Biology subjects

Whalley, H. C.

Publications and source records attributed to Whalley, H. C..

At least 19 recordsLinked to original sources

bioRxiv2026

General cognitive function and the brain's structural connectome

General cognitive function ( g) reflects a broad capacity for flexible information processing, yet how brain-wide structural connectivity supports it remains unclear. In 38,824 individuals (26-84 years) across three cohorts, we show that g is supported by a widely distributed white matter network. Across streamline count, fractional anisotropy (FA), and mean diffusivity (MD), meta-analytic associations with g were widespread at global, nodal and edge levels, spanning all cerebral lobes and key subcortical structures and dependent on both inter- and intra-hemispheric connectivity, particularly ipsilateral long-range inter-lobar connections. White matter node-g associations spatially mirrored independent cortical morphometry-g associations, indicating convergence of grey and white matter contributions. Effect sizes increased with age: MD associations became more negative and FA more positive, especially in frontal regions. Edge-level findings replicated across cohorts and predicted g in a hold-out sample. These findings suggest g reflects a distributed structural communication backbone which becomes increasingly important with age.

neuroscience↗

bioRxiv2025

Lifestyle, Early-life, and Genetic Health Risk Factors Underlying the Brain Age Gap: A Mega-Analysis Across 3,934 Individuals from the ENIGMA MDD Consortium

BackgroundLarge-scale studies show that adults with major depressive disorder (MDD) generally have a higher imaging-predicted age relative to their chronological age (i.e., positive brain age gap) compared to controls, though considerable within-group variation exists. This study examines lifestyle, early-life, and genetic health risk factors contributing to the brain age gap. Identifying risk and resilience factors could help protect brain and mental health. MethodsUsing an established model trained on FreeSurfer-derived brain regions (www.photon-ai.com/enigma_brainage), we generated brain age predictions for 1,846 controls and 2,088 individuals with MDD (aged 18-75) from 12 international cohorts. Polygenic risk scores (PRS) were calculated for major depression, C-reactive protein, and body mass index (BMI) using large-scale GWAS results. Linear mixed models were applied to assess lifestyle (BMI, smoking, education), early-life childhood trauma, and genetic (PRS) health risk associations with the brain age gap. Additionally, we evaluated the link between the brain age gap and peripheral biological age indicators (epigenetic clocks). ResultsHigher brain age gaps were significantly associated with BMI ({beta}=0.01, PFDR=0.02) and smoking ({beta}=0.11, PFDR=0.02), while lower brain age gaps were linked to higher education ({beta}=-0.02, PFDR=0.02). Higher childhood trauma scores predicted a higher brain age gap ({beta}=0.04, P=0.01). Higher brain age gaps were positively associated with all PRS ({beta}s=0.04-0.16, PsFDR=0.02-0.03). There were no significant interactions between diagnosis and assessed factors on the brain age gap. In a multivariable model, only modifiable health factors--BMI, smoking, and education--remained uniquely associated with brain age gaps. ConclusionsGenetic liability for depression and related traits is linked to poorer brain health, but health behaviors potentially offer a key opportunity for intervention. This study underscores the importance of targeting modifiable lifestyle factors to mitigate poor brain health in depressed individuals, an approach perhaps under-recognized in clinical practice.

neuroscience↗

bioRxiv2025

Relative strength variability measures for brain structural connectomes and their relationship with cognitive functioning

In this work, we propose a new class of graph measures for weighted connectivity information in the human brain based on node relative strengths: relative strength variability (RSV), measuring susceptibility to targeted attacks, and hierarchical RSV (hRSV), a first weighted statistical complexity measure for networks. Using six different network weights for structural connectomes from the UK Biobank, we conduct comprehensive analyses to explore relationships between the RSV and hRSV, and (i) other known network measures, (ii) general cognitive function ( g). Both measures exhibit low correlations with other graph measures across all connectivity weightings indicating that they capture new information of the brain connectome. We found higher g was associated with lower RSV and lower hRSV. That is, higher g was associated with higher resistance to targeted attack and lower statistical complexity. Moreover, the proposed measures had consistently stronger associations with g than other widely used graph measures including clustering coefficient and global efficiency and were incrementally significant for predicting g above other measures for five of the six network weights. Overall, we present a new class of weighted network measures based on variations of relative node strengths which significantly improved prediction of general cognition from traditional weighted structural connectomes.

