Around one-third of young people in the UK have suffered intimate partner violence (emotional, physical, or sexual abuse from a romantic partner) by the time they turn 21. This is more likely for those who have had symptoms of anxiety or depression during their teenage years. A common form of support for anxiety or depression at this age is through the GP - who might offer either psychological therapy (like counselling sessions, or cognitive behavioural therapy), or drug treatment (such as anti-depressants), to try to reduce anxiety and depression symptoms. This might also reduce likelihood of intimate partner violence later on, but there so far this has not been studied.

Early-life urban stressors have been identified as a risk to the onset of non-communicable diseases. An accumulating body of evidence is suggestive for health-promoting effects of exposure to green spaces. However, the association between green spaces and harmful or beneficial effects on children health is still poor for most outcomes, and the underlying mechanisms remain unclear. Outcome wide analysis have recently been proposed as a way to evaluate the effects of exposures over numerous important outcomes. Indeed, some exposures may be harmful for some outcomes and beneficial for others, including green spaces exposures. This approach also has the advantage to better control for confounding for the effects of the exposure on all outcomes simultaneously. It is different from the traditional one-outcome approach and more similar to GWAS (genome wide associations) or EWAS (epigenome wide associations) or ExWAS (exposome wide associations). For outcome-wide approaches, larger datasets are needed so LifeCycle is a good opportunity to apply this novel method. The idea is to add as many outcomes as possible, this is the great interest of the outcome wide approach.

Myopia, or short-sightedness, is one of the most common causes of sight impairment worldwide. By 2050, five billion people- half the world’s population- will be short-sighted, compared to ~1.4 billion people today.
People with myopia have relatively long eyes, so that light is focused in front of the retina instead of directly onto it. Since the eye continues to grow throughout childhood, someone who develops myopia as a child will continue to get worse as they grow older, with greater risk of sight-threatening complications.
Previously, we showed that for each additional year we spend in education, the more myopic we become, on average, as a population. Evidence from other studies suggests that this may be because we spend less time outside, reducing our exposure to natural daylight. Other risk factors for myopia include the time we spend on near work, urbanization, socioeconomic position, diet, pregnancy-related factors and genetics. Children with myopia tend to engage in less physical activity, but physical activity alone is not protective against myopia.
Myopia is more prevalent in countries adopting a Western diet and lifestyle, and many of the genes that increase the risk of myopia are involved in insulin/glucose signalling and obesity/fat metabolism. Insulin signalling also appears to influence the normal growth of the eye. As the Western diet is linked to greater intake of food with higher energy loads, one hypothesis is that compensatory increases in blood glucose and insulin levels send increased growth signals to the eyes.
This project aims to determine how genetic and environmental factors interact with insulin signalling to affect myopia. Changes in insulin signalling happen naturally in children around puberty, and so we would like to use information in the Avon Longitudinal Study of Parents and Children on eye growth, glasses prescriptions, blood levels of glucose and insulin before, during and after puberty on thousands of children followed prospectively from birth, to find out how they interact to affect eye growth. Additionally, there are normal variants in our genes that influence fasting levels of insulin and glucose and we will find out how these genetic variants are linked to myopia. These analyses should provide novel insights into the relationship between insulin signalling and myopia, and have the potential to identify novel targets for treatment.

Epidemiological research has shown that adolescents worldwide are chronically sleep deprived due to increased use of technology at night, consumption of caffeinated beverages, as well as more academic and social demands (1-3). Sleep fragmentation and sleep loss have been associated with emotional dysregulation (4), increased psychosis (5), and higher risk-taking behaviours, including substance abuse (6). Adolescents are particularly exposed to the risk of developing substance use disorders (SUDs) and related psychiatric comorbidities (7,8), hence it is particularly important to measure to what extent and how adolescent chronic sleep restriction contributes to the development of such mental disorders. So far, longitudinal analysis using ALSPAC data have found that less total sleep time at age 15 years predicts symptoms and diagnosis of anxiety and depression later in life (9). However, the prospective association between adolescent sleep and development of SUDs has not been explored.
One of the mechanisms through which sleep loss can affect brain function is by inducing epigenetic changes, dynamic modifications that can powerfully regulate gene expression, without changing the heritable genetic sequences. Epigenetic changes in the form of DNA methylation have been associated with both altered sleeping patterns (10,11) and SUDs in adults (12-15). Our goal is to determine whether adolescent sleep patterns predict the risk of developing SUDs later in life and to what extent epigenetic changes are associated with both adolescent chronic sleep restriction and drug consumption. Since, despite recent declines, alcohol remains the substance most widely used by today’s teenagers, this research proposal will focus on alcohol drinking and alcohol use disorders. The results of this analysis will guide future causal experiments to identify the biological mechanisms mediating the consequences of sleep loss and to develop new strategies to reduce alcohol abuse and improve mental health.

