The need for comprehensive and representative data collection on populations for epidemiological research has been brought into sharp focus by the COVID-19 pandemic. This has resulted in the generation of many COVID-specific modules within existing cohorts and datasets (e.g. Henderson et al, 2020, Kwong et al, 2020). These datasets are going to be invaluable in understanding the mental health response of individuals to the COVID-19 pandemic. These studies reflect individuals responding under unique circumstances, presenting unique selection effects, which have the capacity to substantially bias results (Griffith et al. 2020). These selection effects are likely to be particularly stark with respect to mental health, which is known to be associated with non-response (Kwong, 2019). The data and analysis will be carried out by GG and DS, and data stored on the University of Bristol RDSF.

The obesity epidemic is one of the most important health challenges of the 21st century.
Identifying genetic factors predisposing to weight gain is crucial for identifying biological processes important for weight-control and help identify individuals already at young age that might benefit from health interventions and thereby reducing their risk for disorders such as type 2 diabetes that follow in its footpath.

While there is great progress deciphering the genetic factors influencing weight in in adulthood, and at birth, there is a huge knowledge gap on the role of the genomes of the child and its parents in infancy and childhood into puberty. This is very unfortunate, as it is firmly established that the BMI-development during the first 6 years of life are strong predictors of obesity in adolescence . Results from our own ongoing work in the Norwegian Mother, Father and Child and work of others show that it is possible to find novel genetic variants with specific and substantial effect on weight development during infancy with high quality data and large GWAS sample sizes.

Autism is a lifelong condition characterised by difficulties with social interaction, social communication and repetitive behaviours. In recent decades, the number of children and adults diagnosed with autism has increased. As autism is heritable, genetic risk may explain why there are some individuals with mild autistic traits but they may not meet the criteria for an autism diagnosis and subsequent treatment or other support requirements. As more children with autism and mild autistic traits reach adulthood, the need for support from health and other services will likely increase. However, few large, population based longitudinal studies involving adults with autism/autistic traits exist, representing a research gap.

During this phase of my PhD project, I will study how autism and autism traits, including genetic risk for autism, may be associated with BMI and disordered eating behaviours in adulthood. Children with autism often having sensory issues and unusual eating preferences, which could have an effect on growth and health in adult life. It has been reported that social communication difficulties may increase the risk of disordered eating patterns in adolescence. This implies that maintaining a healthy BMI may be challenging during this period and that there is a possible increased risk of disordered eating behaviours and eating disorders in adulthood for those with social communication difficulties. This phase of my PhD project is focussed on whether autistic individuals are more likely to have high BMI and disordered eating behaviours in adulthood. Changes in growth during late childhood into late adolescence will also be studied to assess whether there are critical periods that are suitable for intervention.

Neonatal jaundice is a yellowish discoloration of the eyes and skin in a newborn baby as a consequence of high bilirubin levels. While jaundice in most newborn is normal, a subset of patients with elevated bilirubin levels may develop excess sleepiness or poor feeding, whereas patients with excessive bilirubin levels are at risk for severe brain damage. In this project we aim to identify genetic risk factors for the development of high bilirubin levels in the newborn. This information can aid in risk prediction and the onset of early treatment for hyperbilirubinemia in the newborn.

Eyesight Problems and Psychotic Illnesses: What’s the Link?
Psychotic illnesses affect just under 1% of people in England. Symptoms include hearing voices and experiencing confusing and distressing thoughts. These often begin in early adulthood, and can have a major effect on people’s lives.
People with psychotic illnesses seem to have more eyesight problems. We are not sure why, but it might be because:
• Possibility 1: Some people with psychotic illnesses find it harder to look after their health including their eyes, for example by going to the optician’s.
• Possibility 2: The same brain changes cause eyesight problems and psychotic illnesses.
• Possibility 3: Eyesight problems increase a person’s chances of having a psychotic illnesses.
I plan to look at which of these best explains the link between eyesight problems and psychosis. If people with psychosis have less eye care (possibility 1), we need to improve this. If possibility 2 is correct, eye research might hold the key to understanding more about the brain changes that cause psychosis. Or, if eyesight problems lead to psychosis, then improving eye health could be a way of preventing or reducing psychosis.
I will start by reviewing past research, to make sure I base my work on the most up-to-date information. I will then carry out research using two large datasets: UK Biobank and the Avon Longitudinal Study of Parents and Children (ALSPAC).
UK Biobank has information on over half a million 40 to 69-year-olds including questionnaires, eyesight tests and genetic tests.
ALSPAC has information on 14,500 UK families. The children have been followed up since before birth and will be 25.
I will look at UK Biobank and ALSPAC because older and younger people are most at risk of developing psychosis. In these datasets, I will see if people with short-sight genes have more psychotic illnesses. If so, this would be evidence that poor eyesight can lead to psychosis. Genes are present before birth, so I will know that they came before any psychotic illness began. I will also find out if genes for psychotic illnesses are linked with eyesight problems. This would suggest the reverse: that psychosis leads to poor eyesight.
I will also use a third, Israeli dataset. All Israeli 17-year-olds have health checks to decide if they can join the armed forces. I will use this data to find out if teenagers with eyesight problems are more likely to have a psychotic illness over the following years. If so, I will see what level of eyesight problems are associated with developing psychosis. This will allow me to test the theory that perfect eyesight and complete blindness both protect against psychosis, with moderate eyesight problems carrying the highest risk.
Throughout this research, I will chair a group every 6 months. It will include people with psychosis, people with eyesight problems, carers, charity members and doctors. We will discuss study findings and think about how to use them to improve the experiences of people with eyesight problems and psychosis. This will include plans to publicise findings, influence healthcare and plan new studies.
When the research is finished, I will tell healthcare professionals and researchers about the results at meetings and in journals. I will also write about them in publications read by people with mental health and eyesight problems. I will offer to present findings to public groups, through links with the Royal National Institute for the Blind (RNIB) and a forum of mental health service users.
People with eyesight problems and mental health service users helped to write this summary.

Pain which lasts for more than 3 months is termed chronic or persistent pain. It often occurs in the absence of obvious injury. It can be very difficult to treat. The UK is home to several large databases that contain information about subjects’ life history of pain and their genetic “make-up”. We plan to use the largest of these (UK Biobank) to help identify the genes that are related to the presence of a chronic pain condition (e.g. pain of duration >3 months) and the related symptoms experienced by sufferers e.g. low mood, poor sleep, lack of motivation, (pain) anxiety or pain depression. By gaining confidence that these genetic markers are related to the presence of a chronic pain condition, we will then use this information to recruit two small (each <200) groups of ALSPAC subjects who may or may not already have a pain condition. The beauty of this approach, is that by identifying the genes that make people susceptible to developing a pain condition, we will see how this interacts with health factors e.g. weight, blood pressure, and events that shape a person’s psychology e.g. adverse life events such as bereavement, to try to provide a means for people to modify their risk, or identify people for whom early intervention might be most beneficial following an injury – to hopefully avoid them going on to suffer life-long pain.

To achieve this goal, we would ask those subjects to undergo a series of pain tests (which of themselves cause only temporary discomfort) and undergo an MRI scan of their brain and spinal cord.

To better characterise pain present in the ALSPAC cohort, we propose to add pain-specific questionnaires (and tasks) to the upcoming age 30 clinics and questionnaires, which would target all participants. These questionnaires/tasks would explore the incidence of pain in the cohort, which will provide a more complete picture than is currently available. By adding information to this time point, we will gain insight to the cohort and to which genetic and biological factors can influence the development of a chronic pain condition. The availability of this window, where the majority of the cohort are still (hopefully) pain-free, will provide a baseline from which future studies will be able to reflect on the factors that ultimately led some participants to develop a chronic pain condition.

The remit of the Advanced Pain Discovery Platform funding call is to better understand the mechanisms associated with pain. Our Expression of Interest is focused on psychosocial mechanisms of pain, and as part of this we wish to see how these impact on individuals across the lifespan. We wish to explore potential psychosocial correlates of pain, and build on work already conducted by members of our consortium on pain. Details of this project will be updated in due course.

This project will provide the most comprehensive interrogation of the genetics of aging-related cognitive changes during prodromal, so-called “silent,” periods of ADRD progression. Identifying the genetic risk factors and mediating biological mechanisms underlying progression to Alzheimer’s disease and related dementias of aging is crucial to developing interventions to prevent, delay, or otherwise mitigate ADRD disease progression.

Depression is the leading cause of global disability with over 300 million people suffering world-wide. Estimates suggest that up to two thirds of patients do not recover following their first antidepressant treatment and up to one third do not recover after multiple treatments. Therefore, it is critically important to identify factors that predict recovery and reduce risk of relapse. Current methods in genetic epidemiology focus on predictors of mental illness onset. While this is crucial to prevent new diagnoses, it does little to help individuals already suffering. Therefore, the Recover project aims to extend current genetic epidemiology methods to better understand recovery from depression. The methods developed here will begin with a focus on depression but can also be extended to other mental illnesses. First, we will develop trajectories of depression using continuous longitudinal measures in two critical time points, 1) adolescence and early adulthood and 2) during and post pregnancy. Second, using these trajectories as outcomes we will explore many modifiable predictors of recovery. Third, we will use cutting-edge causal inference techniques to test whether or not these predictors are causal. And finally, we will develop novel technologies to capture fine-grained fluctuations in mood. Taken together, this work will lead to better interventions and inform adjuncts to treatment having real impact for the growing number of individuals suffering from depression.