neuroscience↗

bioRxiv2024

Brain maps of general cognitive function and spatial correlations with neurobiological cortical profiles

In this paper, we attempt to answer two questions: 1) which regions of the human brain, in terms of morphometry, are most strongly related to individual differences in domain-general cognitive functioning (g)? and 2) what are the underlying neurobiological properties of those regions? We meta-analyse vertex-wise g-cortical morphometry (volume, surface area, thickness, curvature and sulcal depth) associations using data from 3 cohorts: the UK Biobank (UKB), Generation Scotland (GenScot), and the Lothian Birth Cohort 1936 (LBC1936), with the meta-analytic N = 38,379 (age range = 44 to 84 years old). These g-morphometry associations vary in magnitude and direction across the cortex (|{beta}| range = -0.12 to 0.17 across morphometry measures) and show good cross-cohort agreement (mean spatial correlation r = 0.57, SD = 0.18). Then, to address (2), we bring together existing -and derive new -cortical maps of 33 neurobiological characteristics from multiple modalities (including neurotransmitter receptor densities, gene expression, functional connectivity, metabolism, and cytoarchitectural similarity). We discover that these 33 profiles spatially covary along four major dimensions of cortical organisation (accounting for 65.9% of the variance) and denote aspects of neurobiological scaffolding that underpin the spatial patterning of MRI-cognitive associations we observe (significant |r| range = 0.21 to 0.56). Alongside the cortical maps from these analyses, which we make openly accessible, we provide a compendium of cortex-wide and within-region spatial correlations among general and specific facets of brain cortical organisation and higher order cognitive functioning, which we hope will serve as a framework for analysing other aspects of behaviour-brain MRI associations.

neuroscience↗

bioRxiv2024

Lifetime brain atrophy estimated from a single MRI: measurement characteristics and genome-wide correlates

A measure of lifetime brain atrophy (LBA) obtained from a single magnetic resonance imaging (MRI) scan could be an attractive candidate to boost statistical power in uncovering novel genetic signals and mechanisms of neurodegeneration. We analysed data from five young and old adult cohorts (MRi-Share, Human Connectome Project, UK Biobank, Generation Scotland Subsample, and Lothian Birth Cohort 1936 [LBC1936]) to test the validity and utility of LBA inferred from cross-sectional MRI data, i.e., a single MRI scan per participant. LBA was simply calculated based on the relationship between total brain volume (TBV) and intracranial volume (ICV), using three computationally distinct approaches: the difference (ICV-TBV), ratio (TBV/ICV), and regression-residual method (TBV[~]ICV). LBA derived with all three methods were substantially correlated with well-validated neuroradiological atrophy rating scales (r = 0.37-0.44). Compared with the difference or ratio method, LBA computed with the residual method most strongly captured phenotypic variance associated with cognitive decline (r = 0.36), frailty (r = 0.24), age-moderated brain shrinkage (r = 0.45), and longitudinally-measured atrophic changes (r = 0.36). LBA computed using a difference score was strongly correlated with baseline (i.e., ICV; r = 0.81) and yielded GWAS signal similar to ICV (rg = 0.75). We performed the largest genetic study of LBA to date (N = 43,110), which was highly heritable (h2 SNP GCTA = 41% [95% CI = 38-43%]) and had strong polygenic signal (LDSC h2 = 26%; mean{chi} 2 = 1.23). The strongest association in our genome-wide association study (GWAS) implicated WNT16, a gene previously linked with neurodegenerative diseases such as Alzheimer, and Parkinson disease, and amyotrophic lateral sclerosis. This study is the first side-by-side evaluation of different computational approaches to estimate lifetime brain changes and their measurement characteristics. Careful assessment of methods for LBA computation had important implications for the interpretation of existing phenotypic and genetic results, and showed that relying on the residual method to estimate LBA from a single MRI scan captured brain shrinkage rather than current brain size. This makes this computationally-simple definition of LBA a strong candidate for more powerful analyses, promising accelerated genetic discoveries by maximising the use of available cross-sectional data.

