency with which SNPs contributed to the calculation of one or more pathway-PRS for SNPs that were mapped to at least one pathway. C) Correlation matrix of global and pathway-specific PRS.
Figure 2.
Figure 2.. Associations of polygenic risk scores with diagnostic status.
Logistic regressions were used with diagnostic status (cognitively unimpaired vs dementia) as the outcome. Separate models used each PRS as predictor. Models either 1) included number of APOE-ε4 alleles as a separate variable, 2) tested only APOE-ε4 non-carriers, or 3) tested only APOE-ε4 carriers. All models adjusted for age, gender, and the first 3 genetic principal components. Plots show standardized regression coefficients (log-odds) and standard errors. Associations that survived FDR correction are bolded.
Figure 3.
Figure 3.. Associations of polygenic risk scores with amyloid positivity.
Logistic regressions were used with amyloid status (positive vs negative) as the outcome. Separate models used each PRS as predictor. Models either 1) included number of APOE-ε4 alleles as a separate variable, 2) tested only APOE-ε4 non-carriers, or 3) tested only APOE-ε4 carriers. All models adjusted for age, gender, and the first 3 genetic principal components. Plots show standardized regression coefficients (log-odds) and standard errors. Associations that survived FDR correction are bolded.
Figure 4.
Figure 4.. Associations of polygenic risk scores with ptau positivity.
Logistic regressions were used with ptau status (positive vs negative) as the outcome. Separate models used each PRS as predictor. Models either 1) included number of APOE-ε4 alleles as a separate variable, 2) tested only APOE-ε4 non-carriers, or 3) tested only APOE-ε4 carriers. All models adjusted for age, gender, and the first 3 genetic principal components. Plots show standardized regression coefficients (log-odds) and standard errors. Associations that survived FDR correction are bolded.
Figure 5.
Figure 5.. Associations of polygenic risk scores with hippocampal volume.
Linear regressions were used with hippocampal volume normalized by intracranial volume as the outcome. Separate models used each PRS as predictor. Models either 1) included number of APOE-ε4 alleles as a separate variable, 2) tested only APOE-ε4 non-carriers, or 3) tested only APOE-ε4 carriers. All models adjusted for age, gender, scanner field strength and the first 3 genetic principal components. Plots show standardized regression coefficients and standard errors. Associations that survived FDR correction are bolded.
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