Biology subjects

Timpson, N. J.

Publications and source records attributed to Timpson, N. J..

At least 19 recordsLinked to original sources

bioRxiv2025

metaboprep v2: Broadening the application of the metaboprep beyond metabolomics

High-throughput multiplex assays for metabolomics and proteomics offer opportunities for biomarker discovery and disease stratification in epidemiological research. The complexity of these datasets requires robust, standardized and transparent preprocessing workflows to ensure reproducibility and comparability across studies. We present an updated and enhanced version of the metaboprep R package. Originally designed for metabolomics data, it has now been extended to support proteomics datasets from platforms such as Olink(R) and SomaScan(R). This release introduces a user-friendly, modular, object-oriented architecture using Rs S7 system, enabling improved input format flexibility, streamlined report generation and increased compatibility with other third-party tools. The updated pipeline is structured in three parts: data import, filtering and summary, and output generation. This structure provides a reproducible yet customizable framework for pre-analysis data preparation with utility across multiple omics platforms and particular value in supporting multi-cohort epidemiological research. Availability and implementationThe metaboprep package is implemented in R and freely available at: https://github.com/MRCIEU/metaboprep.

bioinformatics↗

bioRxiv2022

DNA co-methylation has a stable structure and is related to specific aspects of genome regulation

DNA methylation (DNAm) is influenced by genetic and environmental factors, and can be used to understand interindividual variability in genomic regulation. Co-methylation between DNAm sites is a known phenomenon, but the architecture of relationships between the approximately 450,000 (450k) sites commonly measured in epidemiological studies has not been described. We investigate whether interindividual co-methylation structure amongst the 450k sites changes with age, whether it differs between UK-born White (n=849, 910, 921 and 424) and Pakistani ancestry (n=439) individuals, and how it relates to genome regulation. We find stability between birth and adolescence, across cohorts, and between two ethnic groups. Highly correlated DNAm sites in close proximity are heritable, but these relationships are weakly influenced by nearby genetic variants, and are enriched for transcription factor (TF) binding sites related to regulation of short RNAs transcribed by RNA polymerase III. Highly correlated sites that are distant, or on different chromosomes (in trans), are driven by common and unique environmental factors, with methylation at these sites less likely to be driven by genotype. Trans co-methylated DNAm sites are enriched for multiple TF binding sites and for inter-chromosomal chromatin contact sites, suggesting DNA co-methylation of distant sites may relate to long-range cooperative TF interactions. We conclude that DNA co-methylation has a stable structure from birth to adolescence, and between UK-born White and Pakistani individuals. This stable structure might have implications for future design and interpretation of epigenetic studies. We hypothesise that co-methylation may have roles in genome regulation in humans, including 3D chromatin architecture.

bioinformatics↗

bioRxiv2021

A framework for research into continental ancestry groups of the UK Biobank

BackgroundThe UK Biobank is a large prospective cohort, based in the United Kingdom, that has deep phenotypic and genomic data on roughly a half a million individuals. Included in this resource are data on approximately 78,000 individuals with "non-white British ancestry". Whilst most epidemiology studies have focused predominantly on populations of European ancestry, there is an opportunity to contribute to the study of health and disease for a broader segment of the population by making use of the UK Biobanks "non-white British ancestry" samples. Here we present an empirical description of the continental ancestry and population structure among the individuals in this UK Biobank subset. ResultsReference populations from the 1000 Genomes Project for Africa, Europe, East Asia, and South Asia were used to estimate ancestry for each individual. Those with at least 80% ancestry in one of these four continental ancestry groups were taken forward (N=62,484). Principal component and K-means clustering analyses were used to identify and characterize population structure within each ancestry group. Of the approximately 78,000 individuals in the UK Biobank that are of "non-white British" ancestry, 50,685, 6,653, 2,782, and 2,364 individuals were associated to the European, African, South Asian, and East Asian continental ancestry groups, respectively. Each continental ancestry group exhibits prominent population structure that is consistent with self-reported country of birth data and geography. ConclusionsMethods outlined here provide an avenue to leverage UK Biobanks deeply phenotyped data allowing researchers to maximise its potential in the study of health and disease in individuals of non-white British ancestry.