genetics↗

bioRxiv2023

The influence of birthweight, socioeconomic status, and adult health on brain volumes during aging

Preserved late-life brain volume is associated with resilience to dementia. We examined relationships between birthweight, socioeconomic status and adult health with late-life brain volumes. We hypothesised that early-life factors have direct and indirect effects on the aging brain. Neurotypical adults aged 61-67y underwent MRI and brain volumes measured. Birthweight, health and socioeconomic status were assessed by historical data or contemporary assessment. Measures of health and socioeconomic status were extracted using principal component analysis. Relationships between variables were explored by linear regression and structural equation models. Birthweight ({beta}=0.095, p=0.017, n=280) and childhood socioeconomic status ({beta}=0.091, p=0.033, n=280) were directly associated with late-life brain volume. Childhood socioeconomic status was associated with additional increase in grey matter volume ({beta}=0.04, p=0.047, n=280). Better adult health was linked to increased late-life brain volume ({beta}=0.15, p=0.003, n=280). Birthweight and childhood socioeconomic status are associated with whole and regional brain volume through direct and indirect mechanisms. Optimal fetal development, good adult health and reduced poverty, may prevent brain atrophy and decrease dementia risk in late-life.

neuroscience↗

bioRxiv2023

General and specific patterns of cortical gene expression as substrates of complex cognitive functioning

Gene expression varies across the brain. This spatial patterning denotes specialised support for particular brain functions. However, the way that a given genes expression fluctuates across the brain may be governed by general rules. Quantifying patterns of spatial covariation across genes would offer insights into the molecular characteristics of brain areas supporting, for example, complex cognitive functions. Here, we use principal component analysis to separate general and unique gene regulatory associations with cortical substrates of cognition. We find that the region-to-region variation in cortical expression profiles of 8235 genes covaries across two major principal components : gene ontology analysis suggests these dimensions are characterised by downregulation and upregulation of cell-signalling/modification and transcription factors. We validate these patterns out-of-sample and across different data processing choices. Brain regions more strongly implicated in general cognitive functioning (g; 3 cohorts, total meta-analytic N = 39,519) tend to be more balanced between downregulation and upregulation of both major components (indicated by regional component scores). We then identify a further 41 genes as candidate cortical spatial correlates of g, beyond the patterning of the two major components (|{beta}| range = 0.15 to 0.53). Many of these genes have been previously associated with clinical neurodegenerative and psychiatric disorders, or with other health-related phenotypes. The results provide insights into the cortical organisation of gene expression and its association with individual differences in cognitive functioning.

neuroscience↗

bioRxiv2022

Feature similarity gradients detect alterations in the neonatal cortex associated with preterm birth

The early life environment programmes cortical architecture and cognition across the life course. A measure of cortical organisation that integrates information from multi-modal MRI and is unbound by arbitrary parcellations has proven elusive, which hampers efforts to uncover the perinatal origins of cortical health. Here, we use the Vogt-Bailey index to provide a fine-grained description of regional homogeneities and sharp variations in cortical microstructure based on feature gradients, and we investigate the impact of being born preterm on cortical development. Preterm infants have a homogeneous microstructure in temporal and occipital lobes, and the medial parietal, cingulate, and frontal cortices, compared with term infants. These observations replicated across two independent datasets and were robust to differences that remain in the data after matching samples and alignment of processing and quality control strategies. We conclude that cortical microstructural architecture is altered in preterm infants in a spatially distributed rather than localised fashion.

