Although genome-wide association studies (GWAS) have been very successful in identification of genetic variants associated with complex traits, the mechanistic links between these variants and complex traits are still largely unknown. A scientific hypothesis is that genetic variants influence complex traits via affecting cellular traits, e.g., regulating gene expression and altering protein abundance. Yet, how to systematically examine this hypothesis remains an open problem.
Recently, large genomic consortia are generating a vast volume of genomic data towards comprehensively characterizing the regulatory role of genetic variants. For example, the Genotype-Tissue Expression Project (GTEx) has collected genomic profile of 449 individuals, comprised of measurements of gene expression from multiple tissues and about 12.5 million DNA bases, providing unprecedented opportunities to dissect genetic contributions to complex traits. Statistical methods that effectively harness multilayer data (genetic variants, cellular traits and organismal traits) for mechanistic interpretation are highly demanding. The phenomenon that genetic variants have individually weak effects on complex traits makes statistical modeling very challenging, because it requires that the developed models can efficiently exploit information in low signal-noise-ratio (SNR) regimes.
In the proposed research, we aim at developing statistical methods to advance mechanistic understanding of the role of associated variants in complex traits. The key idea is built upon the emerging scientific evidence that genetic effects at the cellular level are much stronger than those at the organismal level. In our pilot study, we proposed a statistical approach, AUDIS, to accounting for uncertainty in dissecting genetic contributions to complex traits by leveraging regulatory information. We also developed a parameter expanded expectation-maximization (PX-EM) algorithm to ensure that AUDIS can perform stably in low SNR regimes. Interestingly, two popular methods in this field, SKAT and PrediXcan can be connected via a stagewise view of AUDIS, as supported by results from comprehensive simulation studies. Then we applied stagewise AUDIS to analyze 20 complex traits in GERA by incorporating the transcriptome data in the Genetic European Variation in Health and Disease (GEUVADIS) Project. Real data analysis results show that AUDIS can identify more genetically regulated genes that are significantly associated with complex traits, without inflated type I errors. To continue in this promising direction, we propose generalized AUDIS models to allow detection of trait-associated expression quantitative trait loci (eQTLs), as well as integrative analysis of GWAS data with transcriptome data from multiple tissues. We also propose to investigate theoretical properties of our methods, e.g., why AUDIS can be immune to model mis-specification.
The novelty of this research is that a statistically efficient and computationally feasible framework is developed to dissect genetic contributions to complex traits. Specifically, the proposed methods (AUDIS and its generalizations) can effectively borrow regulatory information at the cellular level and then boost statistical power of identifying genetically-regulated and trait-associated genes, even though in low SNR regimes. The stagewise view of AUDIS not only offers a statistical connection between existing approaches, but also highlights the importance of statistically rigorous design. Unlike conventional engineering approaches that often only consider a point estimate or a maximum a posteriori probability estimate, the stagewise view of AUDIS implies that the posterior distribution obtained in former stage should be naturally incorporated as the prior in the later stage. This gives birth to a modularized design of the whole data analytics system, greatly helping biomedical researchers to gain new scientific insights. The statistical and computational skills developed here are also broadly useful to many other applications in the field of data science.

Review of the literature relating dietary intake of sugars to adverse health outcomes is being undertaken by EFSA. Five ALSPAC papers have been selected for the review and need extra details added on precise intakes of sugars in order to improve their usefulness to the review

Childhood adversity is associated with increased risk of future psychiatric illness (Croft et al., 2018). Raised levels of markers of inflammation are also associated future psychiatric illness. We have shown that certain lipids (O'Gorman et al,2017 ) and complement and coagulation proteins (English et al, 2018; Focking et al, 2019) are also raised and /or dysregulated in association with future psychotic illness, and we have shown that these lipid and proteins changes are functionally related to each other (Madrid et al, 2019). Changes to these biological markers may reflect these environmental exposures, in addition to representing novel targets for therapeutic intervention. We have also shown that the complement and coagulation pathway proteins are elevated by exposure to stress in an animal model of social defeat (Focking et al, 2019). There are however, important gaps in our knowledge regarding the relationship between psychiatric outcomes, early exposure to adversity, and early biological measures such as blood protein, lipid, and inflammatory markers levels. Our proposal will address these gaps.

