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.
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.
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|He, Liang; Zhbannikov, Ilya; Arbeev, Konstantin G et al. (2017) A genetic stochastic process model for genome-wide joint analysis of biomarker dynamics and disease susceptibility with longitudinal data. Genet Epidemiol 41:620-635|
|Kulminski, Alexander M; Kernogitski, Yelena; Culminskaya, Irina et al. (2017) Uncoupling associations of risk alleles with endophenotypes and phenotypes: insights from the ApoB locus and heart-related traits. Aging Cell 16:61-72|
|Zhbannikov, Ilya Y; Arbeev, Konstantin; Akushevich, Igor et al. (2017) stpm: an R package for stochastic process model. BMC Bioinformatics 18:125|
|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|
|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|
|Kulminski, Alexander M; He, Liang; Culminskaya, Irina et al. (2016) Pleiotropic Associations of Allelic Variants in a 2q22 Region with Risks of Major Human Diseases and Mortality. PLoS Genet 12:e1006314|
|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|
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