Genetic assessment of age-associated Alzheimer disease risk: Development and validation of a polygenic hazard score.
Genetic assessment of age-associated Alzheimer disease risk: Development and validation of a polygenic hazard score.
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DOI：
10.1371/journal.pmed.1002258
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发表时间：
2017-03
期刊：
影响因子：
15.8
通讯作者：
Dale AM
中科院分区：
文献类型：
--
作者：
Desikan RS;Fan CC;Wang Y;Schork AJ;Cabral HJ;Cupples LA;Thompson WK;Besser L;Kukull WA;Holland D;Chen CH;Brewer JB;Karow DS;Kauppi K;Witoelar A;Karch CM;Bonham LW;Yokoyama JS;Rosen HJ;Miller BL;Dillon WP;Wilson DM;Hess CP;Pericak-Vance M;Haines JL;Farrer LA;Mayeux R;Hardy J;Goate AM;Hyman BT;Schellenberg GD;McEvoy LK;Andreassen OA;Dale AM
识别有患阿尔茨海默病（AD）风险的个体至关重要。虽然遗传学研究已经在APOE和其他基因中发现了AD相关的SNP，但遗传信息尚未整合到流行病学框架中进行风险预测。使用来自国际阿尔茨海默病基因组学项目（IGAP第1阶段）的17，008例AD病例和37，154例对照的基因型数据，我们确定了AD相关的SNP（p < 10−5）。然后，我们将这些AD相关的SNP整合到一个考克斯比例风险模型中，该模型使用来自阿尔茨海默病遗传学联盟（ADGC）1期的6，409名AD患者和9，386名老年对照的基因型数据，为每位参与者提供多基因风险评分（PHS）。通过结合基于人群的发病率和每个个体的基因型推导的PHS，我们基于基因型和年龄推导出了发展AD的瞬时风险估计值，并在多个独立队列中测试了重复性（ADGC第2期，国家老龄化研究所阿尔茨海默病中心[NIA ADC]和阿尔茨海默病神经影像学倡议[ADNI]，总n = 20，680）。在ADGC I期队列中，PHS最高四分位数的个体在相当低的年龄发展为AD，并且具有最高的AD年发病率。在APOE ε3/3个体中，PHS将AD发病的预期年龄在最低和最高十分位数之间修正了10岁以上（风险比3.34，95% CI 2.62-4.24，p = 1.0 × 10−22）。在独立队列中，PHS强烈预测AD发病的经验年龄（ADGC第2阶段，r = 0.90，p = 1.1 × 10−26）和从正常老化到AD的纵向进展（NIA ADC，Cochran-Armitage趋势检验，p = 1.5 × 10−10），并与神经病理学相关（NIA ADC，神经元缠结的Braak分期，p = 3.9 × 10−6，以及建立阿尔茨海默病神经炎斑块评分登记的联盟，p = 6.8 × 10−6）和AD神经变性的体内标志物（ADNI，内嗅皮质体积损失，p = 6.3 × 10−6，海马体积损失，p = 7.9 × 10−5）。在临床使用前，有必要在非美国、非白人和基于社区的前瞻性队列中对这些结果进行额外的前瞻性验证。我们已经开发了一种PHS，用于量化AD年龄特异性遗传风险的个体差异。在这里研究的队列中，多基因结构在APOE以外的AD风险修饰中起着重要作用。通过彻底的验证，遗传变异的量化可能被证明是有用的分层AD的风险，并作为一种富集策略，在治疗试验。Rahul Desikan及其同事使用来自几个大型队列的遗传和流行病学数据来获得预测阿尔茨海默病发展的年龄特异性风险的评分。在美国，晚发性阿尔茨海默病（AD）是最常见的痴呆症。在治疗试验中，强烈需要用于AD风险分层和队列富集的体内标志物。尽管许多研究已经确定了几个遗传风险因素，包括载脂蛋白E（APOE）的ε4等位基因，但遗传变异尚未与遗传流行病学相结合，以量化AD发病年龄。利用来自70，000多名AD患者和正常老年对照的基因型数据，我们评估了将AD相关SNP和APOE状态结合为一个连续测量-多基因危险评分（PHS）-用于预测发展AD的年龄特异性风险的可行性。使用生存模型框架，我们将与AD风险增加相关的单核苷酸多态性整合到每位参与者的PHS中。通过结合基于人群的发病率和每个人的基因型推导的PHS，我们得出了基于基因型和年龄的AD瞬时风险的估计，并在两个独立的队列中进行了重复测试。个人在最高的PHS四分位数开发AD在相当低的年龄，并有最高的AD年发病率。在独立的队列中，我们发现PHS强烈预测AD发病的经验年龄和从正常衰老到AD的纵向进展，并与神经病理学和AD神经变性的体内标志物密切相关。在临床使用前，有必要对非美国、非白人和基于社区的前瞻性队列的这些结果进行额外的前瞻性验证。遗传变异可以整合在流行病学框架内，以获得多基因评分，该评分可以量化除APOE外的AD年龄特异性遗传风险的个体差异。遗传变异的量化可能被证明是有用的AD危险分层和治疗试验。
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. 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. 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. Rahul Desikan and colleagues use genetic and epidemiological data from several large cohorts to derive a score for predicting the age-specific risk for developing Alzheimer's disease. Across the United States, late-onset Alzheimer’s disease (AD) is the most common form of dementia. There is a strong need for in vivo markers for AD risk stratification and cohort enrichment in therapeutic trials. Although numerous studies have identified several genetic risk factors, including the ε4 allele of apolipoprotein E (APOE), genetic variants have not been integrated with genetic epidemiology for quantifying age of AD onset. Using genotype data from over 70,000 AD patients and normal elderly controls, we evaluated the feasibility of combining AD-associated SNPs and APOE status into a continuous measure—a polygenic hazard score (PHS)—for predicting the age-specific risk for developing AD. Using a survival model framework, we integrated single nucleotide polymorphisms associated with increased risk for AD into a 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 two independent cohorts. Individuals in the highest PHS quartile developed AD at a considerably lower age and had the highest yearly AD incidence rate. In independent cohorts, we found that the PHS strongly predicted empirical age of AD onset and longitudinal progression from normal aging to AD, and associated strongly with neuropathology and in vivo markers of AD neurodegeneration. Additional prospective validation of these results on non-US, non-white, and prospective community-based cohorts is necessary before clinical use. Genetic variants can be integrated within an epidemiology framework to derive a polygenic score that can quantify individual differences in age-specific genetic risk for AD, beyond APOE. Quantification of inherited genetic variation may prove useful for AD risk stratification and for therapeutic trials.