genomics↗

bioRxiv2021

A multi-variant recall-by-genotype study of the metabolomic signature of body mass index

ObjectiveWe estimated the effect of body mass index (BMI) on circulating metabolites in young adults using a recall-by-genotype (RbG) study design. MethodsAn RbG study was implemented in the Avon Longitudinal Study of Parents and Children. Samples from 756 participants were selected for untargeted metabolomics analysis based on low/high genetic liability for higher BMI defined by a genetic score (GS). Regression analyses were performed to investigate the association between BMI GS groups and relative abundance of 973 metabolites. ResultsAfter correction for multiple testing, 29 metabolites were associated with BMI GS group. Bilirubin was amongst the most strongly associated metabolites with reduced levels measured in individuals with the highest BMI GS (beta=-0.32, 95% confidence interval (CI): -0.46, -0.18, Benjamini-Hochberg (BH) adjusted p=0.005). We observed associations between BMI GS group and levels of several potentially diet-related metabolites including hippurate which had lower mean abundance in individuals in the high BMI GS group (beta=-0.29, 95% CI: -0.44, -0.15, BH adjusted p=0.008). ConclusionsTogether with existing literature our results suggest a genetic predisposition to higher BMI captures differences in metabolism leading to adiposity gain. In the absence of prospective data, separating these effects from the downstream consequences of weight gain is challenging. Study Importance questionsO_ST_ABSWhat is already known about this subject?C_ST_ABSO_LIMetabolomics, defined as the measurement and study of circulating small molecules that are the substrates and products of cellular metabolism, is increasingly used by epidemiologists to provide a functional read-out of bulk cellular activity and a proxy to individual current health. This approach also provides insight into biological pathways linking exposures and disease. C_LIO_LIIn observational studies, elevated body mass index (BMI) has been associated with a wide range of circulating metabolites. Researchers are now looking to genetic epidemiological methods, such as Mendelian randomization, to offer insight into potential causal relationships. C_LI What are the new findings in your manuscript?O_LIWe identified 29 metabolites whose relative abundance varies with a genetic predisposition to higher BMI. C_LIO_LIBilirubin, a key component of the heme catabolic pathway and a potent antioxidant, showed the strongest association with BMI score group. C_LI How might your results change the direction of research or the focus of clinical practice?O_LIResults of both Mendelian randomization and recall-by-genotype studies need to be combined with alternative study designs to distinguish between biomarkers that are intermediates on the pathway to BMI from those reflective of metabolic changes that result from increased adiposity. C_LIO_LISeparating causal biomarkers from non-causative biomarkers of adiposity is important since only the former are relevant to treatment and prevention, whilst both could be informative with respect to prediction and the downstream consequences of high BMI. C_LI

genetics↗

bioRxiv2021

Large-scale genome-wide association study of food liking reveals genetic determinants and genetic correlations with distinct neurophysiological traits.

Variable preferences for different foods are among the main determinants of their intake and are influenced by many factors, including genetics. Despite considerable twins heritability, studies aimed at uncovering food-liking genetics have focused mostly on taste receptors. Here, we present the first results of a large-scale genome-wide association study of food liking conducted on 161,625 participants from UK Biobank. Liking was assessed over 139 specific foods using a 9-point hedonic scale. After performing GWAS, we used genetic correlations coupled with structural equation modelling to create a multi-level hierarchical map of food liking. We identified three main dimensions: high caloric foods defined as "Highly palatable", strong-tasting foods ranging from alcohol to pungent vegetables, defined as "Learned" and finally "Low caloric" foods such as fruit and vegetables. The "Highly palatable" dimension was genetically uncorrelated from the other two, suggesting that two independent processes underlie liking high reward foods and the Learned/Low caloric ones. Genetic correlation analysis with the corresponding food consumption traits revealed a high correlation, while liking showed twice the heritability compared to consumption. For example, fresh fruit liking and consumption showed a genetic correlation of 0.7 with heritabilities of 0.1 and 0.05, respectively. GWAS analysis identified 1401 significant food-liking associations located in 173 genomic loci, with only 11 near taste or olfactory receptors. Genetic correlation with morphological and functional brain data (33,224 UKB participants) uncovers associations of the three food-liking dimensions with non-overlapping, distinct brain areas and networks, suggestive of separate neural mechanisms underlying the liking dimensions. In conclusion, we created a comprehensive and data-driven map of the genetic determinants and associated neurophysiological factors of food liking beyond taste receptor genes.

genetics↗

bioRxiv2021

metaboprep: an R package for pre-analysis data description and processing.

