Anatoliy I Yashin1, Konstantin G Arbeev2, Deqing Wu3, Liubov Arbeeva4, Alexander Kulminski5, Irina Kulminskaya6, Igor Akushevich7, Svetlana V Ukraintseva8. 1. Professor, Center for Population Health and Aging, Duke University, 2024 W. Main Street, Room A102E, Durham, NC 27705, USA. Tel.: (+1) 919-668-2713. 2. Sr. Research Scientist, Center for Population Health and Aging, Duke University, 2024 W. Main Street, Room A102F, Durham, NC 27705, USA. Tel.: (+1) 919-668-2707. 3. Sr. Research Scientist, Center for Population Health and Aging, Duke University, 2024 W. Main Street, Room A104, Durham, NC 27705, USA. Tel.: (+1) 919-684-6126. 4. Statistician, Center for Population Health and Aging, Duke University, 2024 W. Main Street, Room A102G, Durham, NC 27705, USA. Tel.: (+1) 919-613-0715. 5. Sr. Research Scientist, Center for Population Health and Aging, Duke University, 2024 W. Main Street, Room A106, Durham, NC 27705, USA. Tel.: (+1) 919-684-4962. 6. Research Scientist, Center for Population Health and Aging, Duke University, 2024 W. Main Street, Room A102D, Durham, NC 27705, USA. Tel.: (+1) 919-681-8232. 7. Sr. Research Scientist, Center for Population Health and Aging, Duke University, 2024 W. Main Street, Room A107, Durham, NC 27705, USA. Tel.: (+1) 919-668-2715. 8. Sr. Research Scientist, Center for Population Health and Aging, Duke University, 2024 W. Main Street, Room A105, Durham, NC 27705, USA. Tel.: (+1) 919-668-2712.
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.
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.
Entities:
Keywords:
genetic model of mortality; genetics of aging; genetics of longevity; physiological variables; stress resistance
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