Introduction Many Alzheimer's disease (AD) genetic association studies disregard age or incorrectly account for it, hampering variant discovery. Method Using simulated data, we compared the statistical power of several models: logistic regression on AD diagnosis adjusted and not adjusted for age; linear regression on a score integrating case-control status and age; and multivariate Cox regression on age-at-onset. We applied these models to real exome-wide data of 11,127 sequenced individuals (54% cases) and replicated suggestive associations in 21,631 genotype-imputed individuals (51% cases). Results Modelling variable AD risk across age results in 10-20% statistical power gain compared to logistic regression without age adjustment, while incorrect age adjustment leads to critical power loss. Applying our novel AD-age score and/or Cox regression, we discovered and replicated novel variants associated with AD on KIF21B, USH2A, RAB10, RIN3 and TAOK2 genes. Discussion Our AD-age score provides a simple means for statistical power gain and is recommended for future AD studies.

中文翻译：

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老年痴呆症遗传关联分析的新方法

简介许多阿尔茨海默氏病（AD）遗传协会研究无视年龄或错误地解释了年龄，从而妨碍了变体的发现。方法使用模拟数据，我们比较了几种模型的统计能力：校正后和未校正年龄的AD诊断的logistic回归；结合病例对照状态和年龄的得分进行线性回归；以及发病年龄的多元Cox回归。我们将这些模型应用于11127个测序个体的实际全基因组数据（54％病例），并在21631个基因型影响个体（51％病例）中复制了暗示性关联。结果与不进行年龄调整的逻辑回归相比，对跨年龄的可变AD风险建模可产生10-20％的统计功效，而错误的年龄调整会导致严重的能量损失。应用我们的新型AD年龄评分和/或Cox回归，我们发现并复制了KIF21B，USH2A，RAB10，RIN3和TAOK2基因上与AD相关的新型变异。讨论我们的AD年龄评分为统计功率增益提供了一种简单的方法，建议将来进行AD研究。