This current proposal aims to utilise a longitudinal design involving all subjects within the ALSPAC Cohort who gave blood at age 11 (n=2160) and / or age 17 (n=1776) and who participated in interviews at age 24 . Cytokine, proteomic and lipidomic analyses will be undertaken in plasma samples from all of these subjects. Exposure to childhood adversity will be determined using existing data from ALSPAC children and parents for a range of possible experiences such as bullying, physical abuse, sexual abuse and emotional neglect, as previously described (Croft et al., 2018). The primary outcomes of interest would be subjects at age 24 who report 1) psychotic experiences 2) depressive / affective symptoms 3) anxiety symptoms and 4) fulfill criteria for any psychiatric disorder at age 24. A secondary outcome will be that of global functioning, as determined from available ALSPAC data, at age 24.

Data will be compared and integrated between groups, and examined in relation to the detailed environmental exposure data available within ALSPAC.

Our overarching aim is to investigate the longitudinal relationship between environmental exposures, blood markers and different psychiatric outcomes.

We have the following specific objectives;
1. To investigate the longitudinal pattern of biomarker change in the different disorders.
2. To investigate the relationship between environmental exposures and biomarker expression.
3. To integrate this data with other, already available, genomic, neuroimaging and neuropsychology data in order to build a predictive model of psychiatric disorders generally based on early biological and environmental data.

This proposal represents a unique opportunity to understand the relationship between environmental experiences, longitudinal measures of biological markers and psychiatric outcomes at age 24.

References:

Croft, J., Heron, J., Teufel, C., Cannon, M., Wolke, D., Thompson, A., . . . Zammit, S. (2018). Association of Trauma Type, Age of Exposure, and Frequency in Childhood and Adolescence With Psychotic Experiences in Early Adulthood. JAMA Psychiatry. doi:10.1001/jamapsychiatry.2018.3155. Davison J, O'Gorman A, Brennan L, Cotter DR. A systematic review of metabolite biomarkers of schizophrenia. Schizophr Res. 2018 May;195:32-50. doi: 10.1016/j.schres.2017.09.021. Epub 2017 Sep 22. Review. PMID: 28947341.

Davison J, O'Gorman A, Brennan L, Cotter DR. A systematic review of metabolite biomarkers of schizophrenia. Schizophr Res. 2018 May;195:32-50. doi: 10.1016/j.schres.2017.09.021. Epub 2017 Sep 22. Review. PMID: 28947341.

English JA, Lopez LM, O'Gorman A, Föcking M, Hryniewiecka M, Scaife C, Sabherwal S, Wynne K, Dicker P, Rutten BPF, Lewis G, Zammit S, Cannon M, Cagney G, Cotter DR. Blood-Based Protein Changes in Childhood Are Associated With Increased Risk for Later Psychotic Disorder: Evidence From a Nested Case-Control Study of the ALSPAC Longitudinal Birth Cohort. Schizophr Bull. 2018 Feb 15;44(2):297-306. PMID: 29036721.

Föcking M, Sabherwal S, Cates HM, Scaife C, Dicker P, Hryniewiecka M, Wynne K, Rutten BPF, Lewis G, Cannon M, Nestler EJ, Heurich M, Cagney G, Zammit S, Cotter DR. Complement pathway changes at age 12 are associated with psychotic experiences at age 18 in a longitudinal population-based study: evidence for a role of stress. Mol Psychiatry. 2019 Jan 11. PMID: 30635638.

