Familial influences on Neuroticism and Education in the UK Biobank

Cheesman, Rosa; Coleman, Jonathan R. I.; Rayner, Christopher; Purves, Kirstin; Morneau‐Vaillancourt, Geneviève; Glanville, Kylie P.; Choi, Shing Wan; Breen, Gerome; Eley, Thalia C.

doi:10.1101/582627

Cited by 4 publications

(5 citation statements)

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“…as part of the questionnaire presented on touch screen tablets. A continuous variable "years of education" was defined according to the ISCED categories (Cheesman et al, 2020;Lee et al, 2018); for details see Table A.3. Missingness in the cognitive task variable resulted in 923 exclusions with further 27 exclusions due to missingness in the education variable.…”

Section: Cognitive Testingmentioning

confidence: 99%

Spatial distribution and cognitive impact of cerebrovascular risk-related white matter hyperintensities

Veldsman

Kindalova

Kosmidis

et al. 2020

Preprint

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12Objectives White matter hyperintensities (WMHs) are considered macroscale markers of cerebrovascular 13 burden and are associated with increased risk of vascular cognitive impairment and dementia. However, 14 the spatial location of WMHs has typically been considered in broad categories of periventricular versus 15 deep white matter. The spatial distribution of WHMs associated with individual cerebrovascular risk 16 factors (CVR), controlling for frequently comorbid risk factors, has not been systematically investigated 17 at the population level in a healthy ageing cohort. Furthermore, there is an inconsistent relationship 18 between total white matter hyperintensity load and cognition, which may be due to the confounding of 19 several simultaneous risk factors in models based on smaller cohorts. 20 Methods We examined trends in individual CVR factors on total WMH burden in 13,680 individuals 21 (aged 45-80) using data from the UK Biobank. We estimated the spatial distribution of white matter 22 hyperintensities associated with each risk factor and their contribution to explaining total WMH load 23 using voxel-wise probit regression and univariate linear regression. Finally, we explored the impact of 24 CVR-related WMHs on speed of processing using regression and mediation analysis. 25 Results Contrary to the assumed dominance of hypertension as the biggest predictor of WMH burden, 26 we show associations with a number of risk factors including diabetes, heavy smoking, APOE ε4/ε4 status 27 and high waist-to-hip ratio of similar, or greater magnitude to hypertension. The spatial distribution of 28 WMHs varied considerably with individual cerebrovascular risk factors. There were independent effects 29 of visceral adiposity, as measured by waist-to-hip ratio, and carriage of the APOE ε4 allele in terms of the 30 unique spatial distribution of CVR-related WMHs. Importantly, the relationship between total WMH 31 load and speed of processing was mediated by waist-to-hip ratio suggesting cognitive consequences to 32 WMHs associated with excessive visceral fat deposition. 33Conclusion Waist-to-hip ratio, diabetes, heavy smoking, hypercholesterolemia and homozygous APOE 34 ε4 status are important risk factors, beyond hypertension, associated with WMH total burden and warrant 35 careful control across ageing. The spatial distribution associated with different risk factors may provide 36 important clues as to the pathogenesis and cognitive consequences of WMHs. High waist-to-hip ratio is 37 a key risk factor associated with slowing in speed of processing. With global obesity levels rising, focused 38 management of visceral adiposity may present a useful strategy for the mitigation of cognitive decline in 39 ageing. 40 1 42White matter hyperintensities (WMHs) of presumed vascular origin (Wardlaw et al., 2013) are widely 43 recognised as an indicator of poor brain health (Wardlaw et al., 2015). Age remains the strongest pre-44 dictor for the presence of WMHs. However, the total burden of WMHs...

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Section: Cognitive Testingmentioning

confidence: 99%

Spatial distribution and cognitive impact of cerebrovascular risk-related white matter hyperintensities

Veldsman

Kindalova

Kosmidis

et al. 2020

Preprint

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“…Compared with individuals in high relative amplitude category, those in low relative amplitude category had a significantly higher risk of all-cause dementia (HR 2. 25 S1). And the results remained robust and significant in Model 2 (Fig.…”

