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"Howard, David M."
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Association analysis in over 329,000 individuals identifies 116 independent variants influencing neuroticism
Neuroticism is a relatively stable personality trait characterized by negative emotionality (for example, worry and guilt)
1
; heritability estimated from twin studies ranges from 30 to 50%
2
, and SNP-based heritability ranges from 6 to 15%
3
–
6
. Increased neuroticism is associated with poorer mental and physical health
7
,
8
, translating to high economic burden
9
. Genome-wide association studies (GWAS) of neuroticism have identified up to 11 associated genetic loci
3
,
4
. Here we report 116 significant independent loci from a GWAS of neuroticism in 329,821 UK Biobank participants; 15 of these loci replicated at
P
< 0.00045 in an unrelated cohort (
N
= 122,867). Genetic signals were enriched in neuronal genesis and differentiation pathways, and substantial genetic correlations were found between neuroticism and depressive symptoms (
r
g
= 0.82, standard error (s.e.) = 0.03), major depressive disorder (MDD;
r
g
= 0.69, s.e. = 0.07) and subjective well-being (
r
g
= –0.68, s.e. = 0.03) alongside other mental health traits. These discoveries significantly advance understanding of neuroticism and its association with MDD.
Analysis of 329,000 individuals in the UK Biobank identifies 116 loci associated with neuroticism. Genes implicated are enriched in neuronal differentiation pathways, and genetic correlations between neuroticism and other mental health traits are elucidated.
Journal Article
Aquaman by Peter David
\"Here begins Arthur Curry's recollection of the epic journey that led him to become the mythical superhero we know as Aquaman. Since his dramatic debut in the 1940s, Aquaman has gone from admired hero to legendary icon. Able to breathe in both air and water, the King of the Seven Seas has fought villainy from the deepest depths of the oceans to the outer limits of the galaxy. He is unquestionably one of the greatest heroes the world has ever seen, but his rise to power was not easy.\"-- Provided by publisher.
Book
Epigenetic prediction of major depressive disorder
Variation in DNA methylation (DNAm) is associated with lifestyle factors such as smoking and body mass index (BMI) but there has been little research exploring its ability to identify individuals with major depressive disorder (MDD). Using penalised regression on genome-wide CpG methylation, we tested whether DNAm risk scores (MRS), trained on 1223 MDD cases and 1824 controls, could discriminate between cases (n = 363) and controls (n = 1417) in an independent sample, comparing their predictive accuracy to polygenic risk scores (PRS). The MRS explained 1.75% of the variance in MDD (β = 0.338, p = 1.17 × 10−7) and remained associated after adjustment for lifestyle factors (β = 0.219, p = 0.001, R2 = 0.68%). When modelled alongside PRS (β = 0.384, p = 4.69 × 10−9) the MRS remained associated with MDD (β = 0.327, p = 5.66 × 10−7). The MRS was also associated with incident cases of MDD who were well at recruitment but went on to develop MDD at a later assessment (β = 0.193, p = 0.016, R2 = 0.52%). Heritability analyses found additive genetic effects explained 22% of variance in the MRS, with a further 19% explained by pedigree-associated genetic effects and 16% by the shared couple environment. Smoking status was also strongly associated with MRS (β = 0.440, p ≤ 2 × 10−16). After removing smokers from the training set, the MRS strongly associated with BMI (β = 0.053, p = 0.021). We tested the association of MRS with 61 behavioural phenotypes and found that whilst PRS were associated with psychosocial and mental health phenotypes, MRS were more strongly associated with lifestyle and sociodemographic factors. DNAm-based risk scores of MDD significantly discriminated MDD cases from controls in an independent dataset and may represent an archive of exposures to lifestyle factors that are relevant to the prediction of MDD.
Journal Article
A meta-analysis of genome-wide association studies of epigenetic age acceleration
'Epigenetic age acceleration' is a valuable biomarker of ageing, predictive of morbidity and mortality, but for which the underlying biological mechanisms are not well established. Two commonly used measures, derived from DNA methylation, are Horvath-based (Horvath-EAA) and Hannum-based (Hannum-EAA) epigenetic age acceleration. We conducted genome-wide association studies of Horvath-EAA and Hannum-EAA in 13,493 unrelated individuals of European ancestry, to elucidate genetic determinants of differential epigenetic ageing. We identified ten independent SNPs associated with Horvath-EAA, five of which are novel. We also report 21 Horvath-EAA-associated genes including several involved in metabolism (NHLRC, TPMT) and immune system pathways (TRIM59, EDARADD). GWAS of Hannum-EAA identified one associated variant (rs1005277), and implicated 12 genes including several involved in innate immune system pathways (UBE2D3, MANBA, TRIM46), with metabolic functions (UBE2D3, MANBA), or linked to lifespan regulation (CISD2). Both measures had nominal inverse genetic correlations with father's age at death, a rough proxy for lifespan. Nominally significant genetic correlations between Hannum-EAA and lifestyle factors including smoking behaviours and education support the hypothesis that Hannum-based epigenetic ageing is sensitive to variations in environment, whereas Horvath-EAA is a more stable cellular ageing process. We identified novel SNPs and genes associated with epigenetic age acceleration, and highlighted differences in the genetic architecture of Horvath-based and Hannum-based epigenetic ageing measures. Understanding the biological mechanisms underlying individual differences in the rate of epigenetic ageing could help explain different trajectories of age-related decline.
Journal Article
An epidemiological study of season of birth, mental health, and neuroimaging in the UK Biobank
Environmental exposures during the perinatal period are known to have a long-term effect on adult physical and mental health. One such influential environmental exposure is the time of year of birth which affects the amount of daylight, nutrients, and viral load that an individual is exposed to within this key developmental period. Here, we investigate associations between season of birth (seasonality), four mental health traits ( n = 137,588) and multi-modal neuroimaging measures ( n = 33,212) within the UK Biobank. Summer births were associated with probable recurrent Major Depressive Disorder (β = 0.026, p corr = 0.028) and greater mean cortical thickness in temporal and occipital lobes (β = 0.013 to 0.014, p corr < 0.05). Winter births were associated with greater white matter integrity globally, in the association fibers, thalamic radiations, and six individual tracts (β = -0.013 to -0.022, p corr < 0.05). Results of sensitivity analyses adjusting for birth weight were similar, with an additional association between winter birth and white matter microstructure in the forceps minor and between summer births, greater cingulate thickness and amygdala volume. Further analyses revealed associations between probable depressive phenotypes and a range of neuroimaging measures but a paucity of interactions with seasonality. Our results suggest that seasonality of birth may affect later-life brain structure and play a role in lifetime recurrent Major Depressive Disorder. Due to the small effect sizes observed, and the lack of associations with other mental health traits, further research is required to validate birth season effects in the context of different latitudes, and by co-examining genetic and epigenetic measures to reveal informative biological pathways.
Journal Article
Local CpG density affects the trajectory and variance of age-associated DNA methylation changes
Background
DNA methylation is an epigenetic mark associated with the repression of gene promoters. Its pattern in the genome is disrupted with age and these changes can be used to statistically predict age with epigenetic clocks. Altered rates of aging inferred from these clocks are observed in human disease. However, the molecular mechanisms underpinning age-associated DNA methylation changes remain unknown. Local DNA sequence can program steady-state DNA methylation levels, but how it influences age-associated methylation changes is unknown.
Results
We analyze 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. We show that changes in methylation with age occur at 182,760 loci largely independently of variation in cell type proportions. These changes are especially apparent at 8322 low CpG density loci. Using SNP data from the same individuals, we demonstrate that methylation trajectories are affected by local sequence polymorphisms at 1487 low CpG density loci. More generally, we find that low CpG density regions are particularly prone to change and do so variably between individuals in people aged over 65. This differs from the behavior of these regions in younger individuals where they predominantly lose methylation.
Conclusions
Our results, which we reproduce in two independent groups of individuals, 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.
Journal Article
Quantifying the relative importance of genetics and environment on the comorbidity between mental and cardiometabolic disorders using 17 million Scandinavians
Mental disorders are leading causes of disability and premature death worldwide, partly due to high comorbidity with cardiometabolic disorders. Reasons for this comorbidity are still poorly understood. We leverage nation-wide health records and near-complete genealogies of Denmark and Sweden (n = 17 million) to reveal the genetic and environmental contributions underlying the observed comorbidity between six mental disorders and 15 cardiometabolic disorders. Genetic factors contributed about 50% to the comorbidity of schizophrenia, affective disorders, and autism spectrum disorder with cardiometabolic disorders, whereas the comorbidity of attention-deficit/hyperactivity disorder and anorexia with cardiometabolic disorders was mainly or fully driven by environmental factors. In this work we provide causal insight to guide clinical and scientific initiatives directed at achieving mechanistic understanding as well as preventing and alleviating the consequences of these disorders.
Mental disorders are often linked with cardiometabolic disorders, but the mechanism remains unclear. Here, the authors use comprehensive health data from Denmark and to shed light on the genetic and environmental factors influencing the co-occurrence of six mental disorders and 15 cardiometabolic disorders.
Journal Article
Exploration of haplotype research consortium imputation for genome-wide association studies in 20,032 Generation Scotland participants
Background
The Generation Scotland: Scottish Family Health Study (GS:SFHS) is a family-based population cohort with DNA, biological samples, socio-demographic, psychological and clinical data from approximately 24,000 adult volunteers across Scotland. Although data collection was cross-sectional, GS:SFHS became a prospective cohort due to of the ability to link to routine Electronic Health Record (EHR) data. Over 20,000 participants were selected for genotyping using a large genome-wide array.
Methods
GS:SFHS was analysed using genome-wide association studies (GWAS) to test the effects of a large spectrum of variants, imputed using the Haplotype Research Consortium (HRC) dataset, on medically relevant traits measured directly or obtained from EHRs. The HRC dataset is the largest available haplotype reference panel for imputation of variants in populations of European ancestry and allows investigation of variants with low minor allele frequencies within the entire GS:SFHS genotyped cohort.
Results
Genome-wide associations were run on 20,032 individuals using both genotyped and HRC imputed data. We present results for a range of well-studied quantitative traits obtained from clinic visits and for serum urate measures obtained from data linkage to EHRs collected by the Scottish National Health Service. Results replicated known associations and additionally reveal novel findings, mainly with rare variants, validating the use of the HRC imputation panel. For example, we identified two new associations with fasting glucose at variants near to Y_RNA and WDR4 and four new associations with heart rate at SNPs within CSMD1 and ASPH, upstream of HTR1F and between PROKR2 and GPCPD1. All were driven by rare variants (minor allele frequencies in the range of 0.08–1%). Proof of principle for use of EHRs was verification of the highly significant association of urate levels with the well-established urate transporter SLC2A9.
Conclusions
GS:SFHS provides genetic data on over 20,000 participants alongside a range of phenotypes as well as linkage to National Health Service laboratory and clinical records. We have shown that the combination of deeper genotype imputation and extended phenotype availability make GS:SFHS an attractive resource to carry out association studies to gain insight into the genetic architecture of complex traits.
Journal Article