Personalized genetic assessment of age associated Alzheimers disease risk
Rahul S. Desikan,
Chun Chieh Fan,
Andrew J Schork,
Howard J. Cabral,
L. Adrienne Cupples,
Wesley K. Thompson,
Walter A. Kukull,
James B. Brewer,
David S. Karow,
Celeste M. Karch,
Luke W. Bonham,
Jennifer S. Yokoyama,
Howard J. Rosen,
Bruce L. Miller,
William P. Dillon,
David M Wilson,
Christopher P. Hess,
Jonathan L. Haines,
Lindsay A. Farrer,
Alison M. Goate,
Bradley T. Hyman,
Gerard D. Schellenberg,
Linda K. McEvoy,
Ole A Andreassen,
Anders M. Dale,
for the ADNI and ADGC investigators
Posted 13 Sep 2016
bioRxiv DOI: 10.1101/074864 (published DOI: 10.1371/journal.pmed.1002258)
Posted 13 Sep 2016
Importance: Identifying individuals at risk for developing Alzheimers disease (AD) is of utmost importance. Although genetic studies have identified APOE and other AD associated single nucleotide polymorphisms (SNPs), genetic information has not been integrated into an epidemiological framework for personalized risk prediction. Objective: To develop, replicate and validate a novel polygenic hazard score for predicting age-specific risk for AD. Setting: Multi-center, multi-cohort genetic and clinical data. Participants: We assessed genetic data from 17,008 AD patients and 37,154 controls from the International Genetics of Alzheimers Project (IGAP), and 6,409 AD patients and 9,386 older controls from Phase 1 Alzheimers Disease Genetics Consortium (ADGC). As independent replication and validation cohorts, we also evaluated genetic, neuroimaging, neuropathologic, CSF and clinical data from ADGC Phase 2, National Institute of Aging Alzheimers Disease Center (NIA ADC) and Alzheimers Disease Neuroimaging Initiative (ADNI) (total n = 20,680) Main Outcome(s) and Measure(s): Use the IGAP cohort to first identify AD associated SNPs (at p < 10-5). Next, integrate these AD associated SNPs into a Cox proportional hazards model using ADGC phase 1 genetic data, providing a polygenic hazard score (PHS) for each participant. Combine population based incidence rates, and genotype-derived PHS for each individual to derive estimates of instantaneous risk for developing AD, based on genotype and age. Finally, assess replication and validation of PHS in independent cohorts. Results: Individuals in the highest PHS quantiles developed AD at a considerably lower age and had the highest yearly AD incidence rate. Among APOE 3/3 individuals, PHS modified expected age of AD onset by more than 10 years between the lowest and highest deciles. In independent cohorts, PHS strongly predicted empirical age of AD onset (p = 1.1 x 10-26), longitudinal progression from normal aging to AD (p = 1.54 x 10-10) and associated with markers of AD neurodegeneration. Conclusions: We developed, replicated and validated a clinically usable PHS for quantifying individual differences in age-specific risk of AD. Beyond APOE, polygenic architecture plays an important role in modifying AD risk. Precise quantification of AD genetic risk will be useful for early diagnosis and therapeutic strategies.
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