Biology subjects

McIntosh, A. M.

Publications and source records attributed to McIntosh, A. M..

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↗

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

A novel framework to build saliva-based DNA methylation biomarkers: quantifying systemic chronic inflammation as a case study

Accessible and non-invasive biomarkers that measure human ageing processes and the risk of developing age-related disease are paramount in preventative healthcare. In this study, we describe a novel framework to train saliva-based DNA methylation (DNAm) biomarkers that are reproducible and biologically interpretable. By leveraging a reliability dataset with replicates across tissues, we demonstrate that it is possible to transfer knowledge from blood DNAm data to saliva DNAm data using DNAm proxies of blood proteins (EpiScores). We then apply these methods to create a new saliva-based epigenetic clock (InflammAge) that quantifies systemic chronic inflammation (SCI) in humans. Using a large blood DNAm human cohort with linked electronic health records and over 18,000 individuals (Generation Scotland), we demonstrate that InflammAge significantly associates with all-cause mortality, disease outcomes, lifestyle factors and immunosenescence; in many cases outperforming the widely used SCI biomarker C-reactive protein (CRP). We propose that our biomarker discovery framework and InflammAge will be useful to improve our understanding of the molecular mechanisms underpinning human ageing and to assess the impact of gero-protective interventions.

bioinformatics↗

bioRxiv2023

Methylome-wide association studies of traumatic physical injury identifies differential DNA methylation of synaptic plasticity and GABAergic-signalling.

Traumatic physical injury is often associated with psychological trauma and is a risk factor for major depressive disorder (MDD). In Generation Scotland traumatic injury was significantly associated with recurrent major depression (OR = 2.10, 95% CI 1.33-3.33, PLJ=LJ0.0016). and schizotypal symptoms, particularly disorganised thought ({beta}LJ=LJ0.111, 95% CI 0.049-0.177, PLJ=LJ0.0004). We performed methylome-wide analyses of traumatic injury in individuals with MDD and controls separately to investigate the link between traumatic injury and MDD. Nominally significant differences in differential DNA methylation between MDD and control groups were identified at 40 003 CpG sites (p < 0.05). Individuals with recurrent MDD showed significantly higher levels of DNA methylation associated with traumatic injury at CpG sites at the first exon and lower levels at exon boundaries, this was significant different to the association pattern at these sites in controls (mean difference in M-value = 0.0083, P = 21.1x10-10, and -0.0125, P = 2.1x10-174, respectively). Analyses at the level of CpG site, genes and gene ontologies implicated dysregulation of processes related to synaptic plasticity, including dendrite development, excitatory synapse and GABAergic signalling (normalised enrichment values > 2, FDR q-values < 0.01). Enrichment analyses for regional brain-expression in the recurrent MDD group highlight the limbic lobe and supraoptic nuclei (recurrent MDD FWER = 0.028 and 0.034, respectively). These results suggest that traumatic injury is associated with patterns of DNA methylation differentially in individuals with MDD compared to controls, highlighting the need for novel analysis approaches.

genomics↗

bioRxiv2023

The effects of CYP2C19 metaboliser status on DNA methylation

CYP2C19 metabolises many medications e.g., antidepressants, proton pump inhibitors and its enzymatic activity can be inferred from genetic variants within its encoding gene, linking to the efficacy of drug treatments and their side effects. It is however unclear if enzymatic activity is associated with local or widespread differences in DNA methylation. DNA methylation differences associated with CYP2C19 metabolising status may also have the potential to reveal interacting genes and pathways that underly CYP2C19 effects on drug response and health consequences. A methylome-wide association study was conducted in the Generation Scotland cohort (n=18,396) to investigate the linear and non-linear effects of CYP2C19 metaboliser status on genome-wide DNA methylation. Pathway enrichment analysis was conducted for the cytosine-guanine dinucleotide (CpG) probes significantly associated with CYP2C19 metaboliser status. We examined whether the associations between CYP2C19 metaboliser status and DNA methylation were independent of theuse of drugs that are inducers, inhibitors, or substrates of the CYP2C19 enzyme by interaction analysis. Forty-eight CpG probes were significantly associated with both linear and quadratic terms of CYP2C19 metaboliser status (PBonferroni < 0.05). These CpG are annotated to genes involving drug metabolism, inflammation, lipid level, and Type 2 diabetes. Pathway enrichment analysis showed enrichment in biological processes involving metabolic activities and the Cytochrome P450 pathway. However, DNA methylation signals associated with CYP2C19 metaboliser statusdid not vary by CYP2C19-related medication use. This research suggests that genetically-determined CYP2C19 metaboliser status is associated with both local and distal DNA methylation. These associations are independent of whether individuals were receiving drugs that are related to this enzyme.

