Heritability estimates of endophenotypes of long and health life: the Long Life Family Study.


BACKGROUND: Identification of gene variants that contribute to exceptional survival may provide critical biologic information that informs optimal health across the life span. METHODS: As part of phenotype development efforts for the Long Life Family Study, endophenotypes that represent exceptional survival were identified and heritability estimates were calculated. Principal components (PCs) analysis was carried out using 28 physiologic measurements from five trait domains (cardiovascular, cognition, physical function, pulmonary, and metabolic). RESULTS: The five most dominant PCs accounted for 50% of underlying trait variance. The first PC (PC1), which consisted primarily of poor pulmonary and physical function, represented 14.3% of the total variance and had an estimated heritability of 39%. PC2 consisted of measures of good metabolic and cardiovascular function with an estimated heritability of 27%. PC3 was made up of cognitive measures (h(2) = 36%). PC4 and PC5 contained measures of blood pressure and cholesterol, respectively (h(2) = 25% and 16%). CONCLUSIONS: These PCs analysis-derived endophenotypes may be used in genetic association studies to help identify underlying genetic mechanisms that drive exceptional survival in this and other populations.





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Matteini, Amy M, M Daniele Fallin, Candace M Kammerer, Nicole Schupf, Anatoli I Yashin, Kaare Christensen, Konstantin G Arbeev, Graham Barr, et al. (2010). Heritability estimates of endophenotypes of long and health life: the Long Life Family Study. J Gerontol A Biol Sci Med Sci, 65(12). pp. 1375–1379. 10.1093/gerona/glq154 Retrieved from https://hdl.handle.net/10161/14882.

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Konstantin Arbeev

Associate Research Professor in the Social Science Research Institute

Konstantin G. Arbeev received the M.S. degree in Applied Mathematics from Moscow State University (branch in Ulyanovsk, Russia) in 1995 and the Ph.D. degree in Mathematics and Physics (specialization in Theoretical Foundations of Mathematical Modeling, Numerical Methods and Programming) from Ulyanovsk State University (Russia) in 1999. He was a post-doctoral fellow in Max Planck Institute for Demographic Research in Rostock (Germany) before moving to Duke University in 2004 to work as a Research Scientist and a Senior Research Scientist in the Department of Sociology and the Social Science Research Institute (SSRI).  He is currently an Associate Research Professor in SSRI. Dr. Arbeev's major research interests are related to three interconnected fields of biodemography, biostatistics and genetic epidemiology as pertains to research on aging. The focus of his research is on discovering genetic and non-genetic factors that can affect the process of aging and determine longevity and healthy lifespan. He is interested in both methodological advances in this research area as well as their practical applications to analyses of large-scale longitudinal studies with phenotypic, genetic and, recently, genomic information. Dr. Arbeev authored and co-authored more than 150 peer-reviewed publications in these areas.

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