Tyrosine intake and cardiovascular responses in a motivated performance situation

Ingesting the catecholamine precursor tyrosine can prevent decrements in, or improve, cognitive and motor performance in demanding situations. Furthermore, the biopsychosocial model of challenge and threat specifies that adrenal medullary catecholamine release plays a central role in the occurrence of a challenge state, which has been linked to better performance under pressure than a threat state. The present study thus examined whether acute tyrosine intake impacts upon challenge and threat states or influences cognitive and motor performance independently. A double-blind randomised crossover design with 49 participants (33 males; µage = 22.5 years, SD = 5.0) was used. Participants ingested tyrosine or placebo (150mg/kg body mass) 60 minutes before performing the N-Back task and a bean-bag throwing task. Cognitive self-reports and cardiovascular data before each task provided indicators of challenge and threat states. There were no significant differences between tyrosine and placebo on the cognitive and cardiovascular challenge and threat variables. Generalised Estimating Equations analyses found that tyrosine was associated with better performance than placebo on the bean-bag throwing task, but not on the N-Back task. A significant interaction effect showed that challenge and threat states were more positively related to performance in the placebo condition than in the tyrosine condition. This suggests that tyrosine may have attenuated the detrimental effect of a threat state. The present study breaks new ground in relating the impact of a dietary supplement to challenge and threat states and finding that tyrosine may in some cases attenuate the negative effects of a threat state

DNA Methylation: Methods and Analyses

Epigenome-wide Association Studies (EWAS) have been a popular method to investigate the genome over the past decade. From these experiments, more than 75,000 samples have been assayed using the high-throughput, cost-effective HumanMethylation450 microarray (450k) developed by Illumina. With the recent release of the HumanMethylationEPIC microarray, the size of data is expected to increase considerably so advances are needed in the methodologies used to analyse such data. The first part of this thesis focuses on the development of tools that can be used for the analysis of DNA methylation microarray data. Firstly I develop a wide range of tools that can be used to quality control data. These tools focus specifically on data-driven aspects of quality control that are often overlooked and can cause problems during downstream analysis. Comparison of these tools to other popular methods demonstrate that the tools I created are effective in decreasing test statistic inflation while conserving the largest number of samples (Chapter 2). Secondly to accommodate the increase in the size of data, I developed the bigmelon R package which reduces the amount of memory required to perform the analysis typically required of EWAS (Chapter 3). I then demonstrate how both the tools described in Chapters 2 and 3 can be used in EWAS settings. I perform an EWAS between DNA methylation and various blood-lipid traits and statin-use on a dataset comprising of 1,193 samples from the Understanding Society: UK Household Longitudinal study and replicate the findings of many previous EWAS (Chapter 4). Lastly, I demonstrate how the data from tens of thousands of microarrays can be utilised in preliminary analyses that focus on the wide-spread characterisation of the probes on the 450k microarray and how tissue-specific DNA methylation patterns may correlate with tissue-specific gene expression (Chapter 5)

The DNA methylome of human sperm is distinct from blood with little evidence for tissue-consistent obesity associations

Epidemiological research suggests that paternal obesity may increase the risk of fathering small for gestational age offspring. Studies in non-human mammals indicate that such associations could be mediated by DNA methylation changes in spermatozoa that influence offspring development in utero. Human obesity is associated with differential DNA methylation in peripheral blood. It is unclear, however, whether this differential DNA methylation is reflected in spermatozoa. We profiled genome-wide DNA methylation using the Illumina MethylationEPIC array in a cross-sectional study of matched human blood and sperm from lean (discovery n = 47; replication n = 21) and obese (n = 22) males to analyse tissue covariation of DNA methylation, and identify obesity-associated methylomic signatures. We found that DNA methylation signatures of human blood and spermatozoa are highly discordant, and methylation levels are correlated at only a minority of CpG sites (~1%). At the majority of these sites, DNA methylation appears to be influenced by genetic variation. Obesity-associated DNA methylation in blood was not generally reflected in spermatozoa, and obesity was not associated with altered covariation patterns or accelerated epigenetic ageing in the two tissues. However, one cross-tissue obesity-specific hypermethylated site (cg19357369; chr4:2429884; P = 8.95 × 10^{-8}; 2% DNA methylation difference) was identified, warranting replication and further investigation. When compared to a wide range of human somatic tissue samples (n = 5,917), spermatozoa displayed differential DNA methylation across pathways enriched in transcriptional regulation. Overall, human sperm displays a unique DNA methylation profile that is highly discordant to, and practically uncorrelated with, that of matched peripheral blood. We observed that obesity was only nominally associated with differential DNA methylation in sperm, and therefore suggest that spermatozoal DNA methylation is an unlikely mediator of intergenerational effects of metabolic traits