pharmacology and toxicology↗

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↗

bioRxiv2023

Methodological Considerations When Using Polygenic Scores to Explore Parent-Offspring Genetic Nurturing Effects

BackgroundResearch has begun to explore the effects of parental genetic nurturing on offspring outcomes using polygenic scores (PGSs). However, there are concerns regarding potential biases due to confounding when mediating parental phenotypes are included. MethodsDepression, educational attainment and height PGSs were generated for 2680 biological parent-offspring trios using genome-wide association study (GWAS) meta-analysis summary statistics in a large population study: Generation Scotland. Regression and pathway models were estimated incorporating PGSs for both parents and offspring to explore direct (offspring PGS) and genetic nurturing (parental PGS) effects on psychological distress, educational attainment and height. Genetic nurturing via parental phenotypes were incorporated into the models. To explore sources of bias we conducted simulation analyses of 10,000 trios using combinations of PGS predictive accuracy and accounted variance. ResultsModels incorporating both offspring and parental PGSs suggested positive parental genetic nurturing effects on offspring educational attainment, but not psychological distress or height. In contrast, models additionally incorporating parental phenotypic information suggested positive parent phenotype mediated genetic nurturing effects were at play for all phenotypes explored as well as negative residual genetic nurturing effects for height. 10,000 parent-offspring trio effects (without genetic nurturing effects) were simulated. Simulations demonstrated that models incorporating parent and offspring PGSs resulted in genetic nurturing effects that were unbiased. However, adding parental phenotypes as mediating variables results in biased positive estimates of parent phenotype mediated genetic nurturing effects and negative estimates of residual genetic nurturing effects. Biased effects increased in magnitude as PGS accuracy and accounted variance decreased. These biases were only eliminated when PGSs were simulated to capture the entirety of trait genetic variance. ConclusionResults suggest that in the absence of PGSs that capture all genetic variance, parental phenotypes act as colliders in the same way as heritable environments. Relatively simple models combining parental and offspring PGSs can be used to detect genetic nurturing effects in complex traits. However, our findings suggest alternative methods should be utilised when aiming to identify mediating phenotypes and potentially modifiable parental nurturing effects.

genomics↗

bioRxiv2022

Refining epigenetic prediction of chronological and biological age

Epigenetic clocks can track both chronological age (cAge) and biological age (bAge). The latter is typically defined by physiological biomarkers and risk of adverse health outcomes, including all-cause mortality. As cohort sample sizes increase, estimates of cAge and bAge become more precise. Here, we aim to refine predictors and improve understanding of the epigenomic architecture of cAge and bAge. First, we perform large-scale (N = 18,413) epigenome-wide association studies (EWAS) of chronological age and all-cause mortality. Next, to improve cAge prediction, we use methylation data from 24,673 participants from the Generation Scotland (GS) study, the Lothian Birth Cohorts (LBC) of 1921 and 1936 and 8 publicly available datasets. Through the inclusion of linear and non-linear age-CpG associations from the EWAS, feature pre-selection/dimensionality reduction in advance of elastic net regression, and a leave-one-cohort-out (LOCO) cross validation framework, we arrive at an improved cAge predictor (median absolute error = 2.3 years across 10 cohorts). In addition, we train a predictor of bAge on 1,214 all-cause mortality events in GS, based on epigenetic surrogates for 109 plasma proteins and the 8 component parts of GrimAge, the current best epigenetic predictor of all-cause mortality. We test this predictor in four external cohorts (LBC1921, LBC1936, the Framingham Heart Study and the Womens Health Initiative study) where it outperforms GrimAge in its association to survival (HRGrimAge = 1.47 [1.40, 1.54] with p = 1.08 x 10-52, and HRbAge = 1.52 [1.44, 1.59] with p = 2.20 x 10-60). Finally, we introduce MethylBrowsR, an online tool to visualize epigenome-wide CpG-age associations.

