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

By Yann Le Guen, Michael E Belloy, Valerio Napolioni, Sarah J. Eger, Gabriel Kennedy, Ran Tao, Zihuai He, Michael D. Greicius, for the Alzheimer’s Disease Neuroimaging Initiative

Posted 06 Jan 2021
medRxiv DOI: 10.1101/2021.01.05.21249292

IntroductionMany Alzheimers disease (AD) genetic association studies disregard age or incorrectly account for it, hampering variant discovery. MethodUsing 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). ResultsModelling 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. DiscussionOur AD-age score provides a simple means for statistical power gain and is recommended for future AD studies.

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