Leveraging DNA-Methylation Quantitative-Trait Loci to Characterize the Relationship between Methylomic Variation, Gene Expression, and Complex Traits

Characterizing the complex relationship between genetic, epigenetic, and transcriptomic variation has the potential to increase understanding about the mechanisms underpinning health and disease phenotypes. We undertook a comprehensive analysis of common genetic variation on DNA methylation (DNAm) by using the Illumina EPIC array to profile samples from the UK Household Longitudinal study. We identified 12,689,548 significant DNA methylation quantitative trait loci (mQTL) associations (p 60 human traits by using summary-data-based Mendelian randomization (SMR) to identify 1,662 pleiotropic associations between 36 complex traits and 1,246 DNAm sites. We also use SMR to characterize the relationship between DNAm and gene expression and thereby identify 6,798 pleiotropic associations between 5,420 DNAm sites and the transcription of 1,702 genes. Our mQTL database and SMR results are available via a searchable online database as a resource to the research community

Bigmelon:Tools for analysing large DNA methylation datasets

Motivation The datasets generated by DNA methylation analyses are getting bigger. With the release of the HumanMethylationEPIC micro-array and datasets containing thousands of samples, analyses of these large datasets using R are becoming impractical due to large memory requirements. As a result there is an increasing need for computationally efficient methodologies to perform meaningful analysis on high dimensional data. Results Here we introduce the bigmelon R package, which provides a memory efficient workflow that enables users to perform the complex, large scale analyses required in epigenome wide association studies (EWAS) without the need for large RAM. Building on top of the CoreArray Genomic Data Structure file format and libraries packaged in the gdsfmt package, we provide a practical workflow that facilitates the reading-in, preprocessing, quality control and statistical analysis of DNA methylation data. We demonstrate the capabilities of the bigmelon package using a large dataset consisting of 1193 human blood samples from the Understanding Society: UK Household Longitudinal Study, assayed on the EPIC micro-array platform. copy; 2018 The Author(s). Published by Oxford University Press.</p

Socioeconomic Position and DNA Methylation Age Acceleration across the Lifecourse.

Accelerated DNA methylation age is linked to all-cause mortality and environmental factors, but studies of associations with socioeconomic position are limited. Studies generally use small selected samples, and it is unclear how findings with two commonly used methylation age calculations (Horvath and Hannum) translate to general population samples including younger and older adults. In 1099 UK adults aged 28-98 y in 2011-12, we assessed the relationship of Horvath and Hannum DNA methylation age acceleration with a range of social position measures: current income and employment, education, income and unemployment across a 12-year period, and childhood social class. Accounting for confounders, participants less advantaged in childhood were epigenetically 'older' as adults: compared to participants with professional/managerial parents, Hannum age was 1.07 years higher (95% confidence interval (CI):0.20-1.94) for those with parents in semi-skilled/unskilled occupations, and 1.85 years higher (95%CI:0.67-3.02) for participants without a working parent at age 14. No other robust associations were seen. Results accord with research implicating early life circumstances as critical for DNA methylation age in adulthood. Since methylation age acceleration as measured by the Horvath and Hannum estimators appears strongly linked to chronological age, research examining associations with the social environment must take steps to avoid age-related confounding

Systematic under-estimation of the epigenetic clock and age acceleration in older subjects

Background: The Horvath epigenetic clock is widely used. It predicts age quite well from 353 CpG sites in the DNA methylation profile in unknown samples and has been used to calculate 'age acceleration’ in various tissues and environments. Results: The model systematically underestimates age in tissues from older people. This is seen in all examined tissues but most strongly in the cerebellum and is consistently observed in multiple datasets. Age acceleration is thus age-dependent, and this can lead to spurious associations. The current literature includes examples of association tests with age acceleration calculated in a wide variety of ways. Conclusions: The concept of an epigenetic clock is compelling, but caution should be taken in interpreting associations with age acceleration. Association tests of age acceleration should include age as a covariate

Guidance for DNA methylation studies: statistical insights from the Illumina EPIC array

