Genetic studies often need to compare diseased participants with healthy participants. In this project, a study of Head and Neck Cancer patients (HN5000) has no healthy participants to compare to. Therefore, participants in ALSPAC are going to be utilised as healthy genetic controls. To do this successfully some ALSPAC participants, who've already had their genetic data measured, will have it remeasured alongside the HN5000 participants. By doing this any errors that may occur in the laboratory process can be identified rather than potentially contributing to false findings. Once this cross-check is complete all ALSPAC participants (unless already diagnosed with HNC) can be used as healthy controls which will greatly increase the utility of the HN5000 study.

In order for the public to have faith in the conclusions of scientists it is important that the methods they employ are robust and transparent. This is especially important for controversial topics with major implications for mental health. The public should rightly demand that such findings are not contingent on the beliefs of the scientists, their particular methods, computational quirks or simple accident. This is of particular relevance when total transparency is not possible because the data is sensitive.

This study addresses the question of robustness by taking a controversial question, “Is there an association between screen time and depression and anxiety?”, recruiting teams of independent data analysts and looking at how they answer the question using the same data, effectively ‘crowd-sourcing’ the data analysis.

The study will use the answers teams provide to this controversial question to answer further questions such as: “Do the methods used to answer the question influence the result and if so by how much?” and, “Do the beliefs and particular expertise of the analysts influence their results?”. To do this, we will use a statistical technique called a ‘multiverse analysis’, whereby reasonable alternatives to choices made by the teams, during the data analysis, are explored and recorded to see how sensitive the results were to the choices made.

To ensure the study is transparent we will investigate the use of anonymised data. Using anonymised data is controversial as the anonymisation process may erase important features of the data. This study will ask “Does the anonymisation process affect the results and if so by how much?”.

The answer to all these questions will help us understand how scientists arrive at answers to controversial questions and whether crowd sourced analysis and data anonymisation techniques can ensure findings are robust and transparent.

Finally, our study will challenge the teams to come up with interesting ways to visualise the answers to these questions in exchange for a prize. Visualising the teams’ answers, along with how robust they are, in a clear, accessible way will be important to help communicate complex results both for this study and in the future.

Fetal alcohol spectrum disorders (FASDs) are lifelong disabilities caused by prenatal alcohol exposure. FASD is thought to be the leading preventable cause of developmental disability in the world and is associated with mental health problems, antisocial behaviour and substance misuse. Understanding which individuals are most at risk to develop FASD is key as it has been shown that an early diagnosis or risk identification could help with primary and secondary prevention, as well as amelioration and treatment efforts. Early identification of individuals most at risk for FASD via the identification of robust maternal and infant biomarkers will offer an early window for intervention, will focus specialist diagnostic efforts in a resource-limited healthcare system, and will contribute towards mitigating FASD-related disabilities in later life. It may also prevent or reduce foetal exposure to alcohol in subsequent pregnancies.

This project will focus on early identification of FASD by DNA methylation levels, which are known to be altered by alcohol use. Indeed, a DNA methylation alcohol (DNAm-Alc) score, comprising 144 CpG sites, was recently developed by Liu et al. that can robustly distinguish between current heavy drinkers and non-drinkers in a general population. In addition, strong evidence from animal models indicates that alcohol use during pregnancy is capable of altering offspring DNAm.

McQuire et al. have developed an algorithm to identify individuals at high risk of FASD regardless of them having received a formal (and rare!) diagnosis. We propose to compare this algorithmic identification with DNAm-based markers to find out whether prenatal risk assessment would be possible.

References
Liu, C. et al. A DNA methylation biomarker of alcohol consumption. Mol. Psychiatry 23(2):422-433 (2018)

McQuire C, Mukherjee R, Hurt L, Higgins A, Greene G, Farewell D et al. Screening prevalence of fetal alcohol spectrum disorders in a region of the United Kingdom: A population-based birth-cohort study. Prev Med (Baltim) 2019; 118: 344–351.

Infections during pregnancy1 and early childhood2 have been linked to increased risk of schizophrenia in observational studies. Inflammation, as measured by interleukin-6 (IL-6) and C-reactive protein (CRP), contribute to host defence against infections3,4. Observational epidemiologic studies suggest that inflammatory biomarkers have a positive association with schizophrenia. Interestingly, we have recently shown that individuals who are genetically predisposed to lower activity of the inflammatory response (i.e. CRP levels and blockade of IL-6 cell signalling) have higher risk of schizophrenia5. This, seemingly contradictive, finding may be explained by a genetically determined decreased inflammatory response resulting in an increased susceptibility to early life infection4. In this project, the link between inflammation, psychosis and early life infection will be explored using publicly available genome-wide association study (GWAS) data and newly measured antibody titres available in the ALSPAC cohort6.