MotivationMetabolomics is an increasingly common part of health research and there is need for pre-analytical data processing. Researchers typically need to characterize the data and to exclude errors within the context of the intended analysis. While some pre-processing steps are common, there is currently a lack of standardization and reporting transparency for these procedures. ResultsHere we introduce metaboprep, a standardized data processing workflow to extract and characterize high quality metabolomics data sets. The package extracts data from pre-formed worksheets, provides summary statistics and enables the user to select samples and metabolites for their analysis based on a set of quality metrics. A report summarizing quality metrics and the influence of available batch variables on the data is generated for the purpose of open disclosure. Where possible, we provide users flexibility in defining their own selection thresholds. Availability and implementationmetaboprep is an open-source R package available at https://github.com/MRCIEU/metaboprep Contactd.a.hughes@bristol.ac.uk or laura.corbin@bristol.ac.uk

bioinformatics↗

bioRxiv2020

Exploring the variance in complex traits captured by DNA methylation assays

Following years of epigenome-wide association studies (EWAS), traits analysed to date tend to yield few associations. Reinforcing this observation, we conducted EWAS on 400 traits and 16 yielded at least one association at the conventional significance threshold (P<1x10-7). To investigate why EWAS yield is low, we formally estimated the proportion of phenotypic variation captured by 421,693 blood derived DNA methylation markers (h2EWAS) across all 400 traits. The mean h2EWAS was zero, with evidence for regular cigarette smoking exhibiting the largest association with all markers (h2EWAS=0.42) and the only one surpassing a false discovery rate < 0.1. Though underpowered to determine the h2EWAS value for any one trait, h2EWAS was predictive of the number of EWAS hits across the traits analysed (AUC=0.7). Modelling the contributions of the methylome on a per-site versus a per-region basis gave varied h2EWAS estimates (r=0.47) but neither approach obtained substantially higher model fits across all traits. Our analysis indicates that most complex traits do not heavily associate with markers commonly measured in EWAS within blood. However, it is likely DNA methylation does capture variation in some traits and h2EWAS may be a reasonable way to prioritise traits that are likely to yield associations.

genetics↗

bioRxiv2020

Schizophrenia-associated variation at ZNF804A correlates with altered experience-dependent dynamics of sleep slow-waves and spindles in healthy young adults

BackgroundThe rs1344706 polymorphism in ZNF804A is robustly associated with schizophrenia (SZ), yet brain and behavioral phenotypes related to this variant have not been extensively characterized. In turn, SZ is associated with abnormal non-rapid eye movement (NREM) sleep neurophysiology. To examine whether rs1344706 is associated with intermediate neurophysiological traits in the absence of disease, we assessed the relationship between genotype, sleep neurophysiology, and sleep-dependent memory consolidation in healthy participants. MethodsWe recruited healthy adult males, with no history of psychiatric disorder, from the Avon Longitudinal Study of Parents and Children (ALSPAC) birth cohort. Participants were homozygous for either the SZ-associated A allele (N=25) or the alternative C allele (N=22) at rs1344706. Actigraphy, polysomnography (PSG) and a motor sequencing task (MST) were used to characterize daily activity patterns, sleep neurophysiology and sleep-dependent memory consolidation. ResultsAverage MST learning and sleep-dependent performance improvements were similar across genotype groups, but with increased variability in the AA group. CC participants showed increased slow-wave and spindle amplitudes, plus augmented coupling of slow-wave activity across recording electrodes after learning. Slow-waves and spindles in those with the AA genotype were insensitive to learning, whilst slow-wave coherence decreased following MST training. ConclusionWe describe evidence that rs1344706 polymorphism in ZNF804A is associated with changes in experience- and sleep-dependent, local and distributed neural network activity that supports offline information processing during sleep in a healthy population. These findings highlight the utility of sleep neurophysiology in mapping the impacts of SZ-associated variants on neural circuit oscillations and function.