Background There has been a steady increase in the number of studies aiming to identify DNA methylation differences associated with complex phenotypes. Many of the challenges of epigenetic epidemiology regarding study design and interpretation have been discussed in detail, however there are analytical concerns that are outstanding and require further exploration. In this study we seek to address three analytical issues. First, we quantify the multiple testing burden and propose a standard statistical significance threshold for identifying DNA methylation sites that are associated with an outcome. Second, we establish whether linear regression, the chosen statistical tool for the majority of studies, is appropriate and whether it is biased by the underlying distribution of DNA methylation data. Finally, we assess the sample size required for adequately powered DNA methylation association studies. Results We quantified DNA methylation in the Understanding Society cohort (n = 1175), a large population based study, using the Illumina EPIC array to assess the statistical properties of DNA methylation association analyses. By simulating null DNA methylation studies, we generated the distribution of p-values expected by chance and calculated the 5% family-wise error for EPIC array studies to be 9 × 10⁻⁸. Next, we tested whether the assumptions of linear regression are violated by DNA methylation data and found that the majority of sites do not satisfy the assumption of normal residuals. Nevertheless, we found no evidence that this bias influences analyses by increasing the likelihood of affected sites to be false positives. Finally, we performed power calculations for EPIC based DNA methylation studies, demonstrating that existing studies with data on ~ 1000 samples are adequately powered to detect small differences at the majority of sites. Conclusion We propose that a significance threshold of P < 9 × 10⁻⁸ adequately controls the false positive rate for EPIC array DNA methylation studies. Moreover, our results indicate that linear regression is a valid statistical methodology for DNA methylation studies, despite the fact that the data do not always satisfy the assumptions of this test. These findings have implications for epidemiological-based studies of DNA methylation and provide a framework for the interpretation of findings from current and future studies

Uncertainty quantification of reference-based cellular deconvolution algorithms

This is the final version. Available on open access from Routledge via the DOI in this recordData and code availability: The DNAm data used in this study are available as R packages or via GEO (see Supplementary Table 2 for details). We have provided the code for calculating the CETYGO score as an R package available via GitHub (https://github.com/ds420/CETYGO). The code to reproduce the analyses in this manuscript using our R package are also available via GitHub (https://github.com/ejh243/CETYGOAnalyses).The majority of epigenetic epidemiology studies to date have generated genome-wide profiles from bulk tissues (e.g., whole blood) however these are vulnerable to confounding from variation in cellular composition. Proxies for cellular composition can be mathematically derived from the bulk tissue profiles using a deconvolution algorithm; however, there is no method to assess the validity of these estimates for a dataset where the true cellular proportions are unknown. In this study, we describe, validate and characterize a sample level accuracy metric for derived cellular heterogeneity variables. The CETYGO score captures the deviation between a sample's DNA methylation profile and its expected profile given the estimated cellular proportions and cell type reference profiles. We demonstrate that the CETYGO score consistently distinguishes inaccurate and incomplete deconvolutions when applied to reconstructed whole blood profiles. By applying our novel metric to >6,300 empirical whole blood profiles, we find that estimating accurate cellular composition is influenced by both technical and biological variation. In particular, we show that when using a common reference panel for whole blood, less accurate estimates are generated for females, neonates, older individuals and smokers. Our results highlight the utility of a metric to assess the accuracy of cellular deconvolution, and describe how it can enhance studies of DNA methylation that are reliant on statistical proxies for cellular heterogeneity. To facilitate incorporating our methodology into existing pipelines, we have made it freely available as an R package (https://github.com/ds420/CETYGO).Biotechnology and Biological Sciences Research Council (BBSRC)Engineering and Physical Sciences Research Council (EPSRC)Medical Research Council (MRC)Alzheimer’s Societ

DNA methylation-based sex classifier to predictsex and identify sex chromosome aneuploidy

Background Sex is an important covariate of epigenome-wide association studies due to its strong influence on DNA methylation patterns across numerous genomic positions. Nevertheless, many samples on the Gene Expression Omnibus (GEO) frequently lack a sex annotation or are incorrectly labelled. Considering the influence that sex imposes on DNA methylation patterns, it is necessary to ensure that methods for filtering poor samples and checking of sex assignment are accurate and widely applicable. Results Here we presented a novel method to predict sex using only DNA methylation beta values, which can be readily applied to almost all DNA methylation datasets of different formats (raw IDATs or text files with only signal intensities) uploaded to GEO. We identified 4345 significantly (p<0.01) sex-associated CpG sites present on both 450K and EPIC arrays, and constructed a sex classifier based on the two first principal components of the DNA methylation data of sex-associated probes mapped on sex chromosomes. The proposed method is constructed using whole blood samples and exhibits good performance across a wide range of tissues. We further demonstrated that our method can be used to identify samples with sex chromosome aneuploidy, this function is validated by five Turner syndrome cases and one Klinefelter syndrome case. Conclusions This proposed sex classifier not only can be used for sex predictions but also applied to identify samples with sex chromosome aneuploidy, and it is freely and easily accessible by calling the ‘estimateSex’ function from the newest wateRmelon Bioconductor package (https://github.com/schalkwyk/wateRmelon)