genomics↗

bioRxiv2022

Characterisation and Genetic Architecture of Major Depressive Disorder Subgroups Defined by Weight and Sleep Changes

Major depressive disorder, MDD, is highly heterogeneous and thus subgroups with different underlying aetiologies have been postulated. The aim of this work is to further characterise depression subgroups defined using sleep and weight changes. Probable lifetime MDD cases (n = 26,662) from the UK Biobank were stratified into three subgroups defined by self-reported weight and sleep changes during worst episode: (i) increased weight and sleep ({uparrow}WS), (ii) decreased weight and sleep ({downarrow}WS) and (iii) the remaining uncategorised individuals. Analyses compared the depression characteristics, mental health scores, neurological and inflammatory comorbidities and genetic architecture between subgroups and with 50,147 controls from UK Biobank. In contrast to {uparrow}WS depression, {downarrow}WS depression had a higher age of onset and lower proportion reporting countless or continuous episodes compared to uncategorised individuals. The {downarrow}WS depression also had a higher wellbeing score than the other subgroups. Analyses of subgroup comorbidities identified a novel association between {uparrow}WS depression and epilepsy. Subgroup-specific GWAS identified three genome-wide significant loci associated with {uparrow}WS in genes previously associated with immunometabolic traits and response to anticonvulsants. The effect of BMI adjustment in the genetic analyses of the subgroups and using broader weight-only definitions were also examined. The findings provide further evidence for differences in the characteristics and genetic architecture of depression subgroups defined by sleep and weight change and highlight the importance of dividing non-{uparrow}WS individuals into {downarrow}WS and uncategorised subgroups in analyses, as {downarrow}WS symptoms may identify a more acute depression subgroup.

genomics↗

bioRxiv2022

Genetic control of KRAB-ZFP genes explains distal CpG-site methylation which associates with human disease phenotypes.

Kruppel-associated box zinc finger proteins (KRAB-ZFPs) are the largest gene family of transcriptional regulators in higher vertebrates. We have developed a method for inferring these factors DNA binding-sites in vivo. We achieved this by combining the genome-wide association of methylation at 573,027 CpG-sites (meQTL) in blood and KRAB-ZFP RNA expression changes (eQTL). This methods efficacy is clearly demonstrated by showing that the CpG-sites whose methylation is affected by KRAB-ZFP expression changes occur preferentially near KRAB-ZFP binding-sites in the HEK293T cell line. We found such an enrichment to be significant for 31% of factors tested. In addition, we found that binding-sites of many KRAB-ZFPs are significantly enriched (or sometimes depleted) in disease-associated CpG-sites. In total, 9% (11 of 125) of the traits tested, and 14% (32 of 222) of the factors (KRAB-ZFPs and TIF1-beta) tested, were enriched or depleted in one or more trait-factor pairing. Rheumatoid arthritis and human immunodeficiency virus (HIV) infection were associated with the largest number of KRAB-ZFP enrichments. There have been variable reports on the effects on HIV infection dynamics of transcription intermediary factor 1-beta (TIF1-beta, also known as KAP1 or TRIM28), a nuclear corepressor for KRAB-ZFPs. We provide evidence that KRAB-ZFP-independent effects of TIF1-beta are responsible for the decreased methylation of CpG-sites within its binding-sites that is observed in HIV infection. In conclusion, KRAB-ZFPs often affect CpG-site methylation within the proximity of their binding-sites, and CpG-sites within the binding-sites of KRAB-ZFPs are enriched for association with many traits, including rheumatoid arthritis and HIV infection.

