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

Konstantin G. Arbeev () and Anatoliy I. Yashin ()
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Konstantin G. Arbeev: Duke Population Research Institute & Social Science Research Institute at Duke University, Biodemography of Aging Research Unit, Center for Population Health and Aging
Anatoliy I. Yashin: Duke Population Research Institute & Social Science Research Institute at Duke University, Biodemography of Aging Research Unit, Center for Population Health and Aging

Chapter Chapter 14 in Biodemography of Aging, 2016, pp 303-319 from Springer

Abstract: Abstract The modern era of revolutionary advances in genetics provides great opportunities and challenges for the field of biodemography. We discuss approaches to work with the rich data available in modern longitudinal studies of aging, health, and longevity that have collected genetic information in addition to follow-up data on events and longitudinal measurements of biomarkers. Such methods provide a possibility for improving 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 a 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 also present the version of the genetic stochastic process model (SPM) modified to include the dependence of the model’s components on a vector of observed time-independent covariates available at baseline. We present simulation studies in the genetic SPM 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 on the impact of genetic factors on “hidden components of aging” indirectly evaluated from the data. We discuss implications of the results for analyses of available data and possible generalizations of the approaches.

Keywords: Hidden Components; Stochastic Process Model (SPM); Biospecimen Collection; Longitudinal Measurements; Negative Feedback Coefficient (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:spr:ssdmcp:978-94-017-7587-8_14

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DOI: 10.1007/978-94-017-7587-8_14

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