neuroscience↗

bioRxiv2022

Sex differences in predictors and regional patterns of brain-age-gap estimates

BackgroundThe brain-age-gap estimate (brainAGE) quantifies the difference between chronological age and age predicted by applying machine-learning models to neuroimaging data, and is considered a biomarker of brain health. Understanding sex-differences in brainAGE is a significant step toward precision medicine. MethodsGlobal and local brainAGE (G-brainAGE and L-brainAGE, respectively) were computed by applying machine learning algorithms to brain structural magnetic resonance imaging data from 1113 healthy young adults (54.45% females; age range: 22-37 years) participating in the Human Connectome Project. Sex-differences were determined in G-brainAGE and L-brainAGE. Random forest regression was used to determine sex-specific associations between G-brainAGE and non-imaging measures pertaining to sociodemographic characteristics and mental, physical, and cognitive functions. ResultsL-brainAGE showed sex-specific differences in brain ageing. In females, compared to males, L-brainAGE was higher in the cerebellum and brainstem and lower in the prefrontal cortex and insula. Although sex-differences in G-brainAGE were minimal, associations between G-brainAGE and non-imaging measures differed between sexes with the exception for poor sleep quality, which was common to both. The most important predictor of higher G-brainAGE was non-white race in males and systolic blood pressure in females. ConclusionsThe results demonstrate the value of applying sex-specific analyses and machine learning methods to advance our understanding of sex-related differences in factors that influence the rate of brain ageing and provide a foundation for targeted interventions.

neuroscience↗

bioRxiv2021

Systematic Evaluation of Machine Learning Algorithms for Neuroanatomically-Based Age Prediction in Youth

Application of machine learning algorithms to structural magnetic resonance imaging (sMRI) data has yielded behaviorally meaningful estimates of the biological age of the brain (brain-age). The choice of the machine learning approach in estimating brain-age in children and adolescents is important because age-related brain changes in these age-groups are dynamic. However, the comparative performance of the multiple machine learning algorithms available has not been systematically appraised. To address this gap, the present study evaluated the accuracy (Mean Absolute Error; MAE) and computational efficiency of 21 machine learning algorithms using sMRI data from 2,105 typically developing individuals aged 5 to 22 years from five cohorts. The trained models were then tested in two independent holdout datasets, comprising 4,078 pre-adolescents aged 9-10 years and another sample of 594 individuals aged 5-21 years. The algorithms encompassed parametric and nonparametric, Bayesian, linear and nonlinear, tree-based, and kernel-based models. Sensitivity analyses were performed for parcellation scheme, number of neuroimaging input features, number of cross-validation folds, number of extreme outliers, and sample size. The best performing algorithms were Extreme Gradient Boosting (MAE of 1.49), Random Forest Regression (MAE of 1.58) and Support Vector Regression with Radial Basis Function Kernel (MAE of 1.64) which had acceptable and comparable computational efficiency. Findings of the present study could be used as a guide for optimizing methodology when quantifying age-related changes in youth.

neuroscience↗

bioRxiv2020

Three major dimensions of human brain cortical ageing in relation to cognitive decline across the 8th decade of life.

Different brain regions can be grouped together, based on cross-sectional correlations among their cortical characteristics; this patterning has been used to make inferences about ageing processes. However, cross-sectional brain data conflates information on ageing with patterns that are present throughout life. We characterised brain cortical ageing across the 8th decade of life in a longitudinal ageing cohort, at ages ~73, ~76, and ~79 years, with a total of 1,376 MRI scans. Volumetric changes among cortical regions of interest (ROIs) were more strongly correlated (average r = 0.805, SD = 0.252) than were cross-sectional volumes of the same ROIs (average r = 0.350, SD = 0.178). We identified a broad, cortex-wide, dimension of atrophy that explained 66% of the variance in longitudinal changes across the cortex. Our modelling also discovered more specific fronto-temporal and occipito-parietal dimensions, that were orthogonal to the general factor and together explained an additional 20% of the variance. The general factor was associated with declines in general cognitive ability (r = 0.431, p < 0.001) and in the domains of visuospatial ability (r = 0.415, p = 0.002), processing speed (r = 0.383, p < 0.001) and memory (r = 0.372, p < 0.001). Individual differences in brain cortical atrophy with ageing are manifest across three broad dimensions of the cerebral cortex, the most general of which is linked with cognitive declines across domains. Longitudinal approaches are invaluable for distinguishing lifelong patterns of brain-behaviour associations from patterns that are specific to aging.

neuroscience↗

bioRxiv2019

Psychotic-like experiences, polygenic risk scores for schizophrenia and structural properties of the salience, default mode and central-executive networks in healthy participants from UK Biobank

