Although cardiovascular disease (CVD) generally manifests in adulthood, the development of cardiovascular pathology is known to start in early life. Higher mean blood pressure (BP) is an established risk factor for CVD. The magnitude of association for BP measured in adolescence/early adulthood with CVD incidence and mortality is the same as the association for BP measured in mid-life or older adulthood, with evidence from prospective cohort studies, RCTs and Mendelian Randomization highlighting the importance of cumulative risk with longer duration of high BP, and the importance of early life interventions to prevent age-related increases. Studies in adults have also demonstrated that greater within-individual variability of BP over a 24-hour period is a risk factor for CVD independently of mean level. BP ‘dipping’ is the degree to which BP is lower during the night compared with the daytime; lower dipping is also associated with CVD risk independent of mean BP level. Much less is known about BP variability and BP dipping within children and adolescents, with the available evidence coming from a small number of studies from the USA.

Cognitive functions are associated with various life outcomes. It is well known that cognitive functions are influenced by genetic variation and are heritable. The largest studies to date have been conducted by the CHARGE-Cognitive Working Group, providing large number of genetic variants. The CHARGE-Cognitive Working Group have invited ALSPAC to contribute towards their effort to perform GWAS meta-analysis on cognitive function, and they have already recruited 90,000+ samples from various other cohorts. Along with CHARGE consortium, this study is aim to investigate genetic variation that related to cognitive function using ALSPAC. This study allows ALSPAC to contribute to valuable research and to reveal more significant genetic variants by increasing sample size.

Self-organising maps (or neural networks) are analytical approaches that seek to describe structure in otherwise complex data sets. Proton NMR based lipidomics and metabolomics are just such phenotypes and are measured at multiple time points and across generations in ALSPAC (a unique resource). The proposed work will aim to look at the impact of body mass index (BMI) on the metabolome (though both observational and genetic analyses), but to characterise this impact using self-organising maps. This is an approach currently being developed by our close collaborators (Johannes Kettunen and Mika Ala-Korpela at the University of Oulu) and one we wish to employ in light of the properties of the ALSPAC data and in light of future work concerned with the same basic problem but with the added complication of metabolic challenge.

Vitamin D is a steroid hormone considered critical for brain development. A link between vitamin D deficiency and offspring autism has been hypothesised and may be biologically plausible. However, very few studies have tested this association and the results have been inconsistent (Whitehouse et al 2012, Magnusson et al 2016, Gale et al 2008, Vinkhuyzen et al 2016), although do suggest further work on this topic is warranted. Considering Vitamin D supplementation may be scaleable at low cost, it is important to study whether any associations between Vit D deficiency during pregnancy and adverse offspring outcomes are likely to be causal.

This proposal contains three projects:

These are the research dissertations for three iBSc students who have been given access to their own 23&me genotype data sets. In the analyses of these, they wish to compare to an essentially annonymised and representative population based collection. As a result, we are seeking permission to generate three "cid" linked releases of the HRC imputed YP GWAS data for these students for supervised analysis. No phenotypic data will be required and this will be solely for the analysis of genetic data as a comparator set.

A woman’s health during pregnancy is of vital importance, not only for the mother’s short and long-term health but also for the children born of the pregnancy. Research has shown that women who develop gestational diabetes are more likely to have type 2 diabetes in later life, and greater weight gain during pregnancy is related to higher BMI. In addition to this. Women who gestational diabetes or greater weight gain during pregnancy also have more complications during birth.
Presently, there is very little information on how mothers’ dietary intake is related to the risk of developing gestational diabetes (or poor glucose control) and weight gain during pregnancy.

Physical activity is widely linked with better metabolic functioning, but whether these links reflect distinct causal effects is unclear. In the absence of robust genetic instruments for physical activity, we aim to examine habitual levels of physical activity across adolescence in relation to future metabolic profiles in young adulthood. To do this, we aim to use 3 repeated measures of objectively assessed physical activity (total, light, moderate-to-vigorous, and sedentary time), along with 2 repeated measures of objectively assessed fitness and lean body mass, and to relate these to a wide array of blood-based metabolic markers at follow-up (and to change in these from historical assessments) in the form of clinically relevant traits including fasting insulin and over 80 metabolites including branched chain amino acids. The ALSPAC cohort of children is an ideal resource for carrying out this study given availability of repeated objective measures and the young age of participants which allows associations to be estimated with minimal bias due to subclinical disease.

Measures of the size and composition of the human body (such as body mass index or waist to hip ratio) are heritable and associated with many diseases. Similarly, measures of the quantity and composition of fats in the bloodstream (such as total cholesterol) are heritable and predispose to disease. Understanding the genetics of these traits would help understand the biology of body composition and blood fat traits, and would help understand how and why these traits relate to disease.
Two large international consortia have been formed to perform studies on the genetics of measures of the human body (the GIANT consortium) and measures of blood fats (GLGC). These consortia successfully identified a large number of genetic variants which influence these traits and published findings between 2013 and 2015.
Since then, newer methods have been developed which have improved our ability to look at rare genetic variants.
GIANT and GLGC have requested new analysis. This will improve the original analysis by
a) including analysis of rarer genetic variants; b) increasing the sample size to approximately one million participants and c) allow better understanding of the genetics of body composition and blood fats in different ethnic groups.
This project aims to undertake analysis in ALSPAC on behalf of GIANT and GLGC, allowing ALSPAC to contribute to this important research.

DNA methylation is a stable but reversible chemical modification of DNA that changes the way DNA sequence is interpreted within a cell. It plays multiple key roles in human biology, acting for example as a mechanism for allowing cells to differentiate into specific types (e.g. skin cells) and to maintain their differentiated identities. It was recently discovered that DNA methylation patterns in the genome change with age and can be used to accurately estimate the age of a person. Although many studies have investigated these estimates in great detail, particularly the cases where there is a discrepancy between actual and estimated age, few studies have more broadly considered the ways that DNA methylation changes throughout the lifecourse and how those changes relate to development and aging. The few that have either consider only aging in adulthood or short periods such as puberty in childhood. We propose to comprehensively investigate DNA methylation change from birth to adolescence using DNA methylation profiles from ALSPAC measured at birth, age 7y and age 15-17y (as part of ARIES: Accessible Resource for Integrated Epigenomic Studies) and from the Generation R cohort study in Rotterdam (http://www.generationr.nl/) measured at birth, age 6y and age 9y. We will use statistical models to identify regions of the genome with similar patterns of change over time (e.g. consistently increasing or decreasing) both within and between the sexes and then investigate the possible biological implications of these patterns by linking them to known developmental stages and the functions of affected genes. This set of regions and their patterns will inform future follow-up studies investigating molecular associations with childhood exposures, phenotypes and outcomes.