Composite Following the strategic directions proposed by the National Institute on Aging, an overall objective of the proposed research in this program project is to identify genetic and non-genetic factors and mechanisms, which can promote long and healthy life in humans on the basis of better understanding the relationships among regulators of aging, risks, of major diseases and related traits, and lifespan. This objective will be reached on the basis of integrative analyses of genome-wide SNP genotyping data and longitudinal data on life course processes in human organisms. The research in this program project will be performed in three subprojects, supported by the (A) Administrative, and (B) Data Management/Analytic Cores. We will use traditional and advanced methodologies of genetic analyses and statistical modeling, and methods of systems biology, which will be built on knowledge accumulated in the fields of aging, health, and lifespan incorporated into the integrative statistical platform. The methodological concept of the POI stands to advance paradigms of current GWAS and future association studies, using next generation sequencing, by bringing state-of-the-art methods to analyzing traits of late life that breaks new ground in the area of life-course genetics. The project will address three Specific Aims.
Aim 1. Conduct comprehensive association analyses using genome-wide SNP genotyping data to identify pleiotropic and specific genetic underpinnings of lifespan, risks of major diseases, health related traits, and physiological aging changes in human body.
Aim 2. Conduct analysis of up-to-date information on biological effects of pleiotropic and specific genes for SNPs discovered in Aim 1 to dissect their roles in molecular pathways, and biological processes and functions.
Aim 3. Perform dynamic integration of genetic effects revealed in Aims 1 and 2 into the life course processes in individuals by combining methods of systems biology and advanced statistical modeling.

Public Health Relevance

This Program Project directly addresses concerns which are of inherent relevance to public health including concern on genetic predisposition to medical complications and side-effects as a result of complex nature of gene action on health traits and concern on whether such genes can be considered as early life targets for preventive interventions as a result of their important role in health changes during the individuals'life course.

Agency
National Institute of Health (NIH)
Institute
National Institute on Aging (NIA)
Type
Research Program Projects (P01)
Project #
1P01AG043352-01A1
Application #
8668227
Study Section
Special Emphasis Panel (ZAG1-ZIJ-8 (J2))
Program Officer
Rossi, Winifred K
Project Start
2014-06-15
Project End
2019-04-30
Budget Start
2014-06-15
Budget End
2015-04-30
Support Year
1
Fiscal Year
2014
Total Cost
$1,751,976
Indirect Cost
$636,068
Name
Duke University
Department
Social Sciences
Type
Schools of Arts and Sciences
DUNS #
044387793
City
Durham
State
NC
Country
United States
Zip Code
27705
Yashin, Anatoliy I; Arbeev, Konstantin G; Arbeeva, Liubov S et al. (2016) How the effects of aging and stresses of life are integrated in mortality rates: insights for genetic studies of human health and longevity. Biogerontology 17:89-107
Yashin, Anatoliy I; Zhbannikov, Ilya; Arbeeva, Liubov et al. (2016) Pure and Confounded Effects of Causal SNPs on Longevity: Insights for Proper Interpretation of Research Findings in GWAS of Populations with Different Genetic Structures. Front Genet 7:188
Ukraintseva, Svetlana; Yashin, Anatoliy; Arbeev, Konstantin et al. (2016) Puzzling role of genetic risk factors in human longevity: "risk alleles" as pro-longevity variants. Biogerontology 17:109-27
Kulminski, Alexander M; Loika, Yury; Culminskaya, Irina et al. (2016) Explicating heterogeneity of complex traits has strong potential for improving GWAS efficiency. Sci Rep 6:35390
Yashin, Anatoliy I; Arbeev, Konstantin G; Wu, Deqing et al. (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 20:201-232
He, Liang; Sillanpää, Mikko J; Silventoinen, Karri et al. (2016) Estimating Modifying Effect of Age on Genetic and Environmental Variance Components in Twin Models. Genetics 202:1313-28
Kulminski, Alexander M; Raghavachari, Nalini; Arbeev, Konstantin G et al. (2016) Protective role of the apolipoprotein E2 allele in age-related disease traits and survival: evidence from the Long Life Family Study. Biogerontology 17:893-905
Arbeev, Konstantin G; Cohen, Alan A; Arbeeva, Liubov S et al. (2016) Optimal Versus Realized Trajectories of Physiological Dysregulation in Aging and Their Relation to Sex-Specific Mortality Risk. Front Public Health 4:3
Arbeev, Konstantin G; Ukraintseva, Svetlana V; Yashin, Anatoliy I (2016) Dynamics of biomarkers in relation to aging and mortality. Mech Ageing Dev 156:42-54
Kulminski, Alexander M; Culminskaya, Irina; Arbeev, Konstantin G et al. (2015) Birth Cohort, Age, and Sex Strongly Modulate Effects of Lipid Risk Alleles Identified in Genome-Wide Association Studies. PLoS One 10:e0136319

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