Abstract Number:
2177
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Poster
Participants:
Salil Koner (1), Sheng Luo (1), Konstantin Arbeev (1), Igor Akushevich (1), Anatoliy Yashnin (1), Dhrubajyoti Ghosh (2)
Institutions:
(1) Duke University, N/A, (2) Washington University in St. Louis, N/A
Co-Author(s):
First Author:
Presenting Author:
Abstract Text:
The Long Life Family Study (LLFS) enrolled 5,089 individuals from 593 two-generation families, selected from the top 1% of the Family Longevity Selection Score. LLFS families, on average, exhibited superior aging outcomes, though with notable variation among pedigrees. The heritability of key healthy aging indicators, both short and long-term, underscores a genetic influence on protection against aging. This project introduces a robust methodology to analyze longitudinal changes in cognitive function, accounting for genetic relations, and other correlated biomarkers. We adopt a nonparametric hierarchical functional model to address the familial structure inherent in LLFS. Departing from conventional approaches that consider the time from baseline as the longitudinal indicator, this model utilizes age as the natural temporal variable, offering advantages in handling limited observations and facilitating the integration of diverse study data. This innovative approach enhances the understanding of cognitive aspects related to exceptional longevity within the LLFS cohort by pooling the shared information from the subjects in a family, even under less than three data per subject.
Keywords:
Hierarchical functional model|Long-life family study|Functional Principal Component analysis|Generalized additive model| |
Sponsors:
Biometrics Section
Tracks:
Longitudinal/Correlated Data
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