The genetic organization of longitudinal subcortical volumetric change is stable throughout the lifespan

Fjell, Anders Martin; Grydeland, Håkon; Wang, Yunpeng; Amlien, Inge K; Bartrés‐Faz, David; Brandmaier, Andreas M.; Düzel, Sandra; Elman, Jeremy A.; Franz, Carol E.; Håberg, Asta; Kietzmann, Tim C.; Kievit, Rogier A.; Kremen, William S.; Krogsrud, Stine Kleppe; Kühn, Simone; Lindenberger, Ulman; Macià, Dídac; Mowinckel, Athanasia M.; Nyberg, Lars; Panizzon, Matthew S.; Solé‐Padullés, Cristina; Sørensen, Øystein; Westerhausen, René; Walhovd, Kristine B.

doi:10.7554/elife.66466

Cited by 9 publications

(6 citation statements)

References 75 publications

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“…Despite the pleiotropic effects, concordance of effects was generally null. This is not surprising, as rate-of-change measures for brain structures are not constant and often switch sign over the course of the lifespan 1 , 39 , whereas the GWAS for other traits assume stability of both the phenotype and the genetic influences on the phenotype over time. As such, concordance and discordance of effects would not be expected.…”

Section: Discussionmentioning

confidence: 99%

“…This approach allowed us to find brain-structure-specific associations. However, several longitudinal studies have described phenotypic correlations between structural changes 39 , 47 , 48 ; combining several phenotypes could thus be an alternative approach to identify genetic variants that exert a global effect. Of note, cohort and age are intertwined in our meta-regression analysis.…”

Section: Discussionmentioning

confidence: 99%

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Genetic variants associated with longitudinal changes in brain structure across the lifespan

et al. 2022

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Under the influence of genes and a varying environment, human brain structure changes throughout the lifespan. Even in adulthood, when the brain seems relatively stable, individuals differ in the profile and rate of brain changes 1 . Longitudinal studies are crucial to identify genetic and environmental factors that influence the rate of these brain changes throughout development 2 and aging 3 . Inter-individual differences in brain development are associated with general cognitive function 4,5 and risk for psychiatric disorders 6,7 and neurological diseases 8,9 . Genetic factors involved in brain development and aging overlap with those for cognition 10 and risk for neuropsychiatric disorders 11 . A recent cross-sectional study showed brain age to be advanced in several brain disorders. Brain age is an estimate of biological age based on brain structure, which can deviate from chronological age. Several shared loci were found between the genome-wide association study (GWAS) summary statistics for advanced brain age and psychiatric disorders 12 . However, information is still lacking on which genetic variants influence an individual's brain changes throughout life, because this requires longitudinal data. Discovering genetic factors that explain variation between individuals in brain structural changes may reveal key biological pathways that drive normal development and aging and may contribute to identifying disease risk and resilience-a crucial goal given the urgent need for new treatments for aberrant brain development and aging worldwide.As part of the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) consortium 13 , the ENIGMA Plasticity Working Group quantified the overall genetic contribution to longitudinal brain changes by combining evidence from multiple twin cohorts across the world 14 . Most global and subcortical brain measures showed genetic influences on change over time, with a higher genetic contribution in the elderly (heritability, 16-42%). Genetic factors that influence longitudinal changes were partially independent of those that influence baseline volumes of brain structures, suggesting that there might be genetic variants that specifically affect the rate of development or aging. However, the genes involved in these processes are still not known, with only a single, small-scale GWAS performed for longitudinal volume change in gray and white matter of the cerebrum, basal ganglia and cerebellum 15 . In this study, we set out to find genetic variants that may influence rates of brain changes over time, using genome-wide analysis in individuals scanned with magnetic resonance imaging (MRI) on more than one occasion. We also aimed to identify references

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Genetic variants associated with longitudinal changes in brain structure across the lifespan

et al. 2022

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“…The observed heterogeneity in atrophy profiles may relate to evidence that the genetic influences on atrophy rates differ for cortex, hippocampus, and the caudate ( Fjell et al. 2021 ; Brouwer et al.…”

