Biodemographic Approaches Can Improve Power of Genetic Analyses of Longitudinal Data on Aging, Health, and Longevity

Konstantin G. Arbeev, Duke University
Liubov Arbeeva, Duke University
Igor Akushevich, Duke University
Alexander Kulminski, Duke University
Deqing Wu, Duke University
Svetlana V. Ukraintseva, Duke University
Irina V. Culminskaya, Duke University

We discuss different approaches to work with rich data available in modern longitudinal studies of aging, health, and longevity that started collecting genetic information in addition to follow-up data on events and longitudinal measurements of biomarkers. Such methods provide a possibility to improve the power of genetic analyses by joint analysis of data for genotyped and non-genotyped sub-samples of the study. We describe results of simulation studies in the longitudinal genetic-demographic model illustrating that inclusion of information on ages at biospecimen collection in addition to follow-up data improves power in analyses of genetic effects on mortality/morbidity risks. We present simulation studies in the genetic stochastic process model illustrating the increase in power in joint analyses of genotyped and non-genotyped participants compared to analyses of non-genotyped participants alone in different scenarios testing relevant biologically-based hypotheses. We will illustrate applications of these approaches to analyses of genetic data in the Framingham Cohorts.