1. Crowley SJ, Wolfson AR, Tarokh L, Carskadon MA. An update on adolescent sleep: New evidence informing the perfect storm model. J Adolesc. 2018;67:55-65. doi:10.1016/j.adolescence.2018.06.001
2. Short MA, Weber N, Reynolds C, Coussens S, Carskadon MA. Estimating adolescent sleep need using dose-response modeling. Sleep. 2018;41(4). doi:10.1093/sleep/zsy011
3. Owens J, Adolescent Sleep Working Group, Committee on Adolescence. Insufficient sleep in adolescents and young adults: an update on causes and consequences. Pediatrics. 2014;134(3):e921-932. doi:10.1542/peds.2014-1696
4. Ben Simon E, Vallat R, Barnes CM, Walker MP. Sleep Loss and the Socio-Emotional Brain. Trends Cogn Sci. 2020;24(6):435-450. doi:10.1016/j.tics.2020.02.003
5. Ritter PS, Höfler M, Wittchen H-U, et al. Disturbed sleep as risk factor for the subsequent onset of bipolar disorder--Data from a 10-year prospective-longitudinal study among adolescents and young adults. J Psychiatr Res. 2015;68:76-82. doi:10.1016/j.jpsychires.2015.06.005
6. Short MA, Weber N. Sleep duration and risk-taking in adolescents: A systematic review and meta-analysis. Sleep Med Rev. Published online March 27, 2018. doi:10.1016/j.smrv.2018.03.006
7. Spear LP. Alcohol Consumption in Adolescence: a Translational Perspective. Curr Addict Rep. 2016;3(1):50-61. doi:10.1007/s40429-016-0088-9
8. Saalfield J, Spear L. The ontogeny of ethanol aversion. Physiol Behav. 2016;156:164-170. doi:10.1016/j.physbeh.2016.01.011
9. Orchard F, Gregory AM, Gradisar M, Reynolds S. Self-reported sleep patterns and quality amongst adolescents: cross-sectional and prospective associations with anxiety and depression. J Child Psychol Psychiatry. Published online June 17, 2020. doi:10.1111/jcpp.13288
10. Massart R, Freyburger M, Suderman M, et al. The genome-wide landscape of DNA methylation and hydroxymethylation in response to sleep deprivation impacts on synaptic plasticity genes. Transl Psychiatry. 2014;4(1):e347-e347. doi:10.1038/tp.2013.120
11. Lahtinen A, Puttonen S, Vanttola P, et al. A distinctive DNA methylation pattern in insufficient sleep. Sci Rep. 2019;9(1):1193. doi:10.1038/s41598-018-38009-0
12. Liu C, Marioni RE, Hedman ÅK, et al. A DNA methylation biomarker of alcohol consumption. Mol Psychiatry. 2018;23(2):422-433. doi:10.1038/mp.2016.192
13. Lohoff FW, Roy A, Jung J, et al. Epigenome-wide association study and multi-tissue replication of individuals with alcohol use disorder: evidence for abnormal glucocorticoid signaling pathway gene regulation. Mol Psychiatry. Published online May 12, 2020. doi:10.1038/s41380-020-0734-4
14. Montalvo-Ortiz JL, Cheng Z, Kranzler HR, Zhang H, Gelernter J. Genomewide Study of Epigenetic Biomarkers of Opioid Dependence in European- American Women. Sci Rep. 2019;9(1):4660. doi:10.1038/s41598-019-41110-7
15. Camilo C, Maschietto M, Vieira HC, et al. Genome-wide DNA methylation profile in the peripheral blood of cocaine and crack dependents. Rev Bras Psiquiatr Sao Paulo Braz 1999. 2019;41(6):485-493. doi:10.1590/1516-4446-2018-0092

