One in six school-aged children now have a probable mental health disorder -- up from one in nine three years ago (NHS digital, 2020). Services for children and young people’s mental health are stretched more than ever before, such that maximising resources is a key priority (Lennon, 2021). The effectiveness of mental-health interventions can be optimised by enhancing our understanding of the mechanisms of change – that is, how an intervention translates into events that lead to improved outcomes (Kazdin, 2007). Parent and child behaviours are primary intervention targets for children’s mental health (WHO, 2009), yet little is known about their assumed mechanistic role, that is, how parent-child dynamics change as a function of, or during intervention. We aim to address this gap, with specific focus on children’s disruptive behaviour as one of the most common reasons for children to receive mental-health services (Achenbach & Edelbrock, 1987; Hinshaw & Lee, 2003).

Evidence from epidemiological and twin studies has shown that some pairs of disorders, such as between brain disorders, often exhibited shared symptoms and comorbidity. However, there is a paucity of observational data on the correlation of other traits, especially for rheumatic diseases and musculoskeletal conditions. Additionally, it remains unknown whether the nature of co-occurrence of these diseases can contribute to potential etiological overlap or due to ascertainment bias and diagnostic misclassification. Recent advances in genetic methods, including the Mendelian randomization analyses, cross-trait meta-analysis, and transcriptome-wide association study, have enabled us to evaluate the degree of genetic overlap and to find the pleiotropic loci. Accordingly, by using data from the Avon longitudinal study of parents and children, we seek to systematically assess the correlation of different diseases, and how connected they on the genomic and environmental level.

Present in nearly all types of human cells, mitochondria generate the majority of our cellular energy and are thus often referred to as the ‘powerhouse of the cell’. In addition, mitochondria play important roles in signalling between cells and cell death (known as apoptosis). Although most of our DNA is within the nucleus (the ‘nuclear genome’ or nDNA), mitochondria contain their own DNA (the ‘mitochondrial genome’ or mtDNA), and human health is dependent upon the coordination of the products of these two genomes.

Genetic variants within mtDNA can cause disease (1) and are also linked to a growing number of age-related complex diseases (2), particularly neurodegenerative diseases including Parkinson’s disease, Alzheimer’s disease, schizophrenia and multiple sclerosis (3). In addition, there are examples where the progression or severity of mtDNA disease is modulated by nDNA variants (e.g. LHON (4, 5)). Conversely, there is evidence in complex diseases where nuclear susceptibility factors are implicated, that common, inherited, mtDNA variation can influence traits such the age of onset (e.g. Alzheimer’s disease (6) and cardiomyopathy (7)).

More recently, the development of mitochondrial replacement therapy, a technique designed to avoid transmission of defective mitochondria from parents to offspring, sparked discussions on mitochondrial nuclear incompatibilities that would require matching donor and recipient mitochondrial and nuclear backgrounds, and more generally on whether the genetic makeup of healthy individuals reflects such incompatibilities (8).

This raises the question whether specific variant combinations of nuclear and mitochondrial alleles are depleted or enriched in the general population. This can be assessed by comparing their frequencies with the frequencies expected from the frequencies of the corresponding nuclear and the mitochondrial alleles assuming independent segregation. Such associations between variants can reflect mixing of different populations or selection against or in favour of particular variant combinations. Associations between nuclear and mitochondrial variants have been repeatedly reported (9, 10). However, it is unclear to what extent the observations reflect population stratification. This is of particular interest because mitochondrial genetic variants have been extensively used to track migration of populations and ancestry.

The aim of this study is to use the ALSPAC cohort genotyping data to: 1) identify combinations of mitochondrial and nuclear variants that are over or underrepresented in a well-characterised population, 2) ascertain whether under or over representation can be explained by heterogeneity within the population and 3) assess the effects of factors such as age or sex. The results will also provide a reference for studying the role of such combinations in human disease.

