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Evie Stergiakoulis (lead), Dr Sarah Lewis, Prof Michael Owen Prof Marianne van den Bree
Cleft of the lip and/or palate is a common birth defect worldwide and occurs at a rate of one in 650 live births in the UK. Being born with cleft places a significant burden on children, their families and the health system as they require surgery (multiple times depending on cleft type), and other interventions to improve appearance, speech, hearing, dentition and other adverse outcomes. They are also at increased risk of psychological, psychiatric and cognitive problems [1]. The UK-based Cleft Collective is a unique resource comprising the world’s largest cohort study of individuals affected by cleft and their families [2]. Rich longitudinal information is collected on the children’s mental health, parental, prenatal and early life factors as well as genetic data, providing unique opportunities to study genetic as well as environmental influences on risk of development of mental health conditions over time.
The PhD project will provide the first detailed description of neurodevelopmental and mental health outcomes in children with cleft and examine the contributions of genetic and environmental factors. We will use two unique genetically informative clinical cohorts of children; the University of Bristol Cleft Collective and the Cardiff University longitudinal ExperiencCes of people witH cOpy number variants (ECHO) study. Control samples will consist of the Avon Longitudinal Study of Parents and Children (ALPSAC) and the Millennium cohort which are deeply-phenotyped cohorts of typically developing children.
The aims of the study are: 1) To improve understanding of risk of neurodevelopmental and mental health outcomes in children born with cleft. This will be achieved by comparing children born with cleft to those at high genetic risk of neurodevelopmental and mental health outcomes but without cleft (children from the ECHO study) and typically developing children.
2) To improve understanding of the causes of neurodevelopmental and mental health outcomes in children born with cleft. This will be achieved by determining in children born with cleft the contribution of: a) composite genetic (polygenic) risk scores for neurodevelopmental and psychiatric disorders and b) rare genetic mutations.
3) To improve understanding of non-genetic factors, the project will also examine contributions of early developmental problems, family socio-economic status, family relationship quality, and traumatic experiences to risk of childhood psychiatric disorders in children born with cleft.
This multidisciplinary project will integrate expertise across cleft, genetic epidemiology, psychiatry, and epidemiology. Advanced epidemiological methods will be applied to study repeated measures and compare outcomes across cohorts. Copy Number Variants (CNVs) and other rare variants will be identified in children from the Cleft Collective and compared with mutations identified in children with neurodevelopmental disorders and children from the general population. Genetic epidemiological methods, such as polygenic risk scores, will be used to study the contribution of common variants and causally informative designs will be employed to test the causal link between cleft and mental health problems. The candidate will have the opportunity to develop into one of few experts globally with in depth understanding across these fields. The project will include training in advanced epidemiology, genetic epidemiology, bioinformatics and a mini-MD in the MRC Centre for Neuropsychiatric Genetics and Genomics at Cardiff.
[1] doi: 10.1016/j.jaac.2018.06.024
[2] http://www.bristol.ac.uk/dental/cleft-collective/
[3] doi: 10.1038/s41380-020-0654-3
[4] doi: 10.1371/journal.pgen.1007501
[5] doi: 10.1016/S2215-0366(19)30123-3
Dr Luisa Zuccolo (lead), Dr Laura Johnson, Dr. Laura Howe Dr. Maria Carolina Borges
The World Health Organisation recommends exclusively breastfeeding for 6 months, but in the UK, only 1% of infants meet the guidelines (1). Supporting mothers to maintain breastfeeding is a key priority for promoting child health. Breastfeeding for longer is associated with a reduced childhood obesity risk in observational studies(2), but breastfeeding is socially patterned meaning associations may be confounded. Also, the most common reason mothers report for stopping breastfeeding early is perceived milk insufficiency (often not real insufficiency)(1), so it is possible that mothers are more likely to worry about insufficient milk supply, and therefore to stop breastfeeding, if their baby is growing fast (which is associated with a higher risk of later obesity(3)). Thus a shorter breastfeeding duration may cause rapid growth or a faster growth rate may cause breastfeeding to stop. Disentangling this will help provide women with informed decisions about whether to stop breastfeeding.
This project aims to disentangle the relationship between infant feeding and infant growth and childhood obesity. This evidence will contribute to developing decision support systems to help mothers with infant feeding choices. Specific aims are to:
1. Establish whether and to what extent rapid infant growth affects infant feeding patterns, in particular shorter breastfeeding duration
2. Establish whether and to what extent longer breastfeeding duration protects against excessive weight gain in infancy
3. Investigate the persistence of these relationships with respect to childhood obesity
This project will use several methods developed and/or expertly applied within the MRC IEU, including Mendelian randomization, longitudinal modelling of childhood growth, and triangulation of epidemiological evidence.
