Biology subjects

Porteous, D.

Publications and source records attributed to Porteous, D..

8 recordsLinked to original sources

bioRxiv2018

Epigenetic signatures of starting and stopping smoking

BackgroundMultiple studies have made robust associations between differential DNA methylation and exposure to cigarette smoke. But whether a DNA methylation phenotype is established immediately upon exposure, or only after prolonged exposure is less well-established. Here, we assess DNA methylation patterns in current smokers in response to dose and duration of exposure, along with the effects of smoking cessation on DNA methylation in former smokers.\n\nMethodsDimensionality reduction was applied to DNA methylation data at 90 previously identified smoking-associated CpG sites for over 4,900 individuals in the Generation Scotland cohort. K-means clustering was performed to identify clusters associated with current and never smoker status based on these methylation patterns. Cluster assignments were assessed with respect to duration of exposure in current smokers (years as a smoker), time since smoking cessation in former smokers (years), and dose (cigarettes per day).\n\nResultsTwo clusters were specified, corresponding to never smokers (97.5% of whom were assigned to Cluster 1) and current smokers (81.1% of whom were assigned to Cluster 2). The exposure time point from which >50% of current smokers were assigned to the smoker-enriched cluster varied between 5-9 years in heavier smokers and between 15-19 years in lighter smokers. Low-dose former smokers were more likely to be assigned to the never smoker-enriched cluster from the first year following cessation. In contrast, a period of at least two years was required before the majority of former high-dose smokers were assigned to the never smoker-enriched cluster.\n\nConclusionsOur findings suggest that smoking-associated DNA methylation changes are a result of prolonged exposure to cigarette smoke, and can be reversed following cessation. The length of time in which these signatures are established and recovered is dose dependent. Should DNA methylation-based signatures of smoking status be predictive of smoking-related health outcomes, our findings may provide an additional criterion on which to stratify risk.

genomics↗

bioRxiv2018

DISC1 regulates N-Methyl-D-Aspartate receptor dynamics: Abnormalities induced by a Disc1 mutation modelling a translocation linked to major mental illness

The neuromodulatory gene DISC1 is disrupted by a t(1;11) translocation that is highly penetrant for schizophrenia and affective disorders, but how this translocation affects DISC1 function is incompletely understood. N-Methyl-D-Aspartate receptors (NMDAR) play a central role in synaptic plasticity and cognition, and are implicated in the pathophysiology of schizophrenia through genetic and functional studies. We show that the NMDAR subunit GluN2B complexes with DISC1-associated trafficking factor TRAK1, while DISC1 interacts with the GluN1 subunit and regulates dendritic NMDAR motility in cultured mouse neurons. Moreover, in the first mutant mouse that models DISC1 disruption by the translocation, the pool of NMDAR transport vesicles and surface/synaptic NMDAR expression are increased. Since NMDAR cell surface/synaptic expression is tightly regulated to ensure correct function, these changes in the mutant mouse are likely to affect NMDAR signalling and synaptic plasticity. Consistent with these observations, RNASeq analysis of translocation carrier-derived human neurons indicates abnormalities of excitatory synapses and vesicle dynamics. RNASeq analysis of the human neurons also identifies many differentially expressed genes previously highlighted as putative schizophrenia and/or depression risk factors through large-scale genome-wide association and copy number variant studies, indicating that the translocation triggers common disease pathways that are shared with unrelated psychiatric patients. Altogether our findings suggest that translocation-induced disease mechanisms are likely to be relevant to mental illness in general, and that such disease mechanisms include altered NMDAR dynamics and excitatory synapse function. This could contribute to the cognitive disorders displayed by translocation carriers.

neuroscience↗

bioRxiv2018

Improved prediction of chronological age from DNA methylation limits it as a biomarker of ageing

DNA methylation is associated with age. The deviation of age predicted from DNA methylation from actual age has been proposed as a biomarker for ageing. However, a better prediction of chronological age implies less opportunity for biological age. Here we used 13,661 samples (from blood and saliva) in the age range of 2 to 104 years from 14 cohorts measured on Illumina HumanMethylation450/EPIC arrays to perform prediction analyses. We show that increasing the sample size achieves a smaller prediction error and higher correlations in test datasets. We demonstrate that smaller prediction errors provide a limit to how much variation in biological ageing can be captured by methylation and provide evidence that age predictors from small samples are prone to confounding by cell composition. Our predictor shows a similar or better performance in non-blood tissues including saliva, endometrium, breast, liver, adipose and muscle, compared with Horvaths across-tissue age predictor.

bioinformatics↗

bioRxiv2017

Genetic and environmental determinants of stressful life events and their overlap with depression and neuroticism