Section: Relative Amplitude and Risk Of Brain Disorder Incidentsmentioning

confidence: 98%

“…Townsend social deprivation scores were derived based on the postcode of residence, with negative scores reflecting relatively greater affluence [24]. Educational attainment was first converted into years of education according to the self-reported educational qualification from a touchscreen questionnaire [25] and then recoded into an ordinal scale from 0 to 10 (e.g., 0: 0 years; 10: >24 years) [26]. More details can be found in previous studies.…”

Section: Covariatesmentioning

confidence: 99%

Association of circadian rhythms with brain disorder incidents: a prospective cohort study of 72242 participants

Chen

Deng

et al. 2022

Transl Psychiatry

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Circadian rhythm disruption (CRD) is a shared characteristic of various brain disorders, such as Alzheimer’s disease (AD), Parkinson’s disease (PD), and major depression disorder (MDD). Disruption of circadian rhythm might be a risk factor for brain disorder incidents. From 7-day accelerometry data of 72,242 participants in UK Biobank, we derived a circadian relative amplitude variable, which to some extent reflected the degree of circadian rhythm disruption. Records of brain disorder incidents were obtained from a wide range of health outcomes across self-report, primary care, hospital inpatient data, and death data. Using multivariate Cox proportional hazard ratio regression, we created two models adjusting for different covariates. Then, linear correlations between relative amplitude and several brain morphometric measures were examined in participants with brain MRI data. After a median follow-up of around 6.1 years, 72,242 participants were included in the current study (female 54.9%; mean age 62.1 years). Individuals with reduced relative amplitude had increasing risk of all-cause dementia (Hazard ratio 1.23 [95% CI 1.15 to 1.31]), PD (1.33 [1.25 to 1.41]), stroke (1.13 [1.06 to 1.22]), MDD (1.18 [1.13 to 1.23]), and anxiety disorder (1.14 [1.09 to 1.20]) in fully adjusted models. Additionally, significant correlations were found between several cortical regions and white matter tracts and relative amplitude that have been linked to dementia and psychiatric disorders. We confirm CRD to be a risk factor for various brain disorders. Interventions for regulating circadian rhythm may have clinical relevance to reducing the risk of various brain disorders.

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“…For the UK Biobank analyses, we used six continuous traits (Body Fat, Mean Corpuscular Haemoglobin, Neuroticism, Hell Bone Mineral Density, Monocyte Count, and Sitting Height) with h 2 SN P > 5% (22) and data on > 4,500 sibling pairs (sibling-pairs defined as having kinship coefficient 0.18 -0.35 and > 0.1% SNPs with 0 IBD to distinguish from parent-offspring (23,24). Outliers with absolute trait value > 6 standard deviations from the mean were removed and then cohort-wide trait values were standardised using a rank-based inverse normal transformation and adjusted for age, sex, recruitment centre, batch covariates and the first 40 principle components.…”

Section: Application To Uk Biobank Datamentioning

confidence: 99%

Sibling Similarity Can Reveal Key Insights Into Genetic Architecture

Souaiaia

Hoggart

et al. 2023

Preprint

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The use of siblings to infer the factors influencing complex traits has been a cornerstone of quantitative genetics. Here we utilise siblings for a novel application: the identification of genetic architecture, specifically that in individuals with extreme trait values (e.g. in the top 1%). Establishing genetic architecture in these individuals is important because they are at greatest risk of disease and are most likely to harbour rare variants of large effect due to natural selection. We develop a theoretical framework that derives expected trait distributions of siblings based on an index sibling's trait value and trait heritability. This framework is used to develop statistical tests that can infer complex genetic architecture in trait tails, distinguishing between polygenic, de novo and Mendelian tail architecture. We apply our tests to UK Biobank data here, while they can be used to infer genetic architecture in any cohort or health registry that includes siblings, without requiring genetic data. We describe how our approach has the potential to help disentangle the genetic and environmental causes of extreme trait values, to identify individuals likely to carry pathogenic variants for follow-up clinical genetic testing, and to improve the design and power of future sequencing studies to detect rare variants.

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Familial influences on Neuroticism and Education in the UK Biobank

Cited by 4 publications

References 50 publications

Spatial distribution and cognitive impact of cerebrovascular risk-related white matter hyperintensities

Spatial distribution and cognitive impact of cerebrovascular risk-related white matter hyperintensities

Association of circadian rhythms with brain disorder incidents: a prospective cohort study of 72242 participants

Sibling Similarity Can Reveal Key Insights Into Genetic Architecture

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