Mendelian randomization is a study design that mimics a randomised experiment, by comparing groups of individuals randomly allocated a genetic predisposition for a trait or behaviour. Randomisation minimises confounding and reverse causation, both of which could explain the observed associations between breastfeeding and infant growth.
This project will harmonise phenotypic data on infant feeding across multiple cohorts and combine with data on maternal/offspring genetics and infant growth outcomes. The Early Growth Genetics (EGG) consortium, a collaboration of birth cohorts with genotype data combined with phenotypes including infant feeding, growth and later childhood obesity (5), will contribute data to this project, and so will several large biobanks internationally including the Norwegian HUNT study, the China Kadoorie Biobank and the UK Biobank.
Analyses will include:
1) Comparing the infant feeding patterns of children with different genetic predispositions to growing faster or having overweight/obesity;
2) Comparing infant growth or obesity in children genetically predisposed to being breastfed for longer, to those whose genetic makeup predicts shorter breastfeeding duration;
3) Cross-cohort comparisons utilising cohorts in diverse settings e.g. where the confounding of infant feeding by social class differs.
4) Formal triangulation of the evidence produced through 1-3 with previous evidence from Randomised Controlled Trials of breastfeeding promotion interventions.
1. DoH. Infant Feeding Survey 2010; http://www.ic.nhs.uk/catalogue/PUB00648/infaseed-serv-2010-earl-resu-rep.pdf
2. Yan J et al. BMC Public Health. 2014;14(1):1267
3. Zheng M et al. Obesity Reviews. 2018;19(3):321-32
4. Johnson L et al. Int J Obesity (2005). 2014;38(7):980-7
5. Middeldorp CM et al. Eur J Epidemiol. 2019;34(3):279-300
Dr Evie Stergiakouli (lead), Prof Claire Haworth, Dr Oliver Davis, Dr Alexandra Havdahl
Children are now exposed to and use digital media from a very young age both for entertainment and educational purposes. Parents and clinicians are concerned about the potential harmful effects that digital media use may have on children’s mental and physical health [1,2,3]. However, most studies have been cross-sectional and have shown positive as well as negative effects (with the exception of excessive use and extreme online harmful behaviour) [4] and they have not considered the effect of digital media use on children from the general population. COVID-19 and lockdown increased digital media use for the majority of children and this has been linked to positive effects, such as increased connectedness, at the time that it was most needed [5].
The aim of this project is to examine if digital media use is having an impact (positive or negative) on children’s mental health and explore the mechanisms behind any associations. We also aim to determine if there are sensitive windows in development or situations (such as lockdown) when digital media are most/least harmful and if there are groups of children (e.g. with pre-existing neurodevelopmental problems) that are particularly vulnerable.
The PhD student will use genetic epidemiological methods to test if digital media use is causally associated with mental health in children. Methods will include performing GWAS for digital media use, polygenic risk score analysis [5] and Multivariable Mendelian randomization [6]. These methods will be applied across large cohorts of children from the general population with mental health data across time, such as the Avon Longitudinal Study of Parents and Children (ALSPAC), the Millennium Cohort and the Norwegian Mother, Father and Child Cohort Study (MoBa).
1. Canadian Paediatric Society, Paediatr Child Health. 2019; 24(6): 402–408.
2. Ra CK, et al. JAMA. 2018;320(3):255–263. doi:10.1001/jama.2018.8931
3. Hoge E et al. Pediatrics. 2017;140(Suppl 2):S76-S80. doi: 10.1542/peds.2016-1758G.
4. Orben, A. Soc Psychiatry Psychiatr Epidemiol 2020; 55, 407–414.
5. Widnall E et al. 24th August 2020, NIHR School for Public Health Research
6. Davey Smith G, Hemani G, Human Molecular Genetics 2014;23(R1):R89–R98, https://doi.org/10.1093/hmg/ddu328
7. Leppert B et al. JAMA Psychiatry. 2019;76(8):834–842. doi:10.1001/jamapsychiatry.2019.0774
Prof Deborah Lawlor (lead), Dr Maria Carolina Borges, Dr Ge Zhang Dr Nicole Warrington
Disruptions in maternal metabolism during pregnancy (e.g. maternal hyperglycaemia) potentially affect several aspects of maternal health and fetal development. Understanding which molecular pathways are implicated in adverse child and maternal health is key to inform effective interventions to improve maternal-child health; however, producing reliable evidence on that is challenging due to issues of confounding and reverse causation in observational studies and due to the scarcity of evidence from high-quality randomised controlled trials. Mendelian randomisation is a method that uses genetic variants robustly associated with modifiable exposures to generate more reliable evidence regarding which risk factors to intervene to produce health benefits. Mendelian randomisation can be used to discriminate causal from non-causal molecular traits, which may facilitate targeted development of interventions and inform evidence-based recommendations for pregnant women.
