"Predicting" parental longevity from offspring endophenotypes: data from the Long Life Family Study (LLFS).

Published

Journal Article

While there is evidence that longevity runs in families, the study of long-lived families is complicated by the fact that longevity-related information is available only for the oldest old, many of whom may be deceased and unavailable for testing, and information on other living family members, primarily descendents, is censored. This situation requires a creative approach for analyzing determinants of longevity in families. There are likely biomarkers that predict an individual's longevity, suggesting the possibility that those biomarkers which are heritable may constitute valuable endophenotypes for exceptional survival. These endophenotypes could be studied in families to identify human longevity genes and elucidate possible mechanisms of their influence on longevity. In this paper, we analyze data collected in the Long Life Family Study (LLFS) investigating whether indicators of physiological state, cognitive functioning and health/well-being among offspring predict longevity in parents. Good predictors can be used as endophenotypes for exceptional survival. Our analyses revealed significant associations between cumulative indices describing physiological state, as well as a number of offspring phenotypes, and parental lifespan, supporting both their familial basis and relevance to longevity. We conclude that the study of endophenotypes within families is a valid approach to the genetics of human longevity.

Full Text

Duke Authors

Cited Authors

  • Yashin, AI; Arbeev, KG; Kulminski, A; Borecki, I; Christensen, K; Barmada, M; Hadley, E; Rossi, W; Lee, JH; Cheng, R; Elo, IT

Published Date

  • March 2010

Published In

Volume / Issue

  • 131 / 3

Start / End Page

  • 215 - 222

PubMed ID

  • 20184914

Pubmed Central ID

  • 20184914

Electronic International Standard Serial Number (EISSN)

  • 1872-6216

International Standard Serial Number (ISSN)

  • 0047-6374

Digital Object Identifier (DOI)

  • 10.1016/j.mad.2010.02.001

Language

  • eng