Schizophrenia is a highly heritable disorder with considerable phenotypic heterogeneity. Hallmark psychotic symptoms can be considered as existing on a continuum from non-clinical to clinical populations. Assessing genetic risk and psychotic-like experiences (PLEs) in non-clinical populations and their associated neurobiological underpinnings can offer valuable insights into symptom-associated brain mechanisms without the potential confounds of the effects of schizophrenia and its treatment. We leveraged a large population-based cohort (UKBiobank) including information on PLEs, polygenic risk scores for schizophrenia (PRSSZ) and multi-modal brain imaging in combination with network neuroscience. Morphometric (cortical thickness, volume) and water diffusion (fractional anisotropy) properties of the regions and pathways belonging to the salience, default-mode and central-executive networks were computed. We hypothesized that these anatomical concomitants of functional dysconnectivity would be negatively associated with PRSSZ and PLEs. PRSSZ was significantly associated with a latent measure of cortical thickness across the salience network (r = -0.069, p = 0.010) and PLEs showed a number of significant associations with properties of the salience and default mode networks (involving the insular cortex, supramarginal gyrus and pars orbitalis, pFDR < 0.050); with the cortical thickness of the insula largely mediating the relationship between PRSSZ and auditory hallucinations. These results are consistent with the hypothesis that higher genetic liability for schizophrenia is related to subtle disruptions in brain structure and predisposes to PLEs even among healthy participants. In addition, our study suggests that networks engaged during auditory hallucinations show structural associations with PLEs in the general population.

neuroscience↗

bioRxiv2019

Stratifying major depressive disorder by polygenic risk for schizophrenia in relation to structural brain measures

BackgroundSubstantial clinical heterogeneity of major depressive disorder (MDD) suggests it may group together individuals with diverse aetiologies. Identifying distinct subtypes should lead to more effective diagnosis and treatment, while providing more useful targets for further research. Genetic and clinical overlap between MDD and schizophrenia (SCZ) suggests an MDD subtype may share underlying mechanisms with SCZ.\n\nMethodsThe present study investigated whether a neurobiologically distinct subtype of MDD could be identified by SCZ polygenic risk score (PRS). We explored interactive effects between SCZ PRS and MDD case/control status on a range of cortical, subcortical and white matter metrics among 2,370 male and 2,574 female UK Biobank participants.\n\nResultsThere was a significant SCZ PRS by MDD interaction for rostral anterior cingulate cortex (RACC) thickness ({beta}=.191, q=.043). This was driven by a positive association between SCZ PRS and RACC thickness among MDD cases ({beta}=.098, p=.026), compared to a negative association among controls ({beta}=-.087, p=.002). MDD cases with low SCZ PRS showed thinner RACC, although the opposite difference for high-SCZ-PRS cases was not significant. There were nominal interactions for other brain metrics, but none remained significant after correcting for multiple comparisons.\n\nConclusionsOur significant results indicate that MDD case-control differences in RACC thickness vary as a function of SCZ PRS. Although this was not the case for most other brain measures assessed, our specific findings still provide some further evidence that MDD in the presence of high genetic risk for SCZ is subtly neurobiologically distinct from MDD in general.

neuroscience↗

bioRxiv2019

Expression quantitative trait loci-derived scores and white matter microstructure in UK Biobank: a novel approach to integrating genetics and neuroimaging

