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Meta-Analysis
. 2019 Feb 18;6(3):456-465.
doi: 10.1002/acn3.716. eCollection 2019 Mar.

Polygenic risk and hazard scores for Alzheimer's disease prediction

Ganna Leonenko  1 , Rebecca Sims  1 , Maryam Shoai  2 , Aura Frizzati  1 , Paola Bossù  3 , Gianfranco Spalletta  3 , Nick C Fox  2 , Julie Williams  1   4 ; GERAD consortium; John Hardy  2 , Valentina Escott-Price  1   4
Affiliations
Free PMC article
Meta-Analysis

Polygenic risk and hazard scores for Alzheimer's disease prediction

Ganna Leonenko et al. Ann Clin Transl Neurol. .
Free PMC article

Abstract

Objective: Genome-wide association studies (GWAS) have identified over 30 susceptibility loci associated with Alzheimer's disease (AD). Using AD GWAS data from the International Genomics of Alzheimer's Project (IGAP), Polygenic Risk Score (PRS) was successfully applied to predict life time risk of AD development. A recently introduced Polygenic Hazard Score (PHS) is able to quantify individuals with age-specific genetic risk for AD. The aim of this study was to quantify the age-specific genetic risk for AD with PRS and compare the results generated by PRS with those from PHS.

Methods: Quantification of individual differences in age-specific genetic risk for AD identified by the PRS, was performed with Cox Regression on 9903 (2626 cases and 7277 controls) individuals from the Genetic and Environmental Risk in Alzheimer's Disease consortium (GERAD). Polygenic Hazard Scores were generated for the same individuals. The age-specific genetic risk for AD identified by the PRS was compared with that generated by the PHS. This was repeated using varying SNPs P-value thresholds for disease association.

Results: Polygenic Risk Score significantly predicted the risk associated with age at AD onset when SNPs were preselected for association to AD at P ≤ 0.001. The strongest effect (B = 0.28, SE = 0.04, P = 2.5 × 10-12) was observed for PRS based upon genome-wide significant SNPs (P ≤ 5 × 10-8). The strength of association was weaker with less stringent SNP selection thresholds.

Interpretation: Both PRS and PHS can be used to predict an age-specific risk for developing AD. The PHS approach uses SNP effect sizes derived with the Cox Proportional Hazard Regression model. When SNPs were selected based upon AD GWAS case/control P ≤ 10-3, we found no advantage of using SNP effects sizes calculated with the Cox Proportional Hazard Regression model in our study. When SNPs are selected for association with AD risk at P > 10-3, the age-specific risk prediction results are not significant for either PRS or PHS. However PHS could be more advantageous than PRS of age specific AD risk predictions when SNPs are prioritized for association with AD age at onset (i.e., powerful Cox Regression GWAS study).

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Conflict of interest statement

JH‐grants from Cytox, outside the submitted work; JW‐patent for diagnostics on some SNPs identified as associated with AD; VE‐P – personal fees from Consultancy, outside the submitted work. GL, RS, MS, AF, PB, GS, and NF have nothing to report.

Figures

Figure 1
Figure 1
Histogram of age of AD cases and controls in the GERAD dataset.
Figure 2
Figure 2
Scatter plot of individual's PRS and PHS that were derived using 25 SNPs from Desikan et al.10 in the GERAD sample.
Figure 3
Figure 3
Survival curves for PHS and PRS scores + APOE ε4 and ε2 risk alleles for 8,415 individuals (2,384 cases and 6,031 controls) for whom APOE genotypes were available. Individuals are split into 5 groups based on 0–5%, 5–25%, 25–75%, 75–95%, and 95–100% of PHS/PRS distributions.
Figure 4
Figure 4
Results of age‐specific predictions using PRS and PHS analyses in GERAD subsamples of 20, 40, 60, 80, and 100% randomly selected individuals. The PHS and PRS are derived based upon 25 SNPs reported by Desikan et al.10
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