BackgroundStressful life events (SLEs) and neuroticism are risk factors for major depressive disorder (MDD). However, SLEs and neuroticism are heritable traits that are correlated with genetic risk for MDD. In the current study, we sought to investigate the genetic and environmental contributions to SLEs in a large family-based sample, and quantify any genetic overlap with MDD and neuroticism.\n\nMethodsA subset of Generation Scotland: the Scottish Family Health Study, consisting of 9618 individuals comprise the present study. We estimated the heritability of SLEs using pedigree-based and molecular genetic data. The environment was assessed by modelling familial, couple and sibling components. Using polygenic risk scores (PRS) and LD score regression we analysed the genetic overlap between MDD, neuroticism and SLEs.\n\nResultsPast 6-month life events were positively correlated with lifetime MDD status ({beta}=0.21, r2=1.1%, p=2.5 x 10-25) and neuroticism ({beta} =0.13, r2=1.9%, p=1.04 x 10-37). Common SNPs explained 8% of the variance in personal life events (those directly affecting the individual) (S.E.=0.03, p=9 x 10-4). A significant effect of couple environment accounted for 13% (S.E.=0.03, p=0.016) of variation in SLEs. PRS analyses found that individuals with higher PRS for MDD reported more SLEs ({beta} =0.05, r2=0.3%, p=3 x 10-5). LD score regression demonstrated genetic correlations between MDD and both SLEs (rG=0.33, S.E.=0.08) and neuroticism (rG=0.15, S.E.=0.07).\n\nConclusionsThese findings suggest that SLEs are partially heritable and this heritability is shared with risk for MDD and neuroticism. Further work should determine the causal direction and source of these associations.

genetics↗

bioRxiv2017

Accelerated Epigenetic Ageing in Major Depressive Disorder

BackgroundMajor depressive disorder (MDD) is a severe, heritable psychiatric disorder associated with shortened lifespan and comorbidities of advancing age. It is unknown however whether MDD is associated with accelerated biological ageing relative to chronological age. This hypothesis was tested using the epigenetic clock as a measure of biological age.\n\nMethodsTo address the main hypothesis, using peripheral blood, we derived measures of Epigenetic Age Acceleration (EAA) in 3,833 controls and 1,219 MDD cases based on Hannum and Horvath epigenetic clocks in Generation Scotland (GS:SFHS, mean age 48 years, std dev 14.5). Models controlled for relatedness, sex, cell counts, and processing batch (basic model), as well as additional covariates of smoking and drinking status, and body mass index (BMI) (full models).\n\nResultsAccelerated epigenetic ageing was found in MDD cases versus controls using the Horvath clock ({beta}=0.0804, p=0.012 equivalent to 0.20 years) in both the basic and full models. Significant MDD*age interactions indicated greatest effects at younger age ranges. No significant differences were observed for the Hannum clock. BMI was the only additional covariate found to attenuate the relationship between EAAHorvath and MDD. Further, genetic correlation analysis indicated significant overlap in the genetic aetiology of EAAHorvath with BMI (rG=0.20, p=0.03), between MDD with BMI (rG=0.10, p=9.86x10-6), but not between EAAHorvath and MDD (rG=0.14, p=0.125). Mediation analysis indicated partial mediation of the relationship between EAAHorvath and depression status through BMI ({beta} =0.0028; p=0.0248, ~13%).\n\nConclusionThese data imply that accelerated biological ageing is associated with MDD and partially mediated through BMI.

genomics↗

bioRxiv2017

Psychosis and the level of mood incongruence in Bipolar Disorder are related to genetic liability for Schizophrenia

