A novel age-informed approach for genetic association analysis in Alzheimer’s disease

Abstract

Background: Many Alzheimer’s disease (AD) genetic association studies disregard age or incorrectly account for it, hampering variant discovery. Methods: 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: Modeling variable AD risk across age results in 5–10% 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. Conclusion: Our AD-age score provides a simple means for statistical power gain and is recommended for future AD studies.