Heritability estimates of endophenotypes of long and health life: the Long Life Family Study.
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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.
SubjectCardiovascular Physiological Phenomena
Forced Expiratory Volume
National Institute on Aging (U.S.)
Principal Component Analysis
Quantitative Trait, Heritable
Published Version (Please cite this version)10.1093/gerona/glq154
Publication InfoMatteini, Amy M; Fallin, M Daniele; Kammerer, Candace M; Schupf, Nicole; Yashin, Anatoli I; Christensen, Kaare; ... Walston, Jeremy D (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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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 Resea
Research Professor in the Social Science Research Institute
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