Genetic Model for Longitudinal Studies of Aging, Health and Longevity and Its Application to Framingham Heart Study Data

Konstantin G. Arbeev, Duke University
Liubov Arbeeva, Duke University
Svetlana V. Ukraintseva, Duke University
Irina V. Culminskaya, Duke University

We present a stochastic model for studying longitudinal data of aging that contain genetic information for a sub-sample of participants of the study. It is based on a joint analysis of genetic and non-genetic sub-samples that results in a substantial increase in the accuracy of estimates compared to the use of information from a genetic sub-sample alone. The model includes several major concepts of aging known to date (age-specific physiological norms, allostasis and allostatic load, stochasticity, decline in stress-resistance and adaptive capacity) and allows for estimating all these concepts together, even if such mechanisms are not directly measured in data, which is typical for longitudinal data available to date. The model permits evaluation of all these characteristics for carriers of different alleles (genotypes) to address questions concerning genetic influence on these aging-related characteristics. Applications of the model to the Framingham Heart Study data are discussed.

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Presented in Poster Session 7