| Literature DB >> 24191011 |
David C Glahn1, Jack W Kent, Emma Sprooten, Vincent P Diego, Anderson M Winkler, Joanne E Curran, D Reese McKay, Emma E Knowles, Melanie A Carless, Harald H H Göring, Thomas D Dyer, Rene L Olvera, Peter T Fox, Laura Almasy, Jac Charlesworth, Peter Kochunov, Ravi Duggirala, John Blangero.
Abstract
Identification of genes associated with brain aging should markedly improve our understanding of the biological processes that govern normal age-related decline. However, challenges to identifying genes that facilitate successful brain aging are considerable, including a lack of established phenotypes and difficulties in modeling the effects of aging per se, rather than genes that influence the underlying trait. In a large cohort of randomly selected pedigrees (n = 1,129 subjects), we documented profound aging effects from young adulthood to old age (18-83 y) on neurocognitive ability and diffusion-based white-matter measures. Despite significant phenotypic correlation between white-matter integrity and tests of processing speed, working memory, declarative memory, and intelligence, no evidence for pleiotropy between these classes of phenotypes was observed. Applying an advanced quantitative gene-by-environment interaction analysis where age is treated as an environmental factor, we demonstrate a heritable basis for neurocognitive deterioration as a function of age. Furthermore, by decomposing gene-by-aging (G × A) interactions, we infer that different genes influence some neurocognitive traits as a function of age, whereas other neurocognitive traits are influenced by the same genes, but to differential levels, from young adulthood to old age. In contrast, increasing white-matter incoherence with age appears to be nongenetic. These results clearly demonstrate that traits sensitive to the genetic influences on brain aging can be identified, a critical first step in delineating the biological mechanisms of successful aging.Entities:
Keywords: diffusion tensor imaging; fractional anisotropy; gene x environment interaction; genetic correlation; neurocognition
Mesh:
Year: 2013 PMID: 24191011 PMCID: PMC3839730 DOI: 10.1073/pnas.1313735110
Source DB: PubMed Journal: Proc Natl Acad Sci U S A ISSN: 0027-8424 Impact factor: 11.205