Project 1 The objective of the research proposed in Project 1 is to significantly improve our understanding of the roles of genetic and non-genetic factors in regulation of "longevity-related" traits, which include: (i) lifespan;(ii) free of selected diseases lifespan;(iii) duration of life with diseases;and (iv) cause of death, as well as to investigate relation of these factors to processes of aging and disease development in human body. This objective will be reached by performing comprehensive analyses of available data on genome-wide SNP genotyping, non-genetic data on fixed covariates, longitudinal data on aging-related changes in physiological state, changes in health status associated with diseases such as cancer, CHD, diabetes, asthma, Alzheimer's disease, and stroke, available from several large sets of longitudinal and cross-sectional human data (FHS, ARIC, CHS, MESA, LOADFS, and HRS).
Specific aims : 1. Using state-of-the-art methods of GWAS, identify genetic and non-genetic factors having positive and negative associations with longevity-related traits defined above. 2. Identify subsets of SNP genetic variants showing pleiotropic (antagonistic and non-antagonistic) effects on two or more traits investigated in Project 1 and, more general in this P01, as well as the age-specific genetic effects on these traits. 3. Validate research findings obtained in Aims 1 and 2: (i) by replicating them in independent populations;(ii) by investigating functional properties of SNP-related genes from sets selected in Aim 2, and their roles in cellular pathways and metabolic processes involved in regulation of longevity-related traits. 4. Evaluate polygenic influence (of groups of genes) on longevity traits, including linear (additive) and non-linear (epistatic) effects. 5. Evaluate dynamic properties of mechanisms connecting longevity traits studied in this project with aging- and disease-related traits by analyzing available longitudinal data using extended versions of stochastic process model of human aging, health and mortality with coefficients depending on genetic and non-genetic covariates. The results of these analyses will facilitate development of personalized prevention and will significantly contribute to improvement of population health.
The results of these analyses will improve our understanding of mechanisms of aging related changes and their influence on health and survival outcomes. The new knowledge produced in course of work on this project will facilitate development of personalized preventive and treatment strategies which contribute to improvement of population health in the U.S. and other developed countries.