139 research outputs found
Single-nucleus transcriptomics of the prefrontal cortex in major depressive disorder implicates oligodendrocyte precursor cells and excitatory neurons
Epigenetic modelling of former, current and never smokers
BACKGROUND: DNA methylation (DNAm) performs excellently in the discrimination of current and former smokers from never smokers, where AUCs > 0.9 are regularly reported using a single CpG site (cg05575921; AHRR). However, there is a paucity of DNAm models which attempt to distinguish current, former and never smokers as individual classes. Derivation of a robust DNAm model that accurately distinguishes between current, former and never smokers would be particularly valuable to epidemiological research (as a more accurate smoking definition vs. self-report) and could potentially translate to clinical settings. Therefore, we appraise 4 DNAm models of ternary smoking status (that is, current, former and never smokers): methylation at cg05575921 (AHRR model), weighted scores from 13 CpGs created by Maas et al. (Maas model), weighted scores from a LASSO model of candidate smoking CpGs from the literature (candidate CpG LASSO model), and weighted scores from a LASSO model supplied with genome-wide 450K data (agnostic LASSO model). Discrimination is assessed by AUC, whilst classification accuracy is assessed by accuracy and kappa, derived from confusion matrices. RESULTS: We find that DNAm can classify ternary smoking status with reasonable accuracy, including when applied to external data. Ternary classification using only DNAm far exceeds the classification accuracy of simply assigning all classes as the most prevalent class (63.7% vs. 36.4%). Further, we develop a DNAm classifier which performs well in discriminating current from former smokers (agnostic LASSO model AUC in external validation data: 0.744). Finally, across our DNAm models, we show evidence of enrichment for biological pathways and human phenotype ontologies relevant to smoking, such as haemostasis, molybdenum cofactor synthesis, body fatness and social behaviours, providing evidence of the generalisability of our classifiers. CONCLUSIONS: Our findings suggest that DNAm can classify ternary smoking status with close to 65% accuracy. Both the ternary smoking status classifiers and current versus former smoking status classifiers address the present lack of former smoker classification in epigenetic literature; essential if DNAm classifiers are to adequately relate to real-world populations. To improve performance further, additional focus on improving discrimination of current from former smokers is necessary. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13148-021-01191-6
The association of DNA methylation with body mass index: distinguishing between predictors and biomarkers
Grandmaternal smoking during pregnancy is associated with differential DNA methylation in peripheral blood of their grandchildren
The idea that information can be transmitted to subsequent generation(s) by epigenetic means has been studied for decades but remains controversial in humans. Epidemiological studies have established that grandparental exposures are associated with health outcomes in their grandchildren, often with sex-specific effects; however, the mechanism of transmission is still unclear. We conducted Epigenome Wide Association Studies (EWAS) to test whether grandmaternal smoking during pregnancy is associated with altered DNA methylation (DNAm) in peripheral blood from their adolescent grandchildren. We used data from a birth cohort, with discovery and replication datasets of up to 1225 and 708 individuals (respectively, for the maternal line), aged 15–17 years, and tested replication in the same individuals at birth and 7 years. We show for the first time that DNAm at a small number of loci in cord blood is associated with grandmaternal smoking in humans. In adolescents we see suggestive associations in regions of the genome which we hypothesised a priori could be involved in transgenerational transmission - we observe sex-specific associations at two sites on the X chromosome and one in an imprinting control region. All are within transcription factor binding sites (TFBSs), and we observe enrichment for TFBS among the CpG sites with the strongest associations; however, there is limited evidence that the associations we see replicate between timepoints. The implication of this work is that effects of smoking during pregnancy may induce DNAm changes in later generations and that these changes are often sex-specific, in line with epidemiological associations
Paternal grandmother’s smoking in pregnancy is associated with extreme aversion to bitter taste in their grandchildren