genomics↗

bioRxiv2021

Local CpG density affects the trajectory of age-associated epigenetic changes

DNA methylation is an epigenetic mark associated with gene repression and genome stability. Its pattern in the genome is disrupted with age and these changes can be used to statistically predict age with epigenetic clocks. Rates of aging inferred from these clocks correlate with human health. However, the molecular mechanisms underpinning age-associated DNA methylation changes are unknown. Local DNA sequence plays a strong role in programming DNA methylation levels at individual loci independently of age, but its influence on age-associated DNA methylation changes is unknown. We analysed longitudinal human DNA methylation trajectories at 345,895 CpGs from 600 individuals aged between 67 and 80 to understand the factors responsible for age-associated epigenetic changes at individual CpGs in the genome. We show that changes in methylation with age are especially apparent at 8,322 low CpG density loci. Using SNP data from the same individuals we demonstrate that DNA methylation trajectories are affected by local sequence polymorphisms at 1,487 loci with low CpG density. More generally, we find that local CpG density is a strong determinant of a CpGs methylation trajectory and that CpGs located in low CpG density regions are particularly prone to change. Overall, our results demonstrate that local DNA sequence influences age-associated DNA methylation changes in humans in vivo. We suggest that this occurs because interactions between CpGs reinforce maintenance of methylation patterns in CpG dense regions.

genetics↗

bioRxiv2020

DNA methylation proxies for 16 plasma proteins predict the incidence of 7 leading causes of morbidity

Protein biomarkers have been identified across many age-related morbidities. However, characterising epigenetic influences could further inform disease predictions. Here, we leverage epigenome-wide data to study links between the DNAm signatures of the circulating proteome and incident diseases. Using data from four cohorts, we trained and tested epigenetic scores (EpiScores) for 953 plasma proteins, identifying 109 scores that explained between 1% and 58% of the variance in protein levels after adjusting for known protein quantitative trait loci (pQTL) genetic effects. By projecting these EpiScores into an independent sample, (Generation Scotland; n=9,537) and relating them to incident morbidities over a follow-up of 14 years, we uncovered 137 EpiScore - disease associations. These associations were largely independent of immune cell proportions, common lifestyle and health factors and biological aging. Notably, we found that our diabetes-associated EpiScores highlighted previous top biomarker associations from proteome-wide assessments of diabetes. These EpiScores for protein levels can therefore be a valuable resource for disease prediction and risk stratification.

genomics↗

bioRxiv2020

A comparison of blood and brain-derived ageing and inflammation-related DNA methylation signatures and their association with microglial burdens

Inflammation and ageing-related DNA methylation patterns in the blood have been linked to a variety of morbidities, including cognitive decline and neurodegenerative disease. However, it is unclear how these blood-based patterns relate to patterns within the brain, and how each associates with central cellular profiles. In this study, we profiled DNA methylation in both the blood and in five post-mortem brain regions (BA17, BA20/21, BA24, BA46 and hippocampus) in 14 individuals from the Lothian Birth Cohort 1936. Microglial burdens were additionally quantified in the same brain regions. DNA methylation signatures of five epigenetic ageing biomarkers ( epigenetic clocks), and two inflammatory biomarkers (DNA methylation proxies for C-reactive protein and interleukin-6) were compared across tissues and regions. Divergent correlations between the inflammation and ageing signatures in the blood and brain were identified, depending on region assessed. Four out of the five assessed epigenetic age acceleration measures were found to be highest in the hippocampus ({beta} range=0.83-1.14, p[≤]0.02). The inflammation-related DNA methylation signatures showed no clear variation across brain regions. Reactive microglial burdens were found to be highest in the hippocampus ({beta}=1.32, p=5x10-4); however, the only association identified between the blood- and brain-based methylation signatures and microglia was a significant positive association with acceleration of one epigenetic clock (termed DNAm PhenoAge) averaged over all five brain regions ({beta}=0.40, p=0.002). This work highlights a potential vulnerability of the hippocampus to epigenetic ageing and provides preliminary evidence of a relationship between DNA methylation signatures in the brain and differences in microglial burdens.