BackgroundExpression quantitative trait loci (eQTL) are genetic variants associated with gene expression. Using genome-wide genotype data, it is now possible to impute gene expression using eQTL mapping efforts. This approach can be used to analyse previously unexplored relationships between gene expression and heritable in vivo measures of human brain structural connectivity.\n\nMethodsUsing large-scale eQTL mapping studies, we computed 6,457 gene expression scores (eQTL scores) using genome-wide genotype data in UK Biobank, where each score represents a genetic proxy measure of gene expression. These scores were then tested for associations with two diffusion tensor imaging measures, fractional anisotropy (NFA=14,518) and mean diffusivity (NMD=14,485), representing white matter structural integrity.\n\nResultsWe found FDR-corrected significant associations between 8 eQTL scores and structural connectivity phenotypes, including global and regional measures ({beta}absolute FA=0.0339-0.0453; MD=0.0308-0.0381) and individual tracts ({beta}absolute FA=0.0320-0.0561; MD=0.0295-0.0480). The loci within these eQTL scores have been reported to regulate expression of genes involved in various brain-related processes and disorders, such as neurite outgrowth and Parkinsons disease (DCAKD, SLC35A4, SEC14L4, SRA1, NMT1, CPNE1, PLEKHM1, UBE3C).\n\nDiscussionOur findings indicate that eQTL scores are associated with measures of in vivo brain connectivity and provide novel information not previously found by conventional genome-wide association studies. Although the role of expression of these genes regarding white matter microstructural integrity is not yet clear, these findings suggest it may be possible, in future, to map potential trait- and disease-associated eQTL to in vivo brain connectivity and better understand the mechanisms of psychiatric disorders and brain traits, and their associated imaging findings.

bioinformatics↗

bioRxiv2019

White matter microstructure and its relation to longitudinal measures of depressive symptoms in mid-late life

BackgroundStudies of white matter microstructure in depression typically show alterations in depressed individuals, but they are frequently limited by small sample sizes and the absence of longitudinal measures of depressive symptoms. Depressive symptoms are however dynamic, and understanding the neurobiology of different trajectories could have important clinical implications.\n\nMethodsWe examined associations between current and longitudinal measures of depressive symptoms and white matter microstructure (Fractional Anisotropy, FA; Mean Diffusivity; MD) in the UK Biobank Imaging study. Depressive symptoms were assessed on 2-4 occasions over 5.9 to 10.7 years (on N=18,959 individuals on at least two occasions, N=4,444 on four occasions) from which we derived four measures of depressive symptomatology; (i) cross-sectional measure at the time of scan (imaging was conducted at a single time point), and three longitudinal measures, (ii) trajectory (iii) mean and (iv) intra-subject variance over time.\n\nResultsDecreased white matter microstructure in the anterior thalamic radiation demonstrated significant associations across all four measures of depressive symptoms (for MD: {beta}=0.020 to 0.029, pcorr<0.030). The greatest effect sizes were however seen between decreasing white matter integrity and increasing longitudinal progression of symptoms (for MD: {beta}=0.030 to 0.040, pcorr<0.049). Cross-sectional symptom severity was particularly associated with decreased white matter integrity in association fibres and thalamic radiations (MD: {beta}=0.015 to 0.039, pcorr<0.041). While greater mean and within subject variance of depressive symptoms were mainly associated with decreased white matter microstructure within projection fibres (MD: {beta}=0.019 to 0.029, pcorr<0.044).\n\nConclusionsThese findings indicate shared and differential neurobiological associations with severity, course and intra-subject variability of depressive symptoms. This enriches our understanding of the neurobiology underlying dynamic features of the disorder.

neuroscience↗

bioRxiv2019

A phenome-wide association and Mendelian Randomisation study of polygenic risk for depression in UK Biobank

Depression is the leading cause of worldwide disability but there remains considerable uncertainty regarding its neural and behavioural associations. Depression is known to be heritable with a polygenic architecture, and results from genome-wide associations studies are providing summary statistics with increasing polygenic signal that can be used to estimate genetic risk scores for prediction in independent samples. This provides a timely opportunity to identify traits that are associated with polygenic risk of depression in the large and consistently phenotyped UK Biobank sample. Using the Psychiatric Genomics Consortium (PGC), 23andMe and non-imaging UK Biobank datasets as reference samples, we estimated polygenic risk scores for depression (depression-PRS) in a discovery sample of 10,674 people and a replication sample of 11,214 people from the UK Biobank Imaging Study, testing for associations with 210 behavioural and 278 neuroimaging phenotypes. In the discovery sample, 93 traits were significantly associated with depression-PRS after multiple testing correction. Among these, 92 traits were in the same direction, and 69 were significant in the replication analysis. For imaging traits that replicated across samples, higher depression-PRS was associated with lower global white matter microstructure, association-fibre and thalamic-radiation microstructural integrity (absolute {beta}: 0.023 to 0.040, PFDR: 0.045 to 3.92x10-4). Mendelian Randomisation analysis showed a causal effect of liability to depression on these structural brain measures ({beta}: 0.125 to 0.707, pFDR<0.048). Replicated behavioural traits that positively associated with depression-PRS included sleep problems, smoking status, measures of pain and stressful life experiences, and those negatively associated with depression-PRS included subjective ratings of physical health (absolute {beta}: 0.014 to 0.180, PFDR: 0.046 to 8.54x10-15). Effect of depression PRS on mental health in the presence of reported childhood trauma, stressful life events and those living in more socially deprived areas showed increased variance explained by 1.42 - 4.08 times (pFDR for their interaction with depression-PRS: 0.049 to 0.003). Overall, the present study revealed replicable associations between depression-PRS and white matter microstructure that appeared to be a causal consequence of liability to depression. Analyses provided further evidence that greater effects of polygenic risk of depression are found in individuals exposed to risk-conferring environments.