neuroscience↗

bioRxiv2020

Common maternal and fetal genetic variants show expected polygenic effects on the probability of being born small- or large-for-gestational-age (SGA or LGA), except in the smallest 3% of babies

Babies born clinically Small- or Large-for-Gestational-Age (SGA or LGA; sex- and gestational age-adjusted birth weight (BW) <10th or >90th percentile, respectively), are at higher risks of complications. SGA and LGA include babies who have experienced growth-restriction or overgrowth, respectively, and babies who are naturally small or large. However, the relative proportions within each group are unclear. We aimed to assess the extent to which the genetics of normal variation in birth weight influence the probability of SGA/LGA. We calculated independent fetal and maternal genetic scores (GS) for BW in 12,125 babies and 5,187 mothers. These scores capture the direct fetal and indirect maternal (via intrauterine environment) genetic contributions to BW, respectively. We also calculated maternal fasting glucose (FG) and systolic blood pressure (SBP) GS. We tested associations between each GS and probability of SGA or LGA. For the BW GS, we used simulations to assess evidence of deviation from an expected polygenic model. Higher BW GS were strongly associated with lower odds of SGA and higher odds of LGA (ORfetal=0.65 (0.60,0.71) and 1.47 (1.36,1.59); ORmaternal=0.80 (0.76,0.87) and 1.23 (1.15,1.31), respectively per 1 decile higher GS). Associations were in accordance with a polygenic model except in the smallest 3% of babies (Pfetal=0.0034, Pmaternal=0.023). Higher maternal GS for FG and SBP were associated with higher odds of LGA and SGA respectively (both P<0.01). While lower maternal FG and SBP are generally considered healthy in pregnancy, we found some evidence of association with higher odds of SGA (P=0.015) and LGA (P=0.14) respectively. We conclude that common genetic variants contribute to risk of SGA and LGA, but that additional factors become more important for risk of SGA in the smallest 3% of babies. Naturally low maternal glucose and blood pressure levels may additionally contribute to risk of SGA and LGA, respectively. Author SummaryBabies in the lowest or highest 10% of the population distribution of birth weight (BW) for a given gestational age are referred to as Small- or Large-for-Gestational-Age (SGA or LGA) respectively. These babies have higher risks of complications compared to babies with BW closer to the mean. SGA and LGA babies may have experienced growth restriction or overgrowth, respectively, but may alternatively just be at the tail ends of the normal growth distribution. The relative proportions of normal vs. sub-optimal growth within these groups is unclear. To examine the role of common genetic variation in SGA and LGA, we tested their associations with a fetal genetic score (GS) for BW in 12,125 European-ancestry individuals. We also tested associations with maternal GS (5,187 mothers) for offspring BW, fasting glucose and systolic blood pressure, each of which influences fetal growth via the in utero environment. We found all fetal and maternal GS were associated with SGA and LGA, supporting strong maternal and fetal genetic contributions to birth weight in both tails of the distribution. However, within the smallest 3% of babies, the maternal and fetal GS for BW were higher than expected, suggesting factors additional to common genetic variation are more important in determining birth weight in these very small babies.

genetics↗

bioRxiv2019

Inference and visualization of phenome-wide causal relationships using genetic data: an application to dental caries and periodontitis.

