This is an application to clean the self-harm data from the CCS questionnaire, in order to provide consistent data for the resource. This work is essential to ensure accuracy of the data and to provide consistency - both across projects, and across waves of data collection. Although some of the data has been cleaned previously, the changes have not been assimilated into the main ALSPAC dataset. We would like to finish off this work by cleaning the remaining items, and coding the free-text responses

In youth, due to the changes in height and weight during the developmental period, mathematically modeling BMI is complex. Historically, this question has been addressed from one of two perspectives: (1) focused on the proper modeling but suffered from poor interpretability, and (2) focused on the applied, translational appeal but suffered from inadequate modeling. No studies to date have combined the strengths of the two perspectives in modeling children’s growth and development. Thus, this research program will aim to (1) using a large birth cohort, extend the existing models to incorporate all periods across the developmental period with a focus on the translational aspects of BMI and obesity risk, (2) cross-validate the model in another large, separate birth cohort (of which data is already obtained) and (3) compare the performance of the new model with pre-existing cross-sectional growth curves to predict BMI and adiposity changes from DXA. The current program will provide new, validated metrics to track and predict obesity risk across childhood. Study results will be used to study genetic, environmental- and behavioural-induced changes in growth across the developmental period. Given the rapid rise in childhood obesity, the applied statistical work developed here is expected to vastly improve the methodologies to characterize longitudinal growth in children.

This project aims to look at whether blood markers might be associated with two other difficulties seen in adolescence: “psychotic experiences” and academic difficulties in school. Both academic/cognitive difficulties in school and psychotic experiences in adolescence are associated with later higher risk for severe mental disorders. Better understanding the biology behind these associations has the potential to help doctors and others to identify who is at highest risk for later problems and to intervene to help them before the difficulties become more severe.

The information collected during the ALSPAC study can be used to answer questions that were not even imagined when the study began. One way that scientists attempt to understand complex systems, and our bodies are complex systems, is by observation. Another approach is to perform experiments but experiments with humans is very expensive and so only small numbers of people are studied. The ALSPAC study is different. By recording lots of facts about a great many babies, and continuing to collect facts from them into adulthood, it is possible to spot patterns that would otherwise be missed.

So, we all eat everyday. Indeed, we have to eat often to remain healthy. We don't all choose the same foods and parents certainly don't all choose the same foods for their children and so we can ask if these differences in early diet may influence the growth, development and health of children. The ALSPAC study has a large enough group of children that broad differences in early diet may result in recognisable patterns of growth and health. We are particularly interested to know if the amount of sugar or sweet-tasting foods in early life results in children actively choosing foods that are sweet as they get older. We are also interested to know if these food choices make it more or less likely that children will grow fatter or have a higher risk of diseases like diabetes.

A blood sample contains a large amount of information about the donor. Full blood counts tap into a small but medically important subset of this information such as the relative proportions of several types of white blood cells in a blood sample: neutrophils, lymphocytes, monocytes, eosinophils, and basophils. Their relative proportions often reflect what is going on the body. For example, high neutrophil counts may indicate a bacterial infection and high eosinophil counts an allergic response. Patterns of methylated and unmethylated cytosines in genomic DNA extracted from white blood cells provide another source of information in a blood sample. These patterns can not only differentiate between cells of different types but also between individuals with different specific phenotypes such as age and BMI and exposure histories such as smoking behaviour. Some phenotypes and exposures have such distinct patterns that the patterns can be used as proxies for the original phenotype or exposure. Proxies for cell counts are the easiest to develop because methylation patterns differ dramatically between cell types. These cell type specific patterns include so much of the genome that they often obscure patterns linked to other phenotypes and exposures. Fortunately in some cases the pattern is only partially obscured and can be rescued by including cell count information in mathematical models. Although directly measured cell counts are unavailable in ALSPAC, they are being generated as part of the ALSPAC Focus @ 24/25 YP Clinic for a subset of the ALSPAC young people. We plan to use these measurements to derive and validate an algorithm for deriving DNA methylation proxies for cell counts in peripheral blood.

Attention-deficit/hyperactivity disorder (ADHD) is a prevalent developmental disorder, associated with a range of long-term impairments. However, the potential role of DNA methylation, an epigenetic mechanism, in ADHD symptoms is currently unclear. We plan to examine peripheral measures of DNA methylation at birth and ADHD symptoms (4–18 years) in different cohorts (GenerationR, ALSPAC, other PACE cohorts). Findings could lend novel insights into the epigenetic landscape of ADHD symptoms.

In the UK it is estimated that 25% of adults and 20% of children experience insufficient sleep. Insufficient and/or disrupted sleep has been linked to many aspects of human health and wellbeing. Much of the evidence about sleep and health comes from small scale studies, and little is understood about factors that influence sleep in the general population, and how such influences might vary across different stages of the lifecourse. The proposed work will look at the influences on, and consequences of sleep across the lifecourse, by exploiting rich contextual, health, education and biomarker data from the UK’s longitudinal studies.

Identifying phenotypic correlations between complex traits and diseases can provide useful etiological insights and help correct multiple testing. Lack of centralized individual-level phenotypes database makes it almost impossible to estimate phenotypic correlations across human traits and diseases as a whole picture. A useful alternative is to use the genome-wide association study (GWAS) summary statistics to estimate phenotypic correlations via the method metaCCA. We applied this method to the centralized GWAS summary results database we created to estimate 358,281 phenotypic correlations among 846 traits and diseases. The atlas of phenotypic correlations systematically scan hypotheses across a large scale of human traits and diseases, which can further tested using methods such as Mendelian randomization, LD score regression and PheWAS. The matrix also informed the selection of covariates for genetic, epigenetic and epidemiology analysis. In addition, we compared the phenotypic correlation and genetic correlation amongst 173 traits. The results of metaSpD suggest a 562 number of independent variable across 846 traits and diseases (P-value threshold of 9e-05). Additionally, metaSpD includes principal-component analysis which enables selection of a subset of traits in a complex molecular network, e.g. metabolites.

Mood disorders such as anxiety and depression are frequent among women, especially during pregnancy. Up to 20 % of pregnant women may experience symptoms of depression with approximately 7% of these women experiencing major depression. Relevant treatment of these disorders is very important, both to secure maternal and consequently fetal health, but also because maternal depression and anxiety may have adverse effects on child development. Therefore an increasing number of women are using antidepressant medication during pregnancy. There is however some evidence that these drugs may lead to congenital defects as well as adverse neurodevelopmental outcomes in the children having been exposed to antidepressant medication during pregnancy.
One possible causal link between exposure to antidepressant drugs during pregnancy and adverse outcomes in infancy and childhood could be exposure induced epigenetic change, regulating gene expression. Epigenetics is the study of molecular modifications to the DNA itself or the protein complex that the DNA is wrapped around. Such modifications do not alter the underlying DNA sequence but heavily impacts gene activity and translation into the proteins that composes the structures of the human body.
The objective of this study is to determine whether there is a difference in the epigenetic pattern between offspring exposed to antidepressant drugs during fetal life, those exposed to maternal depression but not to treatment, and children of mothers without depression or treatment.
High quality epidemiological data and evaluation of epigenetic pathways provide unique ways to potentially link maternal exposure to antidepressant medication to later adverse outcomes. This may provide new knowledge much needed when giving advice to depressed or anxious women planning pregnancy or already pregnant on how to carry on with their treatment. These methods can also be used in future evaluation of the effects of other types of medication given to women during pregnancy and lactation.