Genome-wide association studies (GWAS) have been instrumental in highlighting associations between genetic variants and 1000s of traits. A recent GWAS of major depressive disorder (MDD) by the psychiatric genetics consortium (PGC) has recently identified 44 genetic variants associated with the disorder (Wray et al., 2018). We plan to include ALSPAC data from the mothers and the children in the next round of analysis for both forthcoming MDD and anxiety PGC GWAS. We will prepare summary statistics from the GWAS to be shared with the PGC and perform subsequent in cohort analysis. This will be a big step towards incorporating ALSPAC data into psychiatric genetics. The summary statistics will contain no identifiable information.

Learning disabilities are common disorders characterized by unexpected difficulty with a specific mode of learning in the context of adequate intelligence and academic opportunity. The high prevalence of these disorders in the general population represents a costly burden to the educational system and affected individual are often at risk for long-term adverse psychological and socioeconomic outcomes. Intervention programs work, but are more effective when tailored to individuals and administered earlier in life. The pre-symptomatic detection of individual who are at risk of developing learning disabilities, and who are more likely to benefit from early intervention, is therefore an important diagnostic opportunity with major economic and societal implications. The objective of this project is to evaluate the diagnostic performance and predictive value of genetic variants associated with learning disabilities in the ALSPAC cohort.

Human evolution has been associated with drastic changes in environment and lifestyle over time, with each of these changes resulting in selective pressures (Voight et al., 2006). Natural selection is the differential reproductive success of genetically distinct individuals or genotypes within a population. Strongly deleterious mutations will rapidly be eliminated from populations, whereas strongly positive mutations will quickly rise to fixation leading to changes in allele frequency over time. This process leaves signatures on the genome which can then be detected (Sabeti et al., 2006).

Epigenetics refers to heritable changes outside of the DNA sequence itself and provides a potential mechanism by which environmental and lifestyle exposures can impact gene expression over the course of a lifetime. Epigenetic mechanisms can include DNA methylation and histone modifications. DNA methylation is the most widely studied epigenetic change and involves the addition of methyl groups to nucleotide bases (Vocht et al., 2018).

Natural selection is a long term, multigenerational response to environmental factors that can influence the role of genes in human traits (Bamshed and Wooding, 2003) whereas epigenetic inheritance allows stable changes in DNA methylation to be passed from one generation to the next (Feil and Fraga, 2012). Both selection and methylation act in response to environmental exposures but over different timescales. This project will aim to unravel the interplay between selection and methylation to assess whether DNA methylation offers a mechanism to respond to exposures in the short term which may eventually lead to changes in allele frequency.

References
1. Bamshad, M. & Wooding, S.P. Signatures of natural selection in the human genome. Nature Reviews Genetics 4, 99-111A (2003).
2. de Vocht, F. et al. DNA methylation from birth to late adolescence and development of multiple-risk behaviours. Journal of Affective Disorders227, 588-594 (2018).
3. Feil, R. & Fraga, M.F. Epigenetics and the environment: emerging patterns and implications. Nature Reviews Genetics 13, 97-109 (2012).
4. Sabeti, P.C. et al. Positive natural selection in the human lineage. Science 312, 1614-1620 (2006).
5. Stearns, S.C., Byars, S.G., Govindaraju, D.R. & Ewbank, D. Measuring selection in contemporary human populations (vol 11, pg 611, 2010). Nature Reviews Genetics 12, 1 (2011).
6. Voight, B.F., Kudaravalli, S., Wen, X.Q. & Pritchard, J.K. A map of recent positive selection in the human genome (vol 4, pg 154, 2006). Plos Biology 4, 659-659 (2006).

We want to quantify what the impact of genetics across the whole genome has on weight and risk of severe obesity from birth to middle adulthood.