BackgroundHypothesis-free Mendelian randomization studies provide a way to assess the causal relevance of a trait across the human phenome but can be limited by statistical power or complicated by horizontal pleiotropy. The recently described latent causal variable (LCV) approach provides an alternative method for causal inference which might be useful in hypothesis-free experiments. MethodsWe developed an automated pipeline for phenome-wide tests using the LCV approach including steps to estimate partial genetic causality, filter to a meaningful set of estimates, apply correction for multiple testing and then present the findings in a graphical summary termed a causal architecture plot. We apply this process to body mass index and lipid traits as exemplars of traits where there is strong prior expectation for causal effects and dental caries and periodontitis as exemplars of traits where there is a need for causal inference. ResultsThe results for lipids and BMI suggest that these traits are best viewed as creating consequences on a multitude of traits and conditions, thus providing additional evidence that supports viewing these traits as targets for interventions to improve health. On the other hand, caries and periodontitis are best viewed as a downstream consequence of other traits and diseases rather than a cause of ill health. ConclusionsThe automated process is available as part of the MASSIVE pipeline from the Complex-Traits Genetics Virtual Lab (https://vl.genoma.io) and results are available in (https://view.genoma.io). We propose causal architecture plots based on phenome-wide partial genetic causality estimates as a way visualizing the overall causal map of the human phenome. Key messagesO_LIThe latent causal variable approach uses summary statistics from genome-wide association studies to estimate a parameter termed genetic causality proportion. C_LIO_LISystematic estimation of genetic causality proportion for many pairs of traits provides an alternative method for phenome-wide causal inference with some theoretical and practical advantages compared to phenome-wide Mendelian randomization. C_LIO_LIUsing this approach, we confirm that lipid traits are an upstream risk factor for other traits and diseases, and we identify that dental diseases are predominantly a downstream consequence of other traits rather than a cause of poor systemic health. C_LIO_LIThe method assumes no bidirectional causality and no confounding by environmental correlates of genotypes, so care is needed when these assumptions are not met. C_LIO_LIWe developed an automated and accessible pipeline for estimating phenome-wide causal relationships and generating interactive visual summaries. C_LI

genetics↗

bioRxiv2019

Exploration of the role of CHRNA5-A3-B4 genotype in smoking behaviours

Genome-wide association studies have identified associations between variation at rs16969968/rs1051730 in the CHRNA5-A3-B4 gene cluster and smoking related outcomes. Experiments in rodents have described the nicotinic acetylcholine receptors (nAChRs) subunits encoded by this gene cluster and showed a lack of nicotine aversion in nAChRs deficient animal models. We conducted a nicotine challenge and a smoking topography study in humans, hypothesising that: 1. responses to a nicotine challenge would differ according to the rs16969968/rs1051730 genotype and 2. genotype may influence nicotine intake via smoking topography.\n\nWe used linear regressions to examine associations between rs16969968/rs1051730 genotype and subjective (questionnaires) and objective (physiological parameters) responses following acute nicotine exposure in never smokers (hypothesis 1) or cigarette smoking in current smokers (hypothesis 2). There was evidence to suggest nicotine exposure increases blood pressure and heart rate, and negatively affects mood, but insufficient evidence that these effects differ by genotype. Carriers of the minor allele following smoking one cigarette, exhibited reduced cravings (b=-2.46, 95% CI -4.87 to - 0.06, p=0.04) and inhaled less smoke per cigarette (b=-0.24, 95% CI - 0.43 to - 0.06, p=0.01) and per puff (b=-0.18, 95% CI -0.32 to -0.01, p=0.02). These results suggest that we need to carefully consider the translational value of the findings of aversion behaviour in nAChRs rodent models, and that deeper inhalation does not explain the strong association between rs16969968/rs1051730 genotype and objective biomarkers of tobacco exposure.

neuroscience↗

bioRxiv2019

Early metabolic features of genetic liability to type 2 diabetes: cohort study with repeated metabolomics across early life

