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NG00112 - A novel age-informed approach for genetic association analysis in Alzheimer’s disease summary statistics - Guen et al. 2021


Using a cohort of 11,127 European-ancestry subjects with whole exome data (54% Alzheimer's Disease (AD) cases), a novel method of accounting for the effect of age on AD was tested in conjunction with more common methodologies. The goal of these analyses was to test a novel scoring method of integrating case-control status and age to better account for age-related AD changes in AD genetic studies. This novel AD-age score accounts for age by separating cases and controls by age in such a way that younger cases have higher scores than older cases, and older controls have lower scores than younger controls. This scoring methodology assumes that younger cases and older controls influence genetic associations more than older cases and younger controls, whose diagnostic status is more age-associated. Our analyses demonstrated that the AD-age score is a simple method to increase statistical power in AD genetic studies when compared to the more standard adjustment methods.

Mega-analyses testing the differing effect of age on Alzheimer’s Disease (AD) depending on four model type:

  1. Logistic regression on AD diagnosis adjusted for age
  2. Logistic regression on AD diagnosis
  3. Linear regression on a score integrating case-control status and age
  4. Multivariate Cox regression on the age of onset

The primary dataset used in this project was made up of the ADSP WES, ADSP WGS, and the ADMP-AD cohorts and acted as our discovery sample for the four models. After restricting to non-related subjects with a preference to keep cases over controls, the total dataset was made up of 5075 controls (66% APOE3/3) and 6052 cases (48% APOE3/3), totaling 11,127 subjects.

The p-value data is generally available to all users using the link below; however, gaining access to the complete dataset requires a formal data request.

Summary Statistics p-values only

Molecular Data Type


PI Information