genetics↗

bioRxiv2019

An epigenome-wide association study of sex-specific chronological ageing

IntroductionAdvanced age is associated with cognitive and physical decline, and is a major risk factor for a multitude of disorders including neurodegenerative diseases such as Alzheimers disease. There is also a gap in life-expectancy between males and females. DNA methylation differences have been shown to be associated with both age and sex. Here, we investigate age-by-sex differences in DNA methylation in an unrelated cohort of 2,586 individuals between the ages of 18 and 87 years.\n\nMethodsGenome-wide DNA methylation was measured on the Illumina HumanMethylationEPIC beadchip in a subset of unrelated individuals from the Generation Scotland cohort. Mixed linear model-based analyses were performed to investigate the relationship between DNA methylation and an interaction term between age and sex, as well as chronological age.\n\nResultsAt a genome-wide significance level of P < 3.6 x 10-8, 14 loci were associated with the age-by-sex interaction term, the majority of which were X-linked (n = 12). Seven of these loci were annotated to genes. The site with the greatest difference mapped to GAGE10, an X-linked gene. Here, DNA methylation levels remained stable across the male adult age range (DNA methylation x age r = 0.02), but decreased across female adult age range (DNA methylation x age r = -0.61). The seven age-by-sex-associated genes were enriched among differentially-expressed genes in lung, liver, testis and blood.\n\nConclusionThe majority of differences in age-associated DNA methylation trajectories between sexes are present on the X-chromosome. Several of these differences occur within genes which have implicated in multiple cancers, schizophrenia and systemic lupus erythematosus.

genomics↗

bioRxiv2019

Trajectory of brain maturation in adolescent individuals at familial risk of mood disorder

BackgroundAccelerated biological ageing has been proposed as a mechanism underlying mood disorder, but has been predominantly studied cross-sectionally in adult populations. It remains unclear whether differential ageing/maturation trajectories emerge earlier in life, in particular during the neurodevelopmental period of adolescence, and whether they are associated with onset of mood disorder and/or presence of familial risk. MethodsParticipants were young individuals (16-25 years) from the prospective longitudinal Scottish Bipolar Family Study (SBFS) with and without family history of mood disorder. All were well at time of recruitment. Implementing a structural MRI-based brain age prediction model, individual maturational trajectories were captured by the difference between predicted brain age and chronological age (brain-PAD) at baseline and two-year follow-up. Based on clinical assessment at follow-up, individuals were categorised into three groups: (i) controls who remained well (C-well, n=94), (ii) high familial risk who remained well (HR-well, n=73) and (iii) high familial risk who developed a mood disorder (HR-MD, n=38). ResultsResults showed no differences in brain-PAD between groups at baseline or follow-up. However, we found negative trajectories of brain-PAD for HR-MD versus C-well ({beta}= -0.68 years, p<.001) and versus HR-well ({beta}= -0.38 years, p=.01), and for HR-well versus C-well ({beta}= -0.30 years, p=.03). ConclusionsThese findings suggest that within young individuals, onset of mood disorder and familial risk may be associated with a deceleration in brain maturation trajectory. However, without significantly differential status of brain maturation at follow-up, extended longitudinal research will need to show whether this marks the emergence of maturational lag.

neuroscience↗