|Title||A novel age-informed approach for genetic association analysis in Alzheimer's disease.|
|Publication Type||Journal Article|
|Year of Publication||2021|
|Authors||Le Guen Y, Belloy ME, Napolioni V, Eger SJ, Kennedy G, Tao R, He Z, Greicius MD|
|Corporate Authors||Alzheimer’s Disease Neuroimaging Initiative|
|Journal||Alzheimers Res Ther|
|Date Published||2021 04 01|
|Keywords||Alzheimer Disease, Exome, Genetic Association Studies, Genetic Predisposition to Disease, Genetic Testing, Genome-Wide Association Study, Humans, Polymorphism, Single Nucleotide|
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.
|Alternate Journal||Alzheimers Res Ther|
|PubMed Central ID||PMC8017764|
|Grant List||U01 AG024904 / AG / NIA NIH HHS / United States |
P50 AG047366 / AG / NIA NIH HHS / United States
R01 AG066206 / AG / NIA NIH HHS / United States
AG060747 / NH / NIH HHS / United States