BackgroundType 2 diabetes develops for many years before diagnosis. We aimed to reveal early metabolic features characterising liability to adult disease by examining genetic liability to adult type 2 diabetes in relation to detailed metabolic traits across early life.\n\nMethods and FindingsData were from up to 4,761 offspring from the Avon Longitudinal Study of Parents and Children cohort. Linear models were used to examine effects of a genetic risk score (GRS, including 162 variants) for adult type 2 diabetes on 4 repeated measures of 229 traits from targeted nuclear magnetic resonance (NMR) metabolomics. These traits included lipoprotein subclass-specific cholesterol and triglyceride content, amino and fatty acids, inflammatory glycoprotein acetyls, and others, and were measured in childhood (age 8y), adolescence (age 16y), young-adulthood (age 18y), and adulthood (age 25y). For replication, two-sample Mendelian randomization (MR) was conducted using summary data from genome-wide association studies of metabolic traits from NMR in an independent sample of adults (N range 13,476 to 24,925; mean (SD) age range 23.9y (2.1y) to 61.3y (2.9y)). Among ALSPAC participants (49.7% male), the prevalence of type 2 diabetes was very low across time points (< 5 cases when first assessed at age 16y; 7 cases (0.4%) when assessed at age 25y). At age 8y, type 2 diabetes liability (per SD-higher GRS) was associated with lower lipids in high-density lipoprotein (HDL) particle subtypes - e.g. -0.03 SD (95% CI = -0.06, -0.003; P = 0.03) for total lipids in very-large HDL. At age 16y, associations remained strongest with lower lipids in HDL and became stronger with pre-glycemic traits including citrate (-0.06 SD, 95% CI = -0.09, -0.02; P = 1.41x10-03) and with glycoprotein acetyls (0.05 SD, 95% CI = 0.01, 0.08; P = 0.01). At age 18y, associations were stronger with branched chain amino acids including valine (0.06 SD; 95% CI = 0.02, 0.09; P = 1.24x10-03), while at age 25y, associations had strengthened with VLDL lipids and remained consistent with previously altered traits including HDL lipids. Results of two-sample MR in an independent sample of adults indicated persistent patterns of effect of type 2 diabetes liability, with higher type 2 diabetes liability positively associated with VLDL lipids and branched chain amino acid levels, and inversely associated with HDL lipids - again for large and very large HDL particularly (-0.004 SD (95% CI = -0.007, -0.002; P = 8.45x10-04) per 1 log odds of type 2 diabetes for total lipids in large HDL). Study limitations include modest sample sizes for ALSPAC analyses and limited coverage of protein and hormonal traits; insulin was absent as it is not quantified by NMR and not consistently available at each time point. Analyses were restricted to white-Europeans which reduced confounding by population structure but limited inference to other ethnic groups.\n\nConclusionsOur results support perturbed HDL lipid metabolism as one of the earliest features of type 2 diabetes liability which precedes higher branched chain amino acid and inflammatory glycoprotein acetyl levels. This feature is apparent in childhood as early as age 8y, decades before the clinical onset of disease.\n\nAuthor summaryO_ST_ABSWhy was this study done?C_ST_ABSO_LIType 2 diabetes develops for many years before diagnosis. Clinical disease is characterised by numerous metabolic perturbations that are detectable in circulation, but which of these reflect the developmental stages of type 2 diabetes - as opposed to independent causes of type 2 diabetes or markers of other disease processes - is unknown. Revealing traits specific to type 2 diabetes development could inform the targeting of key pathways to prevent the clinical onset of disease and its complications.\nC_LIO_LIGenetic liability to type 2 diabetes is less prone to confounding than measured type 2 diabetes or blood glucose and may help reveal early perturbations in the blood that arise in response to type 2 diabetes liability itself.\nC_LI\n\nWhat did the researchers do and find?O_LIWe examined effects of genetic liability to adult type 2 diabetes, based on a genetic risk score including 162 variants, on detailed metabolic traits measured on the same individuals across four stages of early life - childhood (age 8y), adolescence (age 16y), young-adulthood (age 18y), and adulthood (age 25y).\nC_LIO_LIWe found that higher type 2 diabetes liability was associated most consistently across ages with lower lipid content in certain subtypes of HDL particles. Effects were more gradual on higher lipid content in VLDL particles and on higher branched chain amino acid and inflammatory glycoprotein acetyl levels.\nC_LI\n\nWhat do these findings mean?O_LISigns of type 2 diabetes liability are detectable in the blood in childhood, decades before the disease becomes noticeable. These signs, taken to reflect the early features of, or coincident with, disease, likely involve lower lipid content in HDL particles, followed by higher levels of branched chain amino acids and inflammation.\nC_LIO_LIGenetic risk scores for adult diseases can be integrated with metabolic measurements taken earlier in life to help to reveal the timing at which signs of disease liability become visible and the traits most central to its development.\nC_LI

genetics↗

bioRxiv2018

Expanded genomic analyses for male voice-breaking highlights a shared phenotypic and genetic basis between puberty timing and hair colour

