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

Rahul S. Desikan, Chun Chieh Fan, Yunpeng Wang, Andrew J. Schork, Howard J. Cabral, L. Adrienne Cupples, Wesley K. Thompson, Lilah Besser, Walter A. Kukull, Dominic Holland, Chi Hua Chen, James B. Brewer, David S. Karow, Karolina Kauppi, Aree Witoelar, Celeste M. Karch, Luke W. Bonham, Jennifer S. Yokoyama, Howard J. Rosen, Bruce L. MillerWilliam P. Dillon, David M. Wilson, Christopher P. Hess, Margaret Pericak-Vance, Jonathan L. Haines, Lindsay A. Farrer, Richard Mayeux, John Hardy, Alison M. Goate, Bradley T. Hyman, Gerard D. Schellenberg, Linda K. McEvoy, Ole A. Andreassen, Anders M. Dale

Research output: Contribution to journalArticlepeer-review

257 Scopus citations


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.

Original languageEnglish
Article numbere1002258
JournalPLoS medicine
Issue number3
StatePublished - Mar 2017


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