Description: The conventional methods for genetic analyses of data on aging and longevity-related traits underutilize available information on aging, health and life span and treat the limited data set as the only source of information about the traits. We show that the power and biological relevance of GWAS can be enhanced by incorporating the biological and demographic principles of trait formation into the backbone of genetic analyses, through appropriate mathematical models and statistical functions, and by incorporating genetic questions into a comprehensive framework of dynamic analyses of longitudinal data. In this paper we use Framingham Heart Study data from the original cohort to show how genetic factors are associated with age trajectories of physiological variables and other biomarkers. The approach allows for incorporating hidden biomarkers of aging into the stochastic process model of aging and mortality and evaluate age their age patterns fr om the data for individuals with different genetic background.