neuroscience↗

bioRxiv2020

Omics versus Questionnaires: Using methylation data to assess the environmental impact of smoking on obesity-related traits

Variation in obesity-related traits has a genetic basis with heritabilities between 40 and 70%. While the global obesity pandemic is usually associated with environmental changes related to lifestyle and socioeconomic changes, most genetic studies do not include all relevant environmental covariates, so genetic contribution to variation in obesity-related traits cannot be accurately assessed. Some studies have described interactions between a few individual genes linked to obesity and environmental variables but there is no agreement on their total contribution to differences between individuals. Here we compared self-reported smoking data and a methylation-based proxy to explore the effect of smoking and genome-by-smoking interactions on obesity related traits from a genome-wide perspective to estimate the amount of variance they explain. Our results indicate that exploiting omic measures can improve models for complex traits such as obesity and can be used as a substitute for, or jointly with, environmental records to better understand causes of disease.

genomics↗

bioRxiv2020

Epigenetic clocks predict prevalence and incidence of leading causes of death and disease burden

Individuals of the same chronological age display different rates of biological ageing. A number of measures of biological age have been proposed which harness age-related changes in DNA methylation profiles. These include methylation-based predictors of chronological age (HorvathAge, HannumAge), all-cause mortality (DNAm PhenoAge, DNAm GrimAge) and telomere length (DNAm Telomere Length). In this study, we test the association between these epigenetic markers of ageing and the prevalence and incidence of the leading causes of disease burden and mortality in high-income countries. Furthermore, we test the clocks relationships with phenotypic measures associated with these conditions, including spirometric and biochemical traits. We carry out these analyses in 9,537 individuals from the Generation Scotland: Scottish Family Health Study. We find that DNAm GrimAge outperforms other epigenetic clocks in its associations with self-report disease prevalence and related clinical traits. DNAm GrimAge associates with chronic obstructive pulmonary disease (COPD) prevalence (Odds Ratio = 3.29, P = 3.0 x 10-4) and pulmonary spirometry tests ({beta} = [-0.10 to -0.15], P = [1.4 x 10-4 to 1.4 x 10-6]) at study baseline after adjusting for possibly confounding risk factors including alcohol, body mass index, deprivation, education and smoking. After adjusting for these confounding risk factors, DNAm GrimAge, DNAm PhenoAge and DNAm Telomere Length, measured at study baseline, predict incidence of ICD-10-coded disease states including COPD, type 2 diabetes and cardiovascular disease after thirteen years of follow-up (Hazard Ratios = [0.80 (telomere length) to 2.19 (GrimAge)], P = [9.9 x 10-4, 1.9 x 10-14]). Our data show that despite accounting for several possible confounding variables, epigenetic markers of ageing predict incidence of common disease. This may have significant implications for their potential utility in clinical settings to complement gold-standard methods of clinical assessment and management.

genomics↗

bioRxiv2020

A genome-wide association study finds genetic variants associated with neck or shoulder pain in UK Biobank

