How Genes Modulate Patterns of Aging-Related Changes on the Way to 100: Biodemographic Models and Methods in Genetic Analyses of Longitudinal Data.
Abstract
BACKGROUND AND OBJECTIVE: To clarify mechanisms of genetic regulation of human aging
and longevity traits, a number of genome-wide association studies (GWAS) of these
traits have been performed. However, the results of these analyses did not meet expectations
of the researchers. Most detected genetic associations have not reached a genome-wide
level of statistical significance, and suffered from the lack of replication in the
studies of independent populations. The reasons for slow progress in this research
area include low efficiency of statistical methods used in data analyses, genetic
heterogeneity of aging and longevity related traits, possibility of pleiotropic (e.g.,
age dependent) effects of genetic variants on such traits, underestimation of the
effects of (i) mortality selection in genetically heterogeneous cohorts, (ii) external
factors and differences in genetic backgrounds of individuals in the populations under
study, the weakness of conceptual biological framework that does not fully account
for above mentioned factors. One more limitation of conducted studies is that they
did not fully realize the potential of longitudinal data that allow for evaluating
how genetic influences on life span are mediated by physiological variables and other
biomarkers during the life course. The objective of this paper is to address these
issues. DATA AND METHODS: We performed GWAS of human life span using different subsets
of data from the original Framingham Heart Study cohort corresponding to different
quality control (QC) procedures and used one subset of selected genetic variants for
further analyses. We used simulation study to show that approach to combining data
improves the quality of GWAS. We used FHS longitudinal data to compare average age
trajectories of physiological variables in carriers and non-carriers of selected genetic
variants. We used stochastic process model of human mortality and aging to investigate
genetic influence on hidden biomarkers of aging and on dynamic interaction between
aging and longevity. We investigated properties of genes related to selected variants
and their roles in signaling and metabolic pathways. RESULTS: We showed that the use
of different QC procedures results in different sets of genetic variants associated
with life span. We selected 24 genetic variants negatively associated with life span.
We showed that the joint analyses of genetic data at the time of bio-specimen collection
and follow up data substantially improved significance of associations of selected
24 SNPs with life span. We also showed that aging related changes in physiological
variables and in hidden biomarkers of aging differ for the groups of carriers and
non-carriers of selected variants. CONCLUSIONS: . The results of these analyses demonstrated
benefits of using biodemographic models and methods in genetic association studies
of these traits. Our findings showed that the absence of a large number of genetic
variants with deleterious effects may make substantial contribution to exceptional
longevity. These effects are dynamically mediated by a number of physiological variables
and hidden biomarkers of aging. The results of these research demonstrated benefits
of using integrative statistical models of mortality risks in genetic studies of human
aging and longevity.
Type
Journal articleSubject
genetic model of mortalitygenetics of aging
genetics of longevity
physiological variables
stress resistance
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https://hdl.handle.net/10161/14755Published Version (Please cite this version)
10.1080/10920277.2016.1178588Publication Info
Yashin, Anatoliy I; Arbeev, Konstantin G; Wu, Deqing; Arbeeva, Liubov; Kulminski,
Alexander; Kulminskaya, Irina; ... Ukraintseva, Svetlana V (2016). How Genes Modulate Patterns of Aging-Related Changes on the Way to 100: Biodemographic
Models and Methods in Genetic Analyses of Longitudinal Data. N Am Actuar J, 20(3). pp. 201-232. 10.1080/10920277.2016.1178588. Retrieved from https://hdl.handle.net/10161/14755.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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Show full item recordScholars@Duke
Igor Akushevich
Research Professor in the Social Science Research Institute
Konstantin Arbeev
Associate Research Professor in the Social Science Research Institute
Konstantin G. Arbeev received the M.S. degree in Applied Mathematics from Moscow State
University (branch in Ulyanovsk, Russia) in 1995 and the Ph.D. degree in Mathematics
and Physics (specialization in Theoretical Foundations of Mathematical Modeling, Numerical
Methods and Programming) from Ulyanovsk State University (Russia) in 1999. He was
a post-doctoral fellow in Max Planck Institute for Demographic Research in Rostock
(Germany) before moving to Duke University in 2004 to work as a Resea
Irina Kulminskaya
Research Scientist, Senior
Alexander Kulminski
Research Professor in the Social Science Research Institute
Svetlana Ukraintseva
Research Professor in the Social Science Research Institute
Dr. Ukraintseva studies causes of human aging and related decline in resilience, to
identify genetic and other factors responsible for the increase in mortality risk
with age eventually limiting longevity. She explores complex relationships, including
trade-offs, between physiological aging-changes and risks of major diseases (with
emphasis on Alzheimer’s and cancer), as well as survival, to find new genetic and
other targets for anti-aging interventions and disease prevention. S
Dequing Wu
Research Scientist, Senior
Anatoli I. Yashin
Research Professor in the Social Science Research Institute
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