The timing of puberty is highly variable and has important consequences for long-term health. Most of our understanding of the genetic control of puberty timing is based on studies in women, as age at menarche is often recorded. Here, we report a multi-trait genome-wide association study for male puberty timing, based on recalled timing of voice breaking and facial hair, with an effective sample size of 205,354 men, nearly four-fold larger than previously reported. We identify 78 independent signals for male puberty timing, including 29 signals not previously associated with puberty in either sex. Novel mechanisms include an unexpected phenotypic and genetic link between puberty timing and natural hair colour, possibly reflecting common effects of pituitary hormones on puberty and pigmentation. Earlier male puberty timing is genetically correlated with several adverse health outcomes and, in Mendelian randomization analyses, shows causal relationships with higher risk of prostate cancer and shorter lifespan. These findings highlight the relationships between puberty timing and later health outcomes, and demonstrate the value of genetic studies of puberty timing in both sexes.

genomics↗

bioRxiv2018

Epigenetic regulation of PAR4-related platelet activation: mechanistic links between environmental exposure and cardiovascular disease

Protease-activated receptor 4 (PAR4) is a potent thrombin receptor. Epigenetic control of the F2RL3 locus (which encodes for PAR4) via DNA methylation is associated with both smoking and cardiovascular disease. We examined the association between DNA hypomethylation at F2RL3 and risk of cardiovascular disease, focusing on acute myocardial infarction (AMI) (n=853 cases / 2,352 controls). We used in vitro cell models to dissect the role of DNA methylation in regulating expression of F2RL3. We investigated the interplay between F2RL3 DNA methylation and platelet function in human (n=41). Lastly, we used Mendelian randomization to unify observational and functional work by assessing evidence for causal relationships using data from UK Biobank (n=407,141) and CARDIoGRAMplusC4D (n=184,305). Observationally, one standard deviation (SD) decrease in DNA methylation at F2RL3 was associated with a 25% increase in the odds of AMI. In vitro, short-term exposure of cells to cigarette smoke reduced F2RL3 DNA methylation and increased gene expression. Transcriptional assays flagged a role for a CEBP recognition sequence in modulating the enhancer activity of F2RL3 exon 2. Lower DNA methylation at F2RL3 was associated with increased platelet reactivity in human. The estimated casual odds ratio of ischaemic heart disease was 1.03 (95% CI: 1.00, 1.07) per 1 SD decrease in F2RL3 DNA. In conclusion, we show that DNA methylation-dependent platelet activation is part of a complex system of features contributing to cardiovascular health. Tailoring therapeutic intervention to new knowledge of F2RL3/PAR4 function should be explored to ameliorate the detrimental effects of this risk factor on cardiovascular health.\n\nOne sentence summaryDNA methylation-dependent platelet activation is a likely causal contributor to cardiovascular health.

epidemiology↗

bioRxiv2018

Hypothesis-free analysis of deep vein thrombosis aetiology: a Mendelian randomization study

BackgroundDeep vein thrombosis (DVT) is the formation of a thrombus/clot in the deep veins, when part of this clot breaks off it can travel to the lungs, resulting in pulmonary embolism. These two conditions together are known as venous thromboembolism (VTE), a leading cause of death and disability worldwide. Despite the prevalence of VTE, we do not fully understand what causes it and it is often overlooked as a major public health problem. Confirming and identifying risk factors associated with DVT is likely to lead to a reduction in the incidence, morbidity and mortality of VTE especially where these risk factors are modifiable. We can do this, by exploiting the availability of summary genetic data from genome-wide association studies (GWAS) of numerous phenotypes, including DVT, which permits hypothesis-free causal inference.\n\nObjectivesTo identify novel risk factors for DVT and to assess the causality of factors previously shown to be associated with DVT.\n\nMethodsTwo-sample Mendelian randomization (MR) was performed using summarised genetic data. Inverse variance weighted (IVW) estimates were calculated and validated by additional methods more robust to horizontal pleiotropy (MR Egger, simple mode, weighted mode, and weighted median). Bidirectional and heterogeneity sensitivity analyses were performed to further evaluate our findings.\n\nResultsForty-seven exposures passed an exposure-exposure correlation-adjusted Bonferroni P-value threshold (5.43E-05). These included previously hypothesised risk factors for DVT (e.g. body mass index, varicose veins, height, hyperthyroidism) and novel associations (e.g. prospective memory, basal metabolic rate).\n\nConclusionOur analyses confirmed causal associations of risk factors previously associated with DVT and highlighted several novel risk factors for the disease. Our study demonstrates the utility of using a hypothesis free Mendelian randomization approach for the identification of novel disease risk factors.