Previous research has shown that autism spectrum disorder is caused by a combination of both genetic and environmental factors. Although studies have identified genes that associated with having ASD, the specific pathways involved in the development and progression of ASD is not well understood. In addition, less is known about the genetic contributions to autistic traits in the more general population and also on the progression of these autistic traits. In this project we would like to address this important gap in our knowledge by investigating these genetic contributions in more detail and by attempting to identify the specific biological pathways that lead to the development of ASD, autistic traits and the progression of these traits over time. To do this we will use data from several cohorts, including Children of the Nineties. The results from this study may inform population health and may help develop more targeted interventions for individuals with ASD symptoms.

DNA methylation (DNAm) plays a central role in gene regulation. It helps to define how cells respond to environmental signals and, ultimately, contributes to health or susceptibility to disease. However, the amount and the effects of differences in DNAm from one person to another is poorly understood. Understanding DNAm variability is a complex area of research as DNAm varies from one type of cell to another and can change over time. In addition, DNAm is influenced by genetic, molecular and environmental and other factors. So far, most epidemiological studies of DNAm have been performed in blood comprising diverse cell types. Furthermore, it is unknown whether DNAm changes lead to other molecular changes (for example gene expression) or whether the reverse is true, with molecular changes leading to changes in DNAm. Together, this makes the interpretation of DNAm variability difficult.

A powerful avenue into researching the functional consequences of changes in DNAm levels is to correlate DNA sequence variants such as single nucleotide polymorphism (SNPs) to DNAm levels to find both local and distal (for example on other chromosomes) effects. Having completed the largest genetic study of DNAm worldwide to date (through the Genetics of DNA Methylation Consortium) by scanning 10 million SNPs genomewide, we have identified 270k SNP-DNAm associations. This was achieved by analysing about 400,000 DNAm sites in blood, which is only 2% of 28 million DNAm sites across the genome. There is a huge potential for improved understanding of DNAm variation between individuals and its influence on health and disease by studying other regulatory regions of the genome, disease-relevant cell type or context and by developing novel epidemiological approaches where effects of multiple cell types and regulatory features are combined in a population-based setting.

We propose to integrate a suite of state-of-the-art technologies to characterise the functional role of DNAm. We will use novel sequencing technologies based on long reads with the ability to measure all 28 million sites and to determine both the DNAm level and the genotype at single molecule level. We will exploit these properties to provide insights on highly complex genomic regions, differential DNAm between alleles and detection of different modifications. We will systematically map genetic influences on DNAm across a wide range of tissues, cell types and ancestries. We will use this resource to understand the regulatory role of non-coding variants associated with disease traits by studying shared genetic variation and by using genotype as a causal anchor. We will further develop epidemiological approaches to explore the functional role of DNAm variation between individuals in large population-based epidemiology studies. We will validate causal relationships between DNAm sites and traits with epigenetic editing experiments where we manipulate DNAm sites to study the effects of gene regulation to disease. We will establish an openly accessible data resource that will enhance our understanding of environmental and genetic influences on genome function in humans.

Dietary intakes have important genetic determinants, however, the genetic variants affect dietary intake is unclear. A large GWAS with body mass index (BMI) identified 97 variants and found that nearly half of them has expression in the brain, the key site of central appetite regulation. We hypothesize that these brain variants contain important genetic information associated with dietary intakes, and will explore this hypothesis using data from ALSPAC and Harvard cohorts. We will further assess whether the associations of these variants with dietary intakes change with age and explore the mechanism using ALSPAC DNA methylation data.

There are multiple evidences in the literature that there is a clustering of energy balance related behaviors in children, however, the link with obesity related outcomes is not always consistent across the studies. Our goal is mobilize several European cohorts, all part of the lifecycle consortium, to achieve greater statistical power, and compare results across European countries.