Madrid-Gambin F, Föcking M, Sabherwal S, Heurich M, English JA, O'Gorman A, Suvitaival T, Ahonen L, Cannon M, Lewis G, Mattila I, Scaife C, Madden S, Hyötyläinen T, Orešič M, Zammit S, Cagney G, Cotter DR, Brennan L. Integrated Lipidomics and Proteomics Point to Early Blood-Based Changes in Childhood Preceding Later Development of Psychotic Experiences: Evidence From the Avon Longitudinal Study of Parents and Children. Biol Psychiatry. 2019 Jan 30. PMID: 30878195.

O'Gorman A, Suvitaival T, Ahonen L, Cannon M, Zammit S, Lewis G, Roche HM, Mattila I, Hyotylainen T, Oresic M, Brennan L, Cotter DR. Identification of a plasma signature of psychotic disorder in children and adolescents from the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort. Transl Psychiatry. 2017 Sep 26;7(9):e1240. doi: 10.1038/tp.2017.211. PMID: 28949339

Rationale: Internalising problems (depression & anxiety) in children have been found to be predictive of negative outcomes later in life (Sallis et al., 2018) but the biological mechanisms underlying the development of these disorders are not well understood. There is some evidence that epigenetics might play a role, but results are mixed (Barker, Walton, & Cecil, 2018). In this study, we will examine internalizing traits in childhood and methylation, both cross-sectionally and at birth.

Observational studies have reported associations between low lung function and dementia risk but cannot test causality of this relationship. If causal, interventions to maintain lung function with ageing could reduce the burden of dementia. It is unclear how early in life the association between low lung function and cognitive impairment begins.
Observational studies have shown an association between asthma and multi-morbidity, especially psychological e.g. anxiety and depression. However this work has been carried out on small numbers of patients, and is purely cross-sectional. Using ALSPAC data we can do larger studies longitudinally gaining insight into how early these affects appear, how long they last and how severe. If we find a consistent association then we may be able to test for causality.

Shift work is a widespread feature of our society, with a third of men and a fifth of women in the UK engaged in it. However, shift work has been linked with a number of poor health outcomes, including obesity, diabetes, heart disease, depression and some types of cancer. The pathways mechanisms by which shift work might lead to these diseases are poorly understood. A major area of interest is the effect shift work, and night shift work in particular, has on altering a person’s body clock. There is information available on shift work patterns at multiple time points in Understanding Society. The availability of blood samples for some study participants provides an opportunity to look at biological changes associated with shift work, including the impact shift work might have on epigenetic modifications, which influence how our genes are expressed turned on or off (via epigenetic changes). This work will help us better understand how the occupational exposure of working shifts might become embodied in human biology, with the potential for long term health consequences.

LONGITOOLS will develop a holistic exposomic approach to this important health, societal and environmental challenge – LONGITOOLS will operationalise a very large and detailed set of longitudinal data with repeated measures spanning form the pre-conceptional period until late adulthood. The conceptualisation of the exposome paradigm defines the health status at any time during the life-course as being a function of a cumulative effect of all exposures an individual has been exposed to. In comparison to traditional models linking environmental exposure to a health outcome, an exposome-wide model will use the multiple dimensions within the data to reduce the fragmented view of a problem. Future opportunities to address the epidemic of MC-NCD, very likely to be a causal consequence of obesity, will depend on the capacity to:

i) model and utilise the correlation structure linking the environmental exposures to the life-course risk of MC-NCD e.g. social disadvantage linked to obesogenic urban design linked to pollution(s) linked to unhealthy behaviour (see impact section);

ii) define the possible paths (and the resilience factors) through which an unfavourable environment may cause health deterioration and

iii) identify from a silo of possible internal exposome how the environmental exposures convert into possible biological cause affecting the health risk (and vice-versa)

LONGITOOLS’s overarching aim is to harness the temporal dimensions in the exposome influencing the bi-directional association observed between individual health and the environment based on longitudinal models with repeated measures and survey of the exposome. The data incorporated as well as the researchers, knowledge managers and innovative network will be harnessed to demonstrate the temporal causality from the pre-conceptional period up until old age, with a dedicated focus on the life-course aetiology of circulatory, cardiac and metabolic NCDs.

