Core B: Data Manage Core B is designed: (i) to manage seven large-scale longitudinal datasets with phenotypic and genotyping information including the Framingham Heart Study (FHS), the Atherosclerosis Risk in Communities (ARIC) Study, the Cardiovascular Health Study (CHS), the Multi-Ethnic Study of Atherosclerosis (MESA), the Late Onset Alzheimer's Disease Family Study (LOADFS), the Health and Retirement Sun/ey (HRS), and the Long Life Family Study (LLFS) selected to cover major health problems of the elderly and comprehensively characterize aging-related processes and survival, (ii) to prepare genotyping and phenotypic information from these studies for each subproject, and (iii) to provide a basis for barrier-free integrative analyses of statistical and biological nature. As such, this core will assist all subprojects in both routine and highly significant aspects. Core B has four Specific Aims:
Aim 1. Download and manage longitudinal datasets;
Aim 2. Construct phenotypes;
Aim 3. Conduct integrative analyses using advanced models;
Aim 4. Dissect biological role of genes for allelic variants with systemic effects.
Aims 1 and 2 are designed to aggregate routine efforts associated with pre-processing these data to increase synergy by: (i) ensuring a unified access to the data and diminishing the role of study-specific biases when processing the data in each subproject and (ii) reducing the burden of routine data-processing procedures on researchers in subprojects.
Aim 3 stands to provide a barrier-free basis for integrating genetic effects discovered in each subproject into the life course processes in aging individuals. This role will be achieved by dropping barriers imposed by specifics of different phenotypes on the basis of a common methodological platform that is a key to enhancing synergism among the subprojects.
Aim 4 will combine statistical inferences on genetic underpinnings of healthspan with previously obtained biological information to provide insights on biologically meaningful mechanisms of the systemic nature underlying healthspan in aging individuals.

Public Health Relevance

Core B is designed to support ail subprojects of this P01 by providing efforts on management of the datasets to be used in the analyses in subprojects as well as by conducting integrative analyses of statistical and biological nature. The results the P01 will improve our understanding of mechanisms of aging related changes and their influence on health and survival outcomes.

National Institute of Health (NIH)
National Institute on Aging (NIA)
Research Program Projects (P01)
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Special Emphasis Panel (ZAG1-ZIJ-8 (J2))
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Duke University
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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
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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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