epidemiology↗

bioRxiv2018

Low-frequency variation in TP53 has large effects on head circumference and intracranial volume

Cranial growth and development affects the closely related traits of head circumference (HC) and intracranial volume (ICV). Here we model the developmental genetic architecture of HC, showing this is genetically stable and correlated with genetic determinants of ICV. Investigating up to 46,000 children and adults of European descent, we identify association with final HC and/or final ICV+HC at 9 novel common and low-frequency loci, illustrating that genetic variation from a wide allele frequency spectrum contributes to cranial growth. The largest effects are reported for low-frequency variants within TP53, with 0.5 cm wider heads in increaser-allele carriers versus non-carriers during mid-childhood.

genetics↗

bioRxiv2018

Association of intrauterine alcohol exposure and offspring depression:A negative control analysis of maternal and partner consumption.

BackgroundPrevious research has suggested that intrauterine alcohol exposure is associated with a variety of adverse outcomes in offspring. However, few studies have investigated its association with offspring internalising disorders in late adolescence.\n\nMethodsUsing data from the Avon Longitudinal Study of Parents and Children (ALSPAC), we investigated the associations of maternal drinking in pregnancy with offspring depression at age 18. We also examined partner drinking as a negative control for intrauterine exposure for comparison.\n\nResultsOffspring of mothers that consumed any alcohol at 18 weeks gestation were at increased risk of having a diagnosis of depression (OR 1.15, 95% CI 1.00 to 1.32), but there was no clear evidence of association between partners alcohol consumption during pregnancy and increased risk of offspring depression (OR 0.90, 95% CI 0.78 to 1.04).\n\nConclusionsMaternal drinking in pregnancy was associated with increased risk of offspring depression at age 18. Residual confounding may explain this association, but the negative control comparison of paternal drinking provides some evidence that it may be causal, and this warrants further investigation.

epidemiology↗

bioRxiv2018

Testing the causal effects between subjective wellbeing and physical health using Mendelian randomisation

ObjectivesTo investigate whether the association between subjective wellbeing (subjective happiness and life satisfaction) and physical health is causal.\n\nDesignWe conducted two-sample bidirectional Mendelian randomisation between subjective wellbeing and six measures of physical health: coronary artery disease, myocardial infarction, total cholesterol, HDL cholesterol, LDL cholesterol and body mass index (BMI).\n\nParticipantsWe used summary data from four large genome-wide association study consortia: CARDIoGRAMplusC4D for coronary artery disease and myocardial infarction; the Global Lipids Genetics Consortium for cholesterol measures; the Genetic Investigation of Anthropometric Traits consortium for BMI; and the Social Science Genetics Association Consortium for subjective wellbeing. A replication analysis was conducted using 337,112 individuals from the UK Biobank (54% female, mean age =56.87, SD=8.00 years at recruitment).\n\nMain outcome measuresCoronary artery disease, myocardial infarction, total cholesterol, HDL cholesterol, LDL cholesterol, BMI and subjective wellbeing.\n\nResultsThere was evidence of a causal effect of BMI on subjective wellbeing such that each 1 kg/m2 increase in BMI caused a 0.045 (95%CI 0.006 to 0.084, p=0.023) SD reduction in subjective wellbeing. Replication analyses provided strong evidence of an effect of BMI on satisfaction with health ({beta}=0.034 (95% CI: -0.042 to -0.026) unit decrease in health satisfaction per SD increase in BMI, p<2-16). There was no clear evidence of a causal effect between subjective wellbeing and the other physical health measures in either direction.\n\nConclusionsOur results suggest that a higher BMI lowers subjective wellbeing. Our replication analysis confirmed this finding, suggesting the effect in middle-age is driven by satisfaction with health. BMI is a modifiable determinant and therefore, our study provides further motivation to tackle the obesity epidemic because of the knock-on effects of higher BMI on subjective wellbeing.

epidemiology↗