A single mutation within the genome (called rs71564871 near a gene called BEND6) has been previously linked to fat distribution in the body, specifically the proportion of fat stored across the waist compared to the hips, in women of the ORCADES study and UK Biobank. Within this study, we want to assess whether this single mutation is similarly related to the same pattern of fat deposition in the Avon Longitudinal Study of Parents and Children.

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).

Intimate Partner Violence (IPV) is defined as any violent behavior within an intimate relationship or any other controlling behavior that is conducted by a current or former partner. It is the most common form of violence in women which constitutes a major public health problem worldwide. The current explanatory theories of IPV perpetration can be summarized as feminist/sociocultural, social learning theory-based intergenerational transmission and psychological/psychosocial. According to the feminist/sociocultural theory, domestic violence is a consequence of “patriarchy”. From this view, violence is used as a form of power and control of women by men. The intergenerational transmission theory asserts that domestic violence is based on the exposure to, or observation of, violence in the family of origin. Psychological theories propose that there are psychological, psychiatric, behavioural and neurological risk factors for domestic violence perpetration. In the study of IPV perpetration, it is important to consider the variables addressed by such theories as a whole and from a developmental perspective and there is no study that simultaneously considers all the variables of these explanatory theories. The general aim of our study is to identify those etiological mechanisms linking risk factors for IPV perpetration across development. This study will be the first one that sheds light on which the origins of IPV perpetration are by knowing how IPV perpetration develops. Implications in terms of prevention and treatment will be of a great relevance for public health.

Childhood adversity could cover many things including extreme difficulties and adverse experiences during childhood such as sexual, physical and emotional abuse, deprivation, and family dysfunction. Experiencing adversity during childhood may have a dramatic effect on a child's life. It has been linked to a number of poor outcomes in adulthood such as worse health outcomes, poor mental health, reduced life satisfaction and dementia. One in three adults diagnosed with mental health conditions are reported to have experienced childhood adversities therefore, there is the potential for life-long associations between childhood adversity and health, which need to be evaluated and accounted for. The proposed project will examine childhood adversity in three different UK populations and in a birth cohort and associations with a number of different outcomes including physica and mental health, poor lifestyle choice such as unhealthy diet, smoking and binge drinking and antisocial behaviours.

Smoking is highly co-morbid with several psychiatric conditions, but understanding the causal nature of this relationship is complicated by well-described issues of confounding and reverse causality. Mendelian randomisation uses genetic variants associated with an exposure (e.g., smoking) to examine causal pathways between the exposure and outcomes. Previous genetic instruments for smoking have only captured discrete aspects (e.g., initiation, heaviness of smoking), limiting power and requiring individual level data on smoking status for analyses of heaviness of smoking. To overcome these issues, we are developing a novel genetic instrument for comprehensive smoking exposure, which takes into account duration of smoking, heaviness of smoking, time since cessation, and a simulated half-life constant to capture the exponentially decreasing effect of smoking on health over time. Our instrument includes both smokers and non-smokers, removing the need to stratify on smoking status.
We have begun work on this instrument by conducting a genome-wide association study (GWAS) of our comprehensive smoking measure in the UK Biobank (N=463,003) and identified 124 independent SNPs associated at the genome-wide level of significance. Our two-sample Mendelian randomisation validation analysis confirmed that smoking causes lung cancer and coronary heart disease. To further establish the validity of the instrument we need to check that it predicts smoking in an independent sample. Here we hope to use ALSPAC, checking whether a polygenic risk score for lifetime smoking exposure predicts actual smoking behaviour. Secondly, we need to check that the instrument is not spuriously associated with any traits other than smoking. We can do this by checking for associations with other outcomes in ALSPAC.
If the instrument predicts smoking in ALSPAC and is not associated with other unexpected traits, we hope to go onto use our novel genetic instrument to explore bi-directional effects between smoking and mental health, focusing on schizophrenia and major depressive disorder.

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 large increase in the prevalence of respiratory diseases over the last decades, in the West more particularly, cannot be explained by genetics only. It has been hypothesized that these increases are a consequence of changing environmental and/or lifestyle factors. Given the multifactorial aspect of these diseases, it is thus important to take into account the interrelations between these factors and respiratory health. The interrelations between body mass/body composition, physical activity, diet and lung function in childhood and adulthood have been incompletely addressed, likely because their time-dependent and bidirectional nature represent a methodologically challenging research question. Marginal structural models (MSMs) allow estimation of causal effects in observational studies by addressing time-dependent confounding (Robins JM et al. Epidemiology 2000). This approach has still limited application in respiratory epidemiology. We aim to investigate the joint and independent causal effects of body mass/body composition, physical activity and diet on lung function during childhood and early adulthood using MSMs in children from the ALSPAC study.

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.

Treatment for mental illness often focuses on changing cognitive patterns (for example, cognitive behavioural therapy). There is much evidence to suggest that cognition is different in those with mental illness but whether change in cognition is a causal risk factor has not yet been established. Classic observational studies of cognitive patterns and mental illness do not get around the problems of reverse causation and residual confounding. That is to say that the change in cognition might instead emerge as a result of the mental illness, or both might be the result of other unmeasured factors.

One way to get around this is using Mendelian randomisation. Here we take genetic variants associated with the trait of interest: cognition and use them to naturally randomise individuals to levels of the exposure. Therefore, analogous to a randomised control trial, we can make conclusions about whether or not the relationship is causal. In this study, we will be looking at the cognitive traits of emotion recognition, working memory, cognitive styles and impulsivity. This research could inform the development of cognitive training tasks as interventions for mental illness.