Dataset Information

Genetic Stratification to Identify Risk Groups for Alzheimer's Disease.

ABSTRACT: Stratification by genetic risk factors for Alzheimer's disease (AD) may help identify groups with the greatest disease risk. Biological changes that cause late-onset AD are likely to occur years, if not decades prior to diagnosis. Here, we select a subset of the Generation Scotland: Scottish Family Health Study cohort in a likely preclinical age-range of 60-70 years (subset n?=?3,495 with cognitive and genetic data). We test for cognitive differences by polygenic risk scores for AD. The polygenic scores are constructed using all available SNPs, excluding those within a 500?kb distance of the APOE locus. Additive and multiplicative effects of APOE status on these associations are investigated. Small memory decrements were observed in those with high polygenic risk scores for AD (standardized beta -0.04, p?=?0.020). These associations were independent of APOE status. There was no difference in AD polygenic scores across APOE haplotypes (p?=?0.72). Individuals with high compared to low polygenic risk scores for AD (top and bottom 5% of the distribution) show cognitive decrements, albeit much smaller than for APOE ?4?4 compared to ?3?3 individuals (2.3 versus 3.5 fewer points on the processing speed test, and 1.8 versus 2.8 fewer points on the memory test). Polygenic risk scores for AD may help identify older individuals at greatest risk of cognitive decline and preclinical AD.

SUBMITTER: Marioni RE

PROVIDER: S-EPMC5345653 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Genetic Stratification to Identify Risk Groups for Alzheimer's Disease.

Marioni Riccardo E RE Campbell Archie A Hagenaars Saskia P SP Nagy Reka R Amador Carmen C Hayward Caroline C Porteous David J DJ Visscher Peter M PM Deary Ian J IJ

Journal of Alzheimer's disease : JAD 20170101 1

Stratification by genetic risk factors for Alzheimer's disease (AD) may help identify groups with the greatest disease risk. Biological changes that cause late-onset AD are likely to occur years, if not decades prior to diagnosis. Here, we select a subset of the Generation Scotland: Scottish Family Health Study cohort in a likely preclinical age-range of 60-70 years (subset n = 3,495 with cognitive and genetic data). We test for cognitive differences by polygenic risk scores for AD. The polygeni ...[more]

PMID: 28222519

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Project description:Alzheimer's disease typically progresses in stages, which have been defined by the presence of disease-specific biomarkers: Amyloid (A), Tau (T) and neurodegeneration (N). This progression of biomarkers has been condensed into the ATN framework, where each of the biomarkers can be either positive (+) or negative (-). Over the past decades genome wide association studies have implicated about 90 different loci involved with the development of late onset Alzheimer's disease. Here we investigate whether genetic risk for Alzheimer's disease contributes equally to the progression in different disease stages or whether it exhibits a stage-dependent effect. Amyloid (A) and tau (T) status was defined using a combination of available PET and CSF biomarkers in the Alzheimer's Disease Neuroimaging Initiative cohort. In 312 participants with biomarker-confirmed A-T- status, we employed Cox proportional hazards models to estimate the contribution of APOE and polygenic risk scores (beyond APOE) to convert to A+T- status (65 conversions). Furthermore, we repeated the analysis in 290 participants with A+T- status and investigated the genetic contribution to conversion to A+T+ (45 conversions). Both survival analyses were adjusted for age, sex, and years of education. For progression from A-T- to A+T-, APOE-e4 burden showed significant effect (HR=2.88; 95% CI: 1.70-4.89; P<0.001), while polygenic risk did not (HR=1.09; 95% CI: 0.84-1.42; P=0.53). Conversely, for the transition from A+T- to A+T+, the APOE-e4 burden contribution was reduced (HR=1.62 95% CI: 1.05-2.51; P=0.031), while the polygenic risk showed an increased contribution (HR=1.73; 95% CI:1.27-2.36; P<0.001). The marginal APOE effect was driven by e4 homozygotes (HR=2.58; 95% CI: 1.05-6.35; P=0.039) as opposed to e4 heterozygotes (HR=1.74; 95% CI: 0.87-3.49; P=0.12). The genetic risk for late-onset Alzheimer's disease unfolds in a disease stage-dependent fashion. A better understanding of the interplay between disease stage and genetic risk can lead to a more mechanistic understanding of transition between ATN stages, a better understanding of the molecular processes leading to Alzheimer's disease as well as opening therapeutic windows for targeted interventions.

Project description:IntroductionStratification of Alzheimer's disease (AD) patients into risk subgroups using Polygenic Risk Scores (PRS) presents novel opportunities for the development of clinical trials and disease-modifying therapies. However, the heterogeneous nature of AD continues to pose significant challenges for the clinical broadscale use of PRS. PRS remains unfit in demonstrating sufficient accuracy in risk prediction, particularly for individuals with mild cognitive impairment (MCI), and in allowing feasible interpretation of specific genes or SNPs contributing to disease risk. We propose adORS, a novel oligogenic risk score for AD, to better predict risk of disease by using an optimized list of relevant genetic risk factors.MethodsUsing whole genome sequencing data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort (n = 1,545), we selected 20 genes that exhibited the strongest correlations with FDG-PET and AV45-PET, recognized neuroimaging biomarkers that detect functional brain changes in AD. This subset of genes was incorporated into adORS to assess, in comparison to PRS, the prediction accuracy of CN vs. AD classification and MCI conversion prediction, risk stratification of the ADNI cohort, and interpretability of the genetic information included in the scores.ResultsadORS improved AUC scores over PRS in both CN vs. AD classification and MCI conversion prediction. The oligogenic model also refined risk-based stratification, even without the assistance of APOE, thus reflecting the true prevalence rate of the ADNI cohort compared to PRS. Interpretation analysis shows that genes included in adORS, such as ATF6, EFCAB11, ING5, SIK3, and CD46, have been observed in similar neurodegenerative disorders and/or are supported by AD-related literature.DiscussionCompared to conventional PRS, adORS may prove to be a more appropriate choice of differentiating patients into high or low genetic risk of AD in clinical studies or settings. Additionally, the ability to interpret specific genetic information allows the focus to be shifted from general relative risk based on a given population to the information that adORS can provide for a single individual, thus permitting the possibility of personalized treatments for AD.

Dataset Information

Genetic Stratification to Identify Risk Groups for Alzheimer's Disease.

Publications

Genetic Stratification to Identify Risk Groups for Alzheimer's Disease.

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