Although there are many examples in the experimental literature of an environmental exposure in one generation impacting the phenotypes of subsequent generations, there are few studies that can assess whether such associations occur in humans. The Avon Longitudinal Study of Parents and Children (ALSPAC) has, however, been able to determine whether there are associations between grandparental exposures and their grandchildren’s development. Several of our studies, including sensitivity to loud noise, have shown associations between a grandmother smoking in pregnancy and the phenotype of the grandchild. These results were mostly specific to the sex of the grandchild and to whether the prenatal (i.e. during pregnancy) smoking occurred in the maternal or paternal grandmother. Here, we have used ancestral data on prenatal smoking among the grandmothers of the ALSPAC index children to examine possible effects on the grandchild’s ability to detect the bitter taste of PROP (6 n-propylthiouracil), distinguishing between the 10% deemed ‘extreme tasters’, and the rest of the population (total N = 4656 children). We showed that grandchildren whose paternal (but not maternal) grandmothers had smoked in pregnancy were more likely than those of non-smoking grandmothers to be extreme tasters [odds ratio (OR) 1.28; 95% confidence interval (CI) 1.03, 1.59] and that this was more likely in granddaughters (OR 1.42; 95% CI 1.03, 1.95) than grandsons (OR 1.18; 95% CI 0.88, 1.60). This pattern of association between paternal foetal exposure and the granddaughter’s development has been found with several other outcomes, suggesting that investigations should be undertaken to investigate possible mechanisms
Epigenetic gestational age and trajectories of weight and height during childhood:a prospective cohort study
Adverse childhood experiences, DNA methylation age acceleration and cortisol in UK children:a prospective population-based cohort study
A robust mean and variance test with application to high-dimensional phenotypes
Most studies of continuous health-related outcomes examine differences in mean levels (location) of the outcome by exposure. However, identifying effects on the variability (scale) of an outcome, and combining tests of mean and variability (location-and-scale), could provide additional insights into biological mechanisms. A joint test could improve power for studies of high-dimensional phenotypes, such as epigenome-wide association studies of DNA methylation at CpG sites. One possible cause of heterogeneity of variance is a variable interacting with exposure in its effect on outcome, so a joint test of mean and variability could help in the identification of effect modifiers. Here, we review a scale test, based on the Brown-Forsythe test, for analysing variability of a continuous outcome with respect to both categorical and continuous exposures, and develop a novel joint location-and-scale score (JLSsc) test. These tests were compared to alternatives in simulations and used to test associations of mean and variability of DNA methylation with gender and gestational age using data from the Accessible Resource for Integrated Epigenomics Studies (ARIES). In simulations, the Brown-Forsythe and JLSsc tests retained correct type I error rates when the outcome was not normally distributed in contrast to the other approaches tested which all had inflated type I error rates. These tests also identified > 7500 CpG sites for which either mean or variability in cord blood methylation differed according to gender or gestational age. The Brown-Forsythe test and JLSsc are robust tests that can be used to detect associations not solely driven by a mean effect
Association between the timing of childhood adversity and epigenetic patterns across childhood and adolescence:Findings from the Avon Longitudinal Study of Parents and Children (ALSPAC) prospective cohort
BackgroundChildhood adversity is a potent determinant of health across development and is associated with altered DNA methylation signatures, which might be more common in children exposed during sensitive periods in development. However, it remains unclear whether adversity has persistent epigenetic associations across childhood and adolescence. We aimed to examine the relationship between time-varying adversity (defined through sensitive period, accumulation of risk, and recency life course hypotheses) and genome-wide DNA methylation, measured three times from birth to adolescence, using data from a prospective, longitudinal cohort study.MethodsWe first investigated the relationship between the timing of exposure to childhood adversity between birth and 11 years and blood DNA methylation at age 15 years in the Avon Longitudinal Study of Parents and Children (ALSPAC) prospective cohort study. Our analytic sample included ALSPAC participants with DNA methylation data and complete childhood adversity data