The current recommendation is that we should limit our intake of free sugars to provide less than 10% of daily energy intake. This study will investigate free sugars intake in ALSPAC children at ages 18 months, 3.5, 5, 7, 10 & 13 years and determine whether those consuming less than 10% energy from free sugars have a more beneficial nutrient and food group intake than those that consuming more free sugars. Then to investigate if there are differences in obesity levels in relation to free sugars intakes.

Research suggests that children who reach puberty early are at risk of poorer health and wellbeing. Such ‘early maturing’ children also tend to take part in riskier health behaviours, such as smoking, drinking alcohol and risky sexual activity. However, most studies look at these links at a single point in time, which limits how confident we can be that early maturing causes poorer wellbeing. Few studies have looked at what factors may increase or decrease some of these risks. Our study aims to use the ALSPAC data explore three questions relating to the longer-term health risks of adolescents who mature early. The first builds on work we have done with parents, showing many believe we should judge whether or not a child is overweight differently if they are an early maturer, to avoid labeling them as overweight when they are not. This is a particularly important time to consider how we respond to children's weights, as it is the point at which over 95% of children in England are weighed and measured as part of the National Child Measurement Programme. Some parents have been strong critics of this process, so public health teams and parents alike are interested in exploring how we could interpret and use this information better. To explore whether it is appropriate to use the same means of classifying early and on-time maturers as overweight, we will apply an adjustment for maturity to our calculations of weight status for children at age 11, and explore whether this is a better way of predicting which children will have higher/lower future health risks at age 17 (including obesity, blood pressure and other risk factors). Second, we will compare which factor at at age 10/11 is the stronger predictor of wellbeing in later adolescence; being overweight, or maturing early. Finally, to explore what factors might help children to avoid some of the potential consequences of maturing early, we will look at whether children’s views of the strength and importance of their relationships with parents and peers influence the effect of being an early maturer on their wellbeing by the age of 17 (including depression, positive wellbeing, and risk behaviours such as smoking and drinking).

LifeCycle is a cross-cohort collaboration which brings together data from pregnancy and child cohorts from across Europe and also Australia to facilitate studies on the influence of early-life exposures on cardio-metabolic, respiratory and mental health outcomes. The end product of this collaboration is a sustainable data resource known as the EU Child Cohort Network.
In the proposed paper we provide a detailed description of the EU Child Cohort Network’s core variables; a set of basic variables, derivable by the majority of participating cohorts and frequently needed as covariates in life-course research. We firstly describe the process adopted to establish a list of core variables and the protocol developed to harmonise these core data, thus making them interoperable. This protocol also defines the harmonisation process adopted generally within LifeCycle. Secondly, we describe the catalogue developed to ensure that all EU Child Cohort Network data are both findable and reusable. Finally, we describe the core data themselves, including the proportion of variables harmonised by each cohort and the number of children with harmonised data.
We would also like to provide some summary statistics (N and % for categorical variables, and N, mean, standard deviation for continuous variables) on some key variables (namely, sex, maternal education at baseline, mother’s ethnic background, mother’s parity, mother’s smoking in pregnancy, size for gestational age, whether the index child was ever breastfed, age of the mother at birth, birth weight and gestational age). These variables have already been harmonised as part of the LifeCycle project. To obtain the requested summary statistics, we have prepared some R code for individual cohorts to run on their harmonised datasets.
The paper is already written and we hope to submit it to the Journal of Epidemiology in the summer.

Anti-sclerostin antibody treatment has recently been licensed as a monthly injection for treating osteoporosis (Evenity), a condition in which bones become fragile and more susceptible to fracture. Though effective at treating osteoporosis, concerns have been raised that Evenity increases the risk of developing cardiovascular disease (CVD) and stroke, either via a direct effect on arteries, or by modifying associated risk factors. This project aims to examine this question, by studying whether circulating levels of sclerostin are related to CVD end-points, related phenotypes and risk factors. This will be achieved by examining these relationships in a range of independent cohorts, including ALSPAC. Furthermore, we aim to triangulate our findings by Mendelian Randomisation, using a genetic instrument for circulating sclerostin which we recently published and are currently refining.