We will empower this innovative consortium to adapt and develop statistical approaches of data-mining in exposomics with longitudinal built-in. We will focus on the changes in the epigenomic, metabolomics, and transcriptomics responses to encompass the internal dynamics. We will incorporate latent trajectory methods in the analysis of specific effect modifiers including diet, physical activity and behaviours. A first key exploitable tool for LONGITOOLS will be the integration in an exposome econometric life-course model of circulatory, cardiac and metabolic NCDs developed in longitudinal birth cohort studies. A second tool LONGITOOLS for impact will exploit our knowledge and innovative network to maximise impact towards innovative research, new technology and environmental and health policies.

All data and tools of LONGITOOLS will be harmonized alongside on-going European initiatives [DynaHEALTH, LifeCycle, BBMRI, EUCAN-Connect and EUCanSHare], Joint Project Initiatives PREcisE (ICL), NutriPROGRAM (EMC) and DiGuMet (CUT), we anticipate interaction with other funded projects to integrate LONGITOOLS into the EU Human Exposome Project Where additional health trajectories can be studied.

Children who are above a ‘healthy’ weight (medically classified as a BMI of over 25kg/m2) tend to do less well at school than their ‘healthy’ weight peers. Studies which followed children up over time found those who were overweight or obese tended to do less well, particularly for maths. This difference was mainly found in girls, and tended to emerge in the teenage years.

Various ideas have been suggested to explain this relationship, ranging from health-related absences, to the impact of size-related bullying, to the unconscious bias of teachers. However, quantitative evidence to support these explanations is inconsistent, making it hard to draw clear conclusions. Surprisingly, there have been very few qualitative studies that ask young people, or their teachers, their views on this issue.

We want to explore if, how and why being above a ‘healthy’ weight affects people’s experiences of school and their educational performance. To do this, we want to interview young people (aged 11-16 years) and young adults (from the ALSPAC cohort, aged ≈28 years) who are/were above a ‘healthy weight’ in adolescence.

We will recruit young people (11-16 years) from community-based weight management services in Bristol and the surrounding areas. This will allow us to explore the views of young people currently in school.

However, we also want to understand how body size and weight might affect education beyond secondary-school level and its potential longer-term impact on young people as they enter the work environment. For this reason, we would also like to invite ALSPAC participants who were above a ‘healthy’ weight in adolescence (11-16 years) to take part in interviews. As ALSPAC participants are now in their late twenties, they will be able to reflect on their entire educational ‘career’, as well as describing if and how it impacted their employment and experience of the workplace.

The transition into puberty is a critical developmental period associated with profound biological and psychosocial changes. During early adolescence there is a dramatic rise in depressive symptoms in girls compared with boys. By the mid-teens, girls are twice as likely to experience depression than boys, and this gender difference persists through adult life. Adolescence is, therefore, the key developmental period for the female rise in depression. The timing of the pubertal transition is of interest because early puberty in girls has been linked to a range of adverse outcomes in psychological, social, sexual and educational domains. Girls who mature earlier than their peers face a cascade of psychosocial challenges that may be inconsistent with their level of cognitive and emotional development. This may lead to feelings of isolation and increased vulnerability to depression. It is still unclear, however, whether adverse outcomes associated with early puberty translate into longer-term negative consequences for mental health in young women. Timing of puberty could also be important in determining risk for depressive symptoms/depression in boys, but findings are inconsistent and more research is needed. Few studies have examined mechanisms through which timing of puberty affects risk for depressive symptoms/depression. Psychosocial theories propose that individual and social factors explain more of the variance in adolescent depression than hormones alone.