Section: Discussionmentioning

confidence: 99%

“…2020 ). Additional genes with distinct influences on the caudate relative to hippocampus and cortex likely also play a role ( Fjell et al. 2021 ).…”

Section: Discussionmentioning

confidence: 99%

Individual differences in brain aging: heterogeneity in cortico-hippocampal but not caudate atrophy rates

et al. 2022

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It is well documented that some brain regions, such as association cortices, caudate, and hippocampus, are particularly prone to age-related atrophy, but it has been hypothesized that there are individual differences in atrophy profiles. Here, we document heterogeneity in regional-atrophy patterns using latent-profile analysis of 1,482 longitudinal magnetic resonance imaging observations. The results supported a 2-group solution reflecting differences in atrophy rates in cortical regions and hippocampus along with comparable caudate atrophy. The higher-atrophy group had the most marked atrophy in hippocampus and also lower episodic memory, and their normal caudate atrophy rate was accompanied by larger baseline volumes. Our findings support and refine models of heterogeneity in brain aging and suggest distinct mechanisms of atrophy in striatal versus hippocampal-cortical systems.

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“…It has been found that changes of cortical thickness covariances in both development and ageing were closely correlated with the genetic organisation (Fjell et al, 2015), and subcortical structures were also governed by the same sets of genes (Fjell et al, 2021). It would be interesting to explore whether cortical thickness and subcortical covariances are associated with longevity by examining relevant genes of longevity polygenic risk scores (longevity-PRS) (Dong et al, 2022; Tesi et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Brain structural covariances in the ageing brain in the UK Biobank

Dong¹,

Thalamuthu²,

Jiang³

et al. 2022

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Brain structural covariances or pairwise correlations describe how morphologic properties of brain regions are related to one another across individuals. Although it is reported that brain structural covariance changes during brain development, it is not clear how structural covariance relates to the ageing process. Here we investigated the human brain structural covariances of cortical thickness and subcortical volumes in the ageing brain and their associations with age, cognition, and longevity polygenic risk score (longevity-PRS) by using cross-sectional data from the UK Biobank (N = 42075, aged 45-83 years, M/F=19752 /22323). The sample of participants was divided into 84 non-overlapping groups based on their age. The older the age group, the greater the variability in the whole brain structural covariance. However, there was a differential rate of age-related increase of variance between males and females. The variance of females started lower than those of males and then increased with age with a greater gradient than that of males. There was a consistent and significant enrichment of pairwise correlations within the occipital lobe in ageing process. The cortical thickness and subcortical covariances in older groups were significantly different from those in the youngest group. Sixty-two of the total 528 pairs of cortical thickness correlations and 10 of the total 21 pairs of subcortical volume correlations were significantly associated with age after Bonferroni correction. Specifically, with an increasing age, most decreased cortical thickness correlations were found between the regions within the frontal lobe as well as between the frontal lobe regions and regions in other lobes, while pairwise correlations within occipital lobe regions were all strengthening. Most of these correlations were also associated with global cognition and weakly associated with longevity-PRS. These findings revealed that the structural covariance was not stable during ageing. Given the thinning of the cortex and the volumetric reduction of subcortical structures seen in the ageing process, an increased pairwise correlation between the brain regions in the older brain suggested a strengthened coordinated decline between the brain regions involved. However, some of the brain regions demonstrated a differentiated rate of decline which was shown as the inversed or reduced pairwise correlations between these regions.

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The genetic organization of longitudinal subcortical volumetric change is stable throughout the lifespan

Cited by 9 publications

References 75 publications

Genetic variants associated with longitudinal changes in brain structure across the lifespan

Genetic variants associated with longitudinal changes in brain structure across the lifespan

Individual differences in brain aging: heterogeneity in cortico-hippocampal but not caudate atrophy rates

Brain structural covariances in the ageing brain in the UK Biobank

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