The epigenetic clock and physical development during childhood and adolescence: longitudinal analysis from a UK birth cohort.

Background: Statistical models that use an individual's DNA methylation levels to estimate their age (known as epigenetic clocks) have recently been developed, with 96% correlation found between epigenetic and chronological age. We postulate that differences between estimated and actual age [age acceleration (AA)] can be used as a measure of developmental age in early life.

Methods: We obtained DNA methylation measures at three time points (birth, age 7 years and age 17 years) in 1018 children from the Avon Longitudinal Study of Parents and Children (ALSPAC).

Collapsed methylation quantitative trait loci analysis for low frequency and rare variants.

Background: Single variant approaches have been successful in identifying DNA methylation quantitative trait loci (mQTL), although as with complex traits they lack the statistical power to identify the effects from rare genetic variants. We have undertaken extensive analyses to identify regions of low frequency and rare variants that are associated with DNA methylation levels.

Methods: We used repeated measurements of DNA methylation from five different life stages in human blood, taken from the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort.

Systematic identification of genetic influences on methylation across the human life course.

Background: The influence of genetic variation on complex diseases is potentially mediated through a range of highly dynamic epigenetic processes exhibiting temporal variation during development and later life. Here we present a catalogue of the genetic influences on DNA methylation (methylation quantitative trait loci (mQTL)) at five different life stages in human blood: children at birth, childhood, adolescence and their mothers during pregnancy and middle age.

Results: We show that genetic effects on methylation are highly stable across the life course and that developmental change in the genetic contribution to variation in methylation occurs primarily through increases in environmental or stochastic effects.

DNA Methylation and BMI: Investigating Identified Methylation Sites at HIF3A in a Causal Framework.

Multiple differentially methylated sites and regions associated with adiposity have now been identified in large-scale cross-sectional studies. We tested for replication of associations between previously identified CpG sites at HIF3A and adiposity in ∼1,000 mother-offspring pairs from the Avon Longitudinal Study of Parents and Children (ALSPAC). Availability of methylation and adiposity measures at multiple time points, as well as genetic data, allowed us to assess the temporal associations between adiposity and methylation and to make inferences regarding causality and directionality.

Prenatal and early life influences on epigenetic age in children: a study of mother-offspring pairs from two cohort studies.

DNA methylation-based biomarkers of aging are highly correlated with actual age. Departures of methylation-estimated age from actual age can be used to define epigenetic measures of child development or age acceleration (AA) in adults. Very little is known about genetic or environmental determinants of these epigenetic measures of aging.

Longitudinal analysis of DNA methylation associated with birth weight and gestational age.

Gestational age (GA) and birth weight have been implicated in the determination of long-term health. It has been hypothesized that changes in DNA methylation may mediate these long-term effects. We obtained DNA methylation profiles from cord blood and peripheral blood at ages 7 and 17 in the same children from the Avon Longitudinal Study of Parents and Children.

Maternal pre-pregnancy BMI and gestational weight gain, offspring DNA methylation and later offspring adiposity: findings from the Avon Longitudinal Study of Parents and Children.

Background: Evidence suggests that in utero exposure to undernutrition and overnutrition might affect adiposity in later life. Epigenetic modification is suggested as a plausible mediating mechanism.

Methods: We used multivariable linear regression and a negative control design to examine offspring epigenome-wide DNA methylation in relation to maternal and offspring adiposity in 1018 participants.

Prenatal exposure to maternal smoking and offspring DNA methylation across the lifecourse: findings from the Avon Longitudinal Study of Parents and Children (ALSPAC).

Maternal smoking during pregnancy has been found to influence newborn DNA methylation in genes involved in fundamental developmental processes. It is pertinent to understand the degree to which the offspring methylome is sensitive to the intensity and duration of prenatal smoking. An investigation of the persistence of offspring methylation associated with maternal smoking and the relative roles of the intrauterine and postnatal environment is also warranted.

Glutamate concentration in the medial prefrontal cortex predicts resting-state cortical-subcortical functional connectivity in humans.

Communication between cortical and subcortical regions is integral to a wide range of psychological processes and has been implicated in a number of psychiatric conditions. Studies in animals have provided insight into the biochemical and connectivity processes underlying such communication. However, to date no experiments that link these factors in humans in vivo have been carried out.

Using XML to encode TMA DES metadata.

Background: The Tissue Microarray Data Exchange Specification (TMA DES) is an XML specification for encoding TMA experiment data. While TMA DES data is encoded in XML, the files that describe its syntax, structure, and semantics are not. The DTD format is used to describe the syntax and structure of TMA DES, and the ISO 11179 format is used to define the semantics of TMA DES.

The tissue microarray data exchange specification: Extending TMA DES to provide flexible scoring and incorporate virtual slides.

Background: Tissue MicroArrays (TMAs) are a high throughput technology for rapid analysis of protein expression across hundreds of patient samples. Often, data relating to TMAs is specific to the clinical trial or experiment it is being used for, and not interoperable. The Tissue Microarray Data Exchange Specification (TMA DES) is a set of eXtensible Markup Language (XML)-based protocols for storing and sharing digitized Tissue Microarray data.

Extending the tissue microarray data exchange specification for inclusion of data analysis results.

Background: The Tissue Microarray Data Exchange Specification (TMA DES) is an eXtensible Markup Language (XML) specification for encoding TMA experiment data in a machine-readable format that is also human readable. TMA DES defines Common Data Elements (CDEs) that form a basic vocabulary for describing TMA data. TMA data are routinely subjected to univariate and multivariate statistical analysis to determine differences or similarities between pathologically distinct groups of tumors for one or more markers or between markers for different groups.