Dementia Research Institute, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Hadyn Ellis Building, Maindy Rd, Cardiff, CF24 4HQ, UK.
School of Mathematics, Cardiff University, Senghennydd Road, Cardiff, CF24 4AG, UK.
Alzheimers Res Ther. 2021 Aug 17;13(1):140. doi: 10.1186/s13195-021-00884-7.
Alzheimer's disease, among other neurodegenerative disorders, spans decades in individuals' life and exhibits complex progression, symptoms and pathophysiology. Early diagnosis is essential for disease prevention and therapeutic intervention. Genetics may help identify individuals at high risk. As thousands of genetic variants may contribute to the genetic risk of Alzheimer's disease, the polygenic risk score (PRS) approach has been shown to be useful for disease risk prediction. The APOE-ε4 allele is a known common variant associated with high risk to AD, but also associated with earlier onset. Rare variants usually have higher effect sizes than common ones; their impact may not be well captured by the PRS. Instead of standardised PRS, we propose to calculate the disease probability as a measure of disease risk that allows comparison between individuals.
We estimate AD risk as a probability based on PRS and separately accounting for APOE, AD rare variants and the disease prevalence in age groups. The mathematical framework makes use of genetic variants effect sizes from summary statistics and AD disease prevalence in age groups.
The AD probability varies with respect to age, APOE status and presence of rare variants. In age group 65+, the probability of AD grows from 0.03 to 0.18 (without APOE) and 0.07 to 0.7 (APOE e4e4 carriers) as PRS increases. In 85+, these values are 0.08-0.6 and 0.3-0.85. Presence of rare mutations, e.g. in TREM2, may increase the probability (in 65+) from 0.02 at the negative tail of the PRS to 0.3.
Our approach accounts for the varying disease prevalence in different genotype and age groups when modelling the APOE and rare genetic variants risk in addition to PRS. This approach has potential for use in a clinical setting and can easily be updated for novel rare variants and for other populations or confounding factors when appropriate genome-wide association data become available.
阿尔茨海默病（Alzheimer's disease，AD）和其他神经退行性疾病一样，在个体的一生中跨越了几十年，其表现出复杂的进展、症状和病理生理学特征。早期诊断对于疾病的预防和治疗干预至关重要。遗传学可能有助于识别高风险个体。由于可能有成千上万的遗传变异导致阿尔茨海默病的遗传风险，多基因风险评分（PRS）方法已被证明可用于疾病风险预测。APOE-ε4 等位基因是一种已知的与 AD 高风险相关的常见变体，但也与发病年龄较早相关。罕见变异通常比常见变异具有更高的效应大小；它们的影响可能无法通过 PRS 很好地捕捉到。我们提出计算疾病概率作为疾病风险的衡量标准，而不是标准的 PRS，以允许个体之间进行比较。
我们根据 PRS 并分别考虑 APOE、AD 罕见变异和年龄组中的疾病流行率来估计 AD 风险作为概率。该数学框架利用了来自汇总统计数据的遗传变异效应大小和年龄组中的 AD 疾病流行率。
AD 概率会随着年龄、APOE 状态和罕见变异的存在而变化。在 65 岁以上的年龄组中，随着 PRS 的增加，AD 的概率从 0.03 增加到 0.18（不考虑 APOE）和从 0.07 增加到 0.7（APOE e4e4 携带者）。在 85 岁以上，这些值分别为 0.08-0.6 和 0.3-0.85。罕见突变的存在，例如在 TREM2 中，可能会增加概率（在 PRS 的负尾端从 0.02 增加到 0.3）。
我们的方法在建模 APOE 和罕见遗传变异风险时，考虑了不同基因型和年龄组中疾病流行率的变化，除了 PRS 之外。这种方法具有在临床环境中使用的潜力，并且可以轻松更新新的罕见变体以及其他人群或混杂因素，当适当的全基因组关联数据可用时。