To use Mendelian randomization to systematically assess the effect of the maternal molecular profile (methylome, proteome and metabolome) during pregnancy on a wide-range of perinatal health outcomes (e.g. gestational hypertension, gestational diabetes, perinatal depression, need of induction of labour, caesarean delivery, early-membrane rupture, preterm delivery, birth weight, miscarriage, stillbirth).
1. To develop genetic instruments for probing the effect of changes in maternal molecular traits during pregnancy
2. To use novel statistical methods for partitioning genetic effects at single loci into maternal and offspring genetic components
3. To use (one-sample and two-sample) Mendelian randomisation to probe the causal role of maternal molecular traits on perinatal health
4. To triangulate Mendelian randomisation findings with findings from other study designs (e.g. negative paternal control, randomised controlled trials)
https://wellcomeopenresearch.org/articles/2-11
https://www.ncbi.nlm.nih.gov/pubmed/26978208
https://www.ncbi.nlm.nih.gov/pubmed/30815700
https://www.ncbi.nlm.nih.gov/pubmed/31335958
https://www.biorxiv.org/content/10.1101/737106v2.full
Dr Rebecca Richmond (lead), Gemma Sharp, Prof Deborah Lawlor
A large body of literature from animal studies has highlighted the role of core clock genes in relation to reproductive function [1]. In contrast, only a few epidemiological studies have implicated the circadian clock with human reproduction. These have largely focused on investigating the modification of menstrual cycle patterns in relation to shift work [2,3]. Circadian dysregulation as captured by sleep traits have also been associated with menstrual-related disorders [4] and fertility [5]. In turn, menstrual cycle characteristics 6 and reproductive events such as menopause [7] have been previously linked to changes in sleep, which may imply a bi-directional relationship.
Large epidemiological resources such as ALSPAC and the UK Biobank include detailed data on both sleep characteristics, reproductive traits and menstrual disorders which may be used to investigate associations between these traits. Genetic variants for a range of sleep characteristics, reproductive traits and menstrual conditions have been identified in recent genome-wide association studies (GWAS) which can be used establish and orient causal relationships. In particular, bivariate LD score regression calculates the genetic correlation between two traits [8] and Mendelian randomization (MR) uses those genetic variants most strongly associated with one trait to establish a causal effect on another [9-11].
To use observational and genetic epidemiological approaches within UK Biobank, ALSPAC, and other genetic studies and consortia (ReproGen, Human Reproductive Behaviour consortium, FinnGen) to: i) investigate cross sectional and prospective associations between sleep characteristics and reproductive/menstrual traits , ii) estimate the genetic correlations between sleep characteristics and reproductive/menstrual traits and iii) evaluate causal effects of sleep on reproductive traits and vice-versa.
1) Derive sleep, reproductive and menstrual measures from ALSPAC and UK Biobank
2) Perform multivariable regression analyses to investigate the associations between these traits, both cross sectionally and prospectively
3) Identify genome-wide association studies related to a series of sleep characteristics (insomnia, chronotype, sleep duration, daytime sleepiness, circadian disruption), reproductive traits (age at menarche, age at menopause, age at first birth, number of births, infertility, sex hormones) and menstrual conditions (dysmenorrhea, PCOS, endometriosis, menstrual cycle length) from GWAS data repositories, specifically the GWAS Catalog (ebi.ac.uk/gwas) and IEU Open GWAS (gwas.mrcieu.ac.uk/).
4) Perform bivariate LD score regression analysis to investigate genetic correlations between sleep and reproductive traits.
5) Extract genome-wide significant genetic variants related to those traits where there is evidence for genetic correlation.
6) Perform both one- and two-sample MR analyses to establish causal relationships.
7) Conduct sensitivity analyses to evaluate the robustness of findings.
8) Compare MR estimates with those obtained from multivariable regression.
1. Kennaway DJ, Boden MJ, Varcoe TJ. Circadian rhythms and fertility. Mol Cell Endocrinol. 2012;349(1):56-61.
2. Stocker LJ, Macklon NS, Cheong YC, Bewley SJ. Influence of shift work on early reproductive