between birth and 11 years. We analysed seven types of adversity (caregiver physical or emotional abuse, sexual or physical abuse [by anyone], maternal psychopathology, one-adult households, family instability, financial hardship, and neighbourhood disadvantage) reported by mothers five to eight times between birth and 11 years. We used the structured life course modelling approach (SLCMA) to identify time-varying associations between childhood adversity and adolescent DNA methylation. Top loci were identified using an R2 threshold of 0·035 (ie, ≥3·5% of DNA methylation variance explained by adversity). We attempted to replicate these associations using data from the Raine Study and Future of Families and Child Wellbeing Study (FFCWS). We also assessed the persistence of adversity-DNA methylation associations we previously identified from age 7 blood DNA methylation into adolescence and the influence of adversity on DNA methylation trajectories from ages 0-15 years.FindingsOf 13 988 children in the ALSPAC cohort, 609-665 children (311-337 [50-51%] boys and 298-332 [49-50%] girls) had complete data available for at least one of the seven childhood adversities and DNA methylation at 15 years. Exposure to adversity was associated with differences in DNA methylation at 15 years for 41 loci (R2 ≥0·035). Sensitive periods were the most often selected life course hypothesis by the SLCMA. 20 (49%) of 41 loci were associated with adversities occurring between age 3 and 5 years. Exposure to one-adult households was associated with differences in DNA methylation at 20 [49%] of 41 loci, exposure to financial hardship was associated with changes at nine (22%) loci, and physical or sexual abuse was associated with changes at four (10%) loci. We replicated the direction of associations for 18 (90%) of 20 loci associated with exposure to one-adult household using adolescent blood DNA methylation from the Raine Study and 18 (64%) of 28 loci using saliva DNA methylation from the FFCWS. The directions of effects for 11 one-adult household loci were replicated in both cohorts. Differences in DNA methylation at 15 years were not present at 7 years and differences identified at 7 years were no longer apparent by 15 years. We also identified six distinct DNA methylation trajectories from these patterns of stability and persistence.InterpretationThese findings highlight the time-varying effect of childhood adversity on DNA methylation profiles across development, which might link exposure to adversity to potential adverse health outcomes in children and adolescents. If replicated, these epigenetic signatures could ultimately serve as biological indicators or early warning signs of initiated disease processes, helping identify people at greater risk for the adverse health consequences of childhood adversity
Regular smoking of male ancestors in adolescence and fat mass in young adult grandchildren and great-grandchildren
Background: Previous studies using the Avon Longitudinal Study of Parents and Children (ALSPAC) have shown that if men commenced smoking prior to the onset of puberty their sons, their granddaughters and great-granddaughters were more likely to have excess fat (but not lean) mass during childhood, adolescence and early adulthood. In this study we assess associations between ancestral smoking during adolescence (ages 11–16 years) with fat and lean mass of subsequent generations at two ages. Methods: We analysed data on exposures of grandparents and great-grandparents collected by ALSPAC. The outcomes were the fat masses of their grandchildren and great-grandchildren measured at ages 17 and 24. Measures of lean mass were used as controls. Adjustment was made for 8–10 demographic factors using multiple regression. Results: We found associations between adolescent smoking of the paternal grandfathers and the adjusted fat mass of their grandchildren, but no associations with the grandchildren’s lean mass. Grandchildren at age 17 had an average excess fat mass of +1.65 [95% CI +0.04, +3.26] Kg, and at age 24 an average excess of +1.55 [95% CI -0.27, +3.38] Kg. Adolescent smoking by the maternal grandfather showed similar, but weaker, associations: at 17 an average excess fat mass of +1.02 Kg [95% CI -0.20, +2.25] Kg, and at 24 an average excess of +1.28 [95% CI -0.11, +2.66] Kg. There were no pronounced differences between the sexes of the children. For the great-grandparents there were few convincing results, although numbers were small. Conclusions: We have shown associations between grandfathers’ smoking in adolescence and increased fat (but not lean) mass in their children. Confirmation of these associations is required, either in a further data set or by demonstrating the presence of supportive biomarkers
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