BackgroundCommon types of musculoskeletal conditions include pain in the neck and shoulder areas. This study seeks to identify the genetic variants associated with neck or shoulder pain based on a genome-wide association approach using 203,309 subjects from the UK Biobank cohort and look for replication evidence from the Generation Scotland: Scottish Family Health Study (GS:SFHS) and TwinsUK. MethodsCases in the UK Biobank were determined by a question which asked the participants if they had experienced pain in the neck or shoulder in the previous month influencing daily activities. Controls were the UK Biobank participants who reported no pain anywhere in the last month. A genome-wide association study was performed adjusting for age, sex, BMI and 9 population principal components. Significant and independent genetic variants were then sent to GS:SFHS and TwinsUK for replication. ResultsWe identified 3 genetic loci that were associated with neck or shoulder pain in the UK Biobank samples. The most significant locus was in an intergenic region in chromosome 17, rs12453010, having P = 1.66 x 10-11. The second most significant locus was located in the FOXP2 gene in chromosome 7 with P = 2.38 x 10-10 for rs34291892. The third locus was located in the LINC01572 gene in chromosome 16 with P = 4.50 x 10-8 for rs62053992. In the replication stage, among 4 significant and independent genetic variants, rs2049604 in the FOXP2 gene and rs62053992 in the LINC01572 gene were weakly replicated in GS:SFHS (P = 0.0240 and P = 0.0202, respectively). None of the single nucleotide polymorphisms (SNPs) were replicated in the TwinsUK cohort (P > 0.05). ConclusionsWe have identified 3 loci associated with neck or shoulder pain in the UK Biobank cohort, two of which were weakly supported in a replication cohort. Further evidence is needed to confirm their roles in neck or shoulder pain. SignificanceThis is the first genome-wide association study on neck or shoulder pain. We have identified 3 genetic loci (an intergenic region in chromosome 17, the FOXP2 gene in chromosome 7, and the LINC01572 gene in chromosome 16) that are associated with neck or shoulder pain using the UK Biobank cohort, among which the FOXP2 gene and the LINC01572 gene were weakly replicated by the Generation Scotland: Scottish Family Health Study (P < 0.05). The SNP heritability was 0.11, indicating neck or shoulder pain is a heritable trait. The tissue expression analysis suggested that neck or shoulder pain was related to multiple brain tissues, indicating the involvement of neuron function. The results will inform further research in the characterisation of the mechanisms of neck or shoulder pain.

genetics↗

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

Subthreshold psychosis symptoms associated with molecular genetic risk in a population-based cohort: Findings from Generation Scotland

ImportanceSubthreshold psychosis symptoms in the general population may be associated with genetic risk for schizophrenia. In this analysis, empirically-derived symptom factor scores led to a detection of significant and robust polygenic signal.\n\nObjectiveThis study sought to optimize genetic association with data-driven symptom factor scores, accounting for cohort factor structure and sex differences.\n\nDesignEFA-derived symptom factor scores were regressed onto PRS for schizophrenia in models accounting for age and genetic ancestry principal components. Follow-up examination of symptom factor score associations with other related genetic risks included ADHD, autism, bipolar disorder, major depression, and neuroticism.\n\nParticipantsThis study examined the newly expanded symptom dataset from the Northern European ancestry cohort, Generation Scotland: Scottish Family Health Study (N = 9,105 individuals 18-65 years of age) comprising common variant and subthreshold psychosis symptom data. A total of 5,391 females and 3,713 males with age M[SD] = 45.2 [13] were included in the final analyses.\n\nMain Outcome and MeasureSubthreshold psychosis symptoms were measured using the Schizotypal Personality Questionnaire-Brief (SPQ-B). Primary phenotypic factor scores and genome-wide polygenic risk scores (PRS) reflected weighted sum scores and were examined as continuous measures. Polygenic risk scores were calculated from genome-wide association summary statistics using 7,358,674 imputed common genetic variants passing quality control.\n\nResultsIn males, symptom factor scores were positively associated with polygenic risk for schizophrenia alone and implicated primarily interpersonal/negative symptoms. In females, symptom factor scores were positively associated with polygenic risks for ADHD and autism but not schizophrenia. Scores were robustly associated with genetic risk for neuroticism across both males and females.\n\nConclusions and RelevanceThis study detected a significant association of subthreshold psychosis symptoms with genetic risk for schizophrenia and neuroticism in a population-based sample. Furthermore, important sex differences suggest a need for better understanding of schizophrenia risk assessment in females.\n\nKey PointsO_ST_ABSQuestionC_ST_ABSWhat molecular genetic risks are associated with subthreshold psychosis symptoms in the general population?\n\nFindingsIn a large population-based cohort (N = 9,084), significant associations of polygenic risks with symptoms were observed. Symptoms were associated with genetic risk for schizophrenia in males, for ADHD and autism spectrum disorder in females, and for neuroticism across both males and females.\n\nMeaningAssociations of genetic risk with symptoms in the general population are highly significant and suggest important sex differences.

genetics↗