From the start ALSPAC has included data collections on diet. The first collection was in the mothers during their pregnancy, and it's been repeated three other times with them. We've also got a wealth of information on the diets of the children from birth into teenage years. This has been an incredibly rich source of data for us on changing patterns on diet during childhood and is still on constant use today, mainly to study the effects of diet in pregnancy and in childhood on markers of health in later years. This type of long-term data on diet is very unusual in the UK so we are fortunate to have this in ALSPAC.

It's been some time since we've able to collect new information on diet in ALSPAC in the children, but we have an opportunity to do this in some upcoming questionnaires. This will add to our existing long-term data and add greatly to the potential for future scientific work linking diet and health.

As previously we are using a method called a 'food frequency questionnaire' (FFQ) in which participants are asked how often they usually eat particular foods. The FFQ is very similar to that used before in ALSPAC, but we have updated it for changes in the types of food people eat nowadays. In order to sure that the FFQ is correctly reflecting what participants actually eat, we need to do an additional 'validation' study in which we ask some of the participants to record their diet in another way.

For this validation study, we'll be asking some of the participants who fill in the FFQ to access an electronic platform on their smartphones or other device to record one day's food and drink intake in detail (food diary). We'll then ask them to repeat this twice at monthly intervals (so on three separate days in all). We can then compare the two ways and see how well the FFQ is performing against the electronic food diary and whether we get similar data from the two methods. If they are similar, we have can confidence in the data collected from the FFQ and can use it state this in our scientific publications so that others will know we have accurate data.

ALSPAC contributes to a group of studies called MR-PREG which aims to investigate the impact of maternal exposures during pregnancy (such as BMI, smoking, blood pressure) on pregnancy complications, perinatal outcomes and later offspring outcomes. MR-PREG uses genetic data to explore whether associations between maternal exposures and these outcomes are likely to be causal.

Pre-diabetes is a condition where blood glucose levels are elevated above normal, but below the threshold to be defined as type 2 diabetes. It is estimated that 70% of those meeting the criteria for pre-diabetes will eventually progress to type 2 diabetes, which is a chronic, incurable condition associated with various other comorbidities. It is therefore important to understand why some individuals progress from pre-diabetes to type 2 diabetes, so that we can develop interventions to stop this happening. We therefore aim to identify the genetic basis for why some individuals progress from pre-diabetes to type 2 diabetes.

The current work looks to examine the underpinnings of anhedonic depression - i.e. depression associated with an inability to feel pleasure. This is an important group and little is know about the factors that characterise and are important for this outcome. The work will use detailed questionnaires to examine the likely presence or absence of this presentation in ALSPAC participants and there will be extremely detailed followup of a sub-set of participants likely to have this condition. This work will be in parallel with that in a study called NESDA in The Netherlands - providing important replication and sample size.

Research has shown that there is a link a mother's and a child's mental health. There is a pattern that where mothers have poor mental health, their children are at risk of developing mental health difficulties also. Research with parents of children with mental health difficulties has shown how distressing this can be for parents. For example, parents of young people who self-harm report significant distress related to their child's difficulties. We seek to explore the link between child mental health and subsequent maternal mental health. We focus on the mother as this is where there is most available data within ALSPAC. This will help us understand whether mothers are at risk of developing significant distress after their child has reported clinical levels of mental health difficulties.

We will also look at variables that describe family structure and characteristics of the mother and child. For example, we want to understand if child mental health problems are more likely to be linked to maternal mental health problems if family have a lower income or if the mother does not live with a partner. We are interested in factors that increase stress or reduce available support for mothers. This will help us identify if there may be characteristics that make a mother more likely to have poor mental health in relation to their child's distress. If so, this will help us make recommendations for how services may need to be made available to best support families.

In this project we will create "polygenic indexes" for the ALSPAC participants. Polygenic indexes are summaries of a persons known genetic predisposition to a particular trait, such as height. We will create these indexes for up to 47 different traits, which can then be use by other researchers.