ImportanceBipolar disorder (BD) overlaps schizophrenia in its clinical presentation and genetic liability. Alternative approaches to patient stratification beyond current diagnostic categories are needed to understand the underlying disease processes/mechanisms.\n\nObjectivesTo investigate the relationship between common-variant liability for schizophrenia, indexed by polygenic risk scores (PRS) and psychotic presentations of BD, using clinical descriptions which consider both occurrence and level of mood-incongruent psychotic features.\n\nDesignCase-control design: using multinomial logistic regression, to estimate differential associations of PRS across categories of cases and controls.\n\nSettings & Participants4399 BDcases, mean [sd] age-at-interview 46[12] years, of which 2966 were woman (67%) from the BD Research Network (BDRN) were included in the final analyses, with data for 4976 schizophrenia cases and 9012 controls from the Type-1 diabetes genetics consortium and Generation Scotland included for comparison.\n\nExposureStandardised PRS, calculated using alleles with an association p-value threshold < 0.05 in the second Psychiatric Genomics Consortium genome-wide association study of schizophrenia, adjusted for the first 10 population principal components and genotyping-platform.\n\nMain outcome measureMultinomial logit models estimated PRS associations with BD stratified by (1) Research Diagnostic Criteria (RDC) BD subtypes (2) Lifetime occurrence of psychosis.(3) Lifetime mood-incongruent psychotic features and (4) ordinal logistic regression examined PRS associations across levels of mood-incongruence. Ratings were derived from the Schedule for Clinical Assessment in Neuropsychiatry interview (SCAN) and the Bipolar Affective Disorder Dimension Scale (BADDS).\n\nResultsAcross clinical phenotypes, there was an exposure-response gradient with the strongest PRS association for schizophrenia (RR=1.94, (95% C.1.1.86, 2.01)), then schizoaffective BD (RR=1.37, (95% C.I. 1.22, 1.54)), BD I (RR= 1.30, (95% C.I. 1.24, 1.36)) and BD II (RR=1.04, (95% C.1. 0.97, 1.11)). Within BD cases, there was an effect gradient, indexed by the nature of psychosis, with prominent mood-incongruent psychotic features having the strongest association (RR=1.46, (95% C.1.1.36, 1.57)), followed by mood-congruent psychosis (RR= 1.24, (95% C.1. 1.17, 1.33)) and lastly, BD cases with no history of psychosis (RR= 1.09, (95% C.1. 1.04, 1.15)).\n\nConclusionWe show for the first time a polygenic-risk gradient, across schizophrenia and bipolar disorder, indexed by the occurrence and level of mood-incongruent psychotic symptoms.

genetics↗

bioRxiv2017

Genomic analysis of family data reveals additional genetic effects on intelligence and personality

Pedigree-based analyses of intelligence have reported that genetic differences account for 50-80% of the phenotypic variation. For personality traits these effects are smaller, with 34-48% of the variance being explained by genetic differences. However, molecular genetic studies using unrelated individuals typically report a heritability estimate of around 30% for intelligence and between 0% and 15% for personality variables. Pedigree-based estimates and molecular genetic estimates may differ because current genotyping platforms are poor at tagging causal variants, variants with low minor allele frequency, copy number variants, and structural variants. Using [~]20 000 individuals in the Generation Scotland family cohort genotyped for [~]700 000 single nucleotide polymorphisms (SNPs), we exploit the high levels of linkage disequilibrium (LD) found in members of the same family to quantify the total effect of genetic variants that are not tagged in GWASs of unrelated individuals. In our models, genetic variants in low LD with genotyped SNPs explain over half of the genetic variance in intelligence, education, and neuroticism. By capturing these additional genetic effects our models closely approximate the heritability estimates from twin studies for intelligence and education, but not for neuroticism and extraversion. We then replicated our finding using imputed molecular genetic data from unrelated individuals to show that [~]50% of differences in intelligence, and [~]40% of the differences in education, can be explained by genetic effects when a larger number of rare SNPs are included. From an evolutionary genetic perspective, a substantial contribution of rare genetic variants to individual differences in intelligence and education is consistent with mutation-selection balance.

genetics↗

bioRxiv2016

Rare disruptive variants in the DISC1 Interactome and Regulome: association with cognitive ability and schizophrenia

Schizophrenia (SCZ), bipolar disorder (BD) and recurrent major depressive disorder (rMDD) are common psychiatric illnesses. All have been associated with lower cognitive ability, and show evidence of genetic overlap and substantial evidence of pleiotropy with cognitive function and neuroticism. Disrupted in schizophrenia 1 (DISC1) protein directly interacts with a large set of proteins (DISC1 Interactome) that are involved in brain development and signaling. Modulation of DISC1 expression alters the expression of a circumscribed set of genes (DISC1 Regulome) that are also implicated in brain biology and disorder. Here, we report targeted sequencing of 59 DISC1 Interactome genes and 154 Regulome genes in 654 psychiatric patients and 889 cognitively-phenotyped control subjects, on whom we previously reported evidence for trait association from complete sequencing of the DISC1 locus. Burden analyses of rare and singleton variants predicted to be damaging were performed for psychiatric disorders, cognitive variables and personality traits. The DISC1 Interactome and Regulome showed differential association across the phenotypes tested. After family-wise error correction across all traits (FWERacross), an increased burden of singleton disruptive variants in the Regulome was associated with SCZ (FWERacross P=0.0339). The burden of singleton disruptive variants in the DISC1 Interactome was associated with low cognitive ability at age 11 (FWERacross P=0.0043). These results suggest that variants in the DISC1 Interactome effect the risk of psychiatric illness through altered expression of schizophrenia-associated genes. The biological impact of rare variants highlighted here merit further study.

genetics↗