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. 2017 Mar 21;14(3):e1002258.
doi: 10.1371/journal.pmed.1002258. eCollection 2017 Mar.

Genetic assessment of age-associated Alzheimer disease risk: Development and validation of a polygenic hazard score

Rahul S Desikan  1 , Chun Chieh Fan  2 , Yunpeng Wang  3   4   5 , Andrew J Schork  2 , Howard J Cabral  6 , L Adrienne Cupples  6 , Wesley K Thompson  7 , Lilah Besser  8 , Walter A Kukull  8 , Dominic Holland  3 , Chi-Hua Chen  9 , James B Brewer  3   9   10 , David S Karow  9 , Karolina Kauppi  9 , Aree Witoelar  4   5 , Celeste M Karch  11 , Luke W Bonham  12 , Jennifer S Yokoyama  12 , Howard J Rosen  12 , Bruce L Miller  12 , William P Dillon  1 , David M Wilson  1 , Christopher P Hess  1 , Margaret Pericak-Vance  13 , Jonathan L Haines  14   15 , Lindsay A Farrer  16   17   18   6   19 , Richard Mayeux  20   21   22 , John Hardy  23 , Alison M Goate  24   25 , Bradley T Hyman  26 , Gerard D Schellenberg  27 , Linda K McEvoy  9 , Ole A Andreassen  4   5 , Anders M Dale  2   3   9
Affiliations

Genetic assessment of age-associated Alzheimer disease risk: Development and validation of a polygenic hazard score

Rahul S Desikan et al. PLoS Med. .

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Abstract

Background: Identifying individuals at risk for developing Alzheimer disease (AD) is of utmost importance. Although genetic studies have identified AD-associated SNPs in APOE and other genes, genetic information has not been integrated into an epidemiological framework for risk prediction.

Methods and findings: Using genotype data from 17,008 AD cases and 37,154 controls from the International Genomics of Alzheimer's Project (IGAP Stage 1), we identified AD-associated SNPs (at p < 10-5). We then integrated these AD-associated SNPs into a Cox proportional hazard model using genotype data from a subset of 6,409 AD patients and 9,386 older controls from Phase 1 of the Alzheimer's Disease Genetics Consortium (ADGC), providing a polygenic hazard score (PHS) for each participant. By combining population-based incidence rates and the genotype-derived PHS for each individual, we derived estimates of instantaneous risk for developing AD, based on genotype and age, and tested replication in multiple independent cohorts (ADGC Phase 2, National Institute on Aging Alzheimer's Disease Center [NIA ADC], and Alzheimer's Disease Neuroimaging Initiative [ADNI], total n = 20,680). Within the ADGC Phase 1 cohort, individuals in the highest PHS quartile developed AD at a considerably lower age and had the highest yearly AD incidence rate. Among APOE ε3/3 individuals, the PHS modified expected age of AD onset by more than 10 y between the lowest and highest deciles (hazard ratio 3.34, 95% CI 2.62-4.24, p = 1.0 × 10-22). In independent cohorts, the PHS strongly predicted empirical age of AD onset (ADGC Phase 2, r = 0.90, p = 1.1 × 10-26) and longitudinal progression from normal aging to AD (NIA ADC, Cochran-Armitage trend test, p = 1.5 × 10-10), and was associated with neuropathology (NIA ADC, Braak stage of neurofibrillary tangles, p = 3.9 × 10-6, and Consortium to Establish a Registry for Alzheimer's Disease score for neuritic plaques, p = 6.8 × 10-6) and in vivo markers of AD neurodegeneration (ADNI, volume loss within the entorhinal cortex, p = 6.3 × 10-6, and hippocampus, p = 7.9 × 10-5). Additional prospective validation of these results in non-US, non-white, and prospective community-based cohorts is necessary before clinical use.

Conclusions: We have developed a PHS for quantifying individual differences in age-specific genetic risk for AD. Within the cohorts studied here, polygenic architecture plays an important role in modifying AD risk beyond APOE. With thorough validation, quantification of inherited genetic variation may prove useful for stratifying AD risk and as an enrichment strategy in therapeutic trials.

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Conflict of interest statement

I have read the journal's policy and the authors of this manuscript have the following competing interests: JBB served on advisory boards for Elan, Bristol-Myers Squibb, Avanir, Novartis, Genentech, and Eli Lilly and holds stock options in CorTechs Labs, Inc. and Human Longevity, Inc. AMD is a founder of and holds equity in CorTechs Labs, Inc., and serves on its Scientific Advisory Board. He is also a member of the Scientific Advisory Board of Human Longevity, Inc. (HLI), and receives research funding from General Electric Healthcare (GEHC). The terms of these arrangements have been reviewed and approved by the University of California, San Diego in accordance with its conflict of interest policies. AG served on or have served on in the last 3 years the scientific advisory boards of the following companies: Denali Therapeutics, Cognition Therapeutics and AbbVie. BM served as guest editor on PLOS Medicine’s Special Issue on Dementia.

Figures

Fig 1
Fig 1. Kaplan–Meier estimates and Cox proportional hazard model fits from the ADGC Phase 1 case–control dataset, excluding NIA ADC and ADNI samples.
The proportional hazard assumptions were checked based on graphical comparisons between Kaplan–Meier estimations (dashed lines) and Cox proportional hazard models (solid lines). The 95% confidence intervals of Kaplan–Meier estimations are also demonstrated (shaded with corresponding colors). The baseline hazard (gray line) in this model is based on the mean of ADGC data. ADGC, Alzheimer’s Disease Genetics Consortium; ADNI, Alzheimer’s Disease Neuroimaging Initiative; NIA ADC, National Institute on Aging Alzheimer’s Disease Center; PHS, polygenic hazard score.
Fig 2
Fig 2. Kaplan–Meier estimates and Cox proportional hazard model fits among APOE ε3/3 individuals in the ADGC Phase 1 dataset, excluding NIA ADC and ADNI samples.
The solid lines represent the Cox fit, whereas the dashed lines and shaded regions represent the Kaplan–Meier estimations with 95% confidence intervals. ADGC, Alzheimer’s Disease Genetics Consortium; ADNI, Alzheimer’s Disease Neuroimaging Initiative; NIA ADC, National Institute on Aging Alzheimer’s Disease Center; PHS, polygenic hazard score.
Fig 3
Fig 3. Polygenic hazard score validation in ADGC Phase 2 cohort.
(A) Risk stratification in ADGC Phase 2 cohort, using PHSs derived from ADGC Phase 1 dataset. The dashed lines and shaded regions represent Kaplan–Meier estimations with 95% confidence intervals. (B) Predicted age of AD onset as a function of empirical age of AD onset among cases in ADGC Phase 2 cohort. Prediction is based on the final survival model trained in the ADGC Phase 1 dataset. AD, Alzheimer disease; ADGC, Alzheimer’s Disease Genetics Consortium; PHS, polygenic hazard score.
Fig 4
Fig 4. Annualized incidence rates showing the instantaneous hazard as a function of polygenic hazard score percentile and age.
The gray line represents the population baseline estimate. Dashed lines represent incidence rates in APOE ε4 carriers (dark red dashed line) and non-carriers (light blue dashed line) not associated with a PHS percentile. The asterisk indicates that the baseline estimation is based on previously reported annualized incidence rates by age in the general US population [18]. PHS, polygenic hazard score.
Fig 5
Fig 5. Empirical progression rates observed in the NIA ADC longitudinal cohort as a function of predicted incidence.
Bars show 95% confidence intervals. NIA ADC, National Institute on Aging Alzheimer’s Disease Center.
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