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. 2013 Nov 19;110(47):19006-11.
doi: 10.1073/pnas.1313735110. Epub 2013 Nov 4.

Genetic basis of neurocognitive decline and reduced white-matter integrity in normal human brain aging

Affiliations

Genetic basis of neurocognitive decline and reduced white-matter integrity in normal human brain aging

David C Glahn et al. Proc Natl Acad Sci U S A. .

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.

Keywords: diffusion tensor imaging; fractional anisotropy; gene x environment interaction; genetic correlation; neurocognition.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.
Fig. 1.
The distribution of age (A) and its impact on neurocognitive function (B) and DTI-derived measures of white-matter integrity (C) in the Genetics of Brain Structure and Function Study cohort (n = 1,129). (A) Histogram representing the age distribution of study participants. Subjects’ ages ranged from 18 to 83, with a mean of 45.82 ± 14.82. (B) Performance after z-transformation on each of the 22 neurocognitive variables stratified by the participant age. Though there is some test-specific variability, the overwhelming trend is for dramatic age-related declines across all neurocognitive measures. (C) Comparable to B, but reflects 17 tract-based measures of white-matter integrity changes with advancing age.
Fig. 2.
Fig. 2.
The influence of aging and additive genetics on measures of tract-based white-matter integrity is presented. Though the Upper depicts linear effects of aging on tract-based FA measures, the Lower represents the heritability of each tract. All tracts were significantly heritable and strongly effected by aging. Fig. S1 provides reference labels for tracts.
Fig. 3.
Fig. 3.
A heat map reflecting –log P values for phenotypic and genetic correlations between neurocognitive and tract-based white-matter integrity measures from 809 individuals (see Table S1 for more detail). Though a number of significant phenotypic correlations were estimated, no genetic correlation was significant.
Fig. 4.
Fig. 4.
Predicted changes in heritability (A) or genetic correlation (B) with age and the formal interaction terms (C) for neurocognitive and white-matter integrity traits generated via a gene-by-environment interaction analysis conducted with cross-sectional data in extended pedigrees where aging was treated as an environmental factor (e.g., G × A interaction analysis). (A) Additive genetic heritabilities as a function of age for traits that showed significant changes in genetic variance (σg2) with age. (B) Significant changes in the genetic correlation (ρg) as functions of advancing age. (C) Scatter plot of all of the standardized G × A interaction terms for σg2 (parameter estimate γ) or ρg (parameter estimate λ) for the neurocognitive and white-matter traits separately. The distribution of these interaction terms differed significantly between these classes of traits (γ neurocognitive −0.013 vs. γ white matter 0.005, P = 0.0001).

References

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