1. Khandaker GM, Zimbron J, Lewis G, Jones PB. Prenatal maternal infection, neurodevelopment and adult schizophrenia: a systematic review of population-based studies. Psychol Med. 2013;43(02):239-257.
2. Khandaker GM, Zimbron J, Dalman C, Lewis G, Jones PB. Childhood infection and adult schizophrenia: A meta-analysis of population-based studies. Schizophr Res. 2012;139(1-3):161-168.
3. Calabrese LH, Rose-John S. IL-6 biology: implications for clinical targeting in rheumatic disease. Nat Rev Rheumatol. 2014;10(12):720-727.
4. Pepys MB, Hirschfield GM. C-reactive protein: a critical update. J Clin Invest. 2003;111(12):1805-1812.
5. Hartwig FP, Borges MC, Lessa Horta B, Bowden J, Smith GD. Association between genetically elevated levels of inflammatory biomarkers and risk of schizophrenia: a two-sample Mendelian randomisation study. 2017.
6. Mitchell RE, Jones HJ, Yolken RH, et al. Longitudinal serological measures of common infection in the Avon Longitudinal Study of Parents and Children cohort. Wellcome Open Res. 2018;3:49.

Allergic diseases including asthma, hay fever and eczema often occur in the same individuals. Allergic diseases have been reported to be linked with depression and anxiety disorders, which is also a growing health concern. However, it is not yet clear if the allergic diseases cause depression or mental health disorders, or if these have a causal effect on allergic disease. In this project we hope to use a statistical technique called Mendelian Randomization to investigate the relationship between these traits.

This study will use a latent variable (and negative control) approach to investigate association between adolescent physical activity (PA) profiles and hip bone mineral density (BMD) at age 25. The main analysis will examine relationship between PA patterns over adolescence and age 25 hip BMD by applying (2nd order) latent growth models to repeated measures of PA intensity (light, moderate, vigorous) derived from Actigraph accelerometers at ages 11, 13 and 15. An additional analysis will be performed to explore association between gravitational impacts from PA, assessed at age 17 from Newtest acceleromters, and hip BMD at age 25.

Why do some people suffer with long term pain after an accident or surgery whilst another person recovers without incident? We believe that part of the answer lies in the subtle differences in people's genes. We are interested in how these little differences can add up to make an individual more or less likely to suffer from pain. Unfortunately we don't know at the moment which gene differences are important. We plan to invite people, from the ALSPAC cohort, with known common variants in a specific gene that has been linked to pain detection (TRPA1), to come for testing of their sensitivity to mildly painful stimulation. If we find people who have differences in their pain sensitivity, we will invite them back for more detailed tests. In these detailed tests, we will record the activity in their nerves during mildly painful stimulation to see if a change in their nerve activity underlies their differences in pain sensitivity.

The mildly painful stimulation that we use is well tolerated by both patients and volunteers. Most of our tests look at pain "threshold" e.g. the transition from warm to hot. We therefore use the lowest temperature that causes pain to be perceived. We will also ask participants to rate how much pain they feel in response to a given stimulus. We believe that these tests are less painful then giving blood or a skin biopsy. The participant can stop the experiment at any time if it is too uncomfortable.

A healthy placenta is important for a healthy pregnancy and birth of a healthy baby. Some research shows that a larger placenta is associated with a healthier baby at birth. The placenta is normally weighed after the birth of the baby. This is too late to change things during the pregnancy that might help improve the mothers and baby’s health. The size of the placenta can be measured using ultrasound scans during pregnancy. If we can show that the size of the placenta is an accurate way of identifying women who might be at risk of having health problems during pregnancy and with their baby at birth, and that ultrasound measures of the placenta are accurate measures of its size, it might be possible to prevent problems for mother and her baby.

We would like to access the Children of the 90s and their parents’ genetic data and some imaging and blood biomarker information in order to characterise “favourable adiposity” genes. By this we mean versions of genes (alleles) that result in a higher BMI and body fat but lower risk of diseases such as type 2 diabetes, hypertension and heart disease. Current studies have already identified 14 alleles associated with higher body fat % but lower risk of these diseases. We know that some of these “favourable adiposity” genes operate by putting more fat in the lower body, resulting in a lower waist to hip ratio (“pear” rather than “apple” shape) in women. Using MRI imaging from adults, we know that these alleles add more fat under the skin (subcutaneous) but less fat to the liver. These genes have been discovered in adults, and we would now like to test what they are doing in childhood and adolescents in the CO90s. To do this, we’d like to link the gene variants we are finding in adults to body fat imaging and circulating blood markers at all time points in childhood and adolescence where available. We’d like to analyse the parent’s data to help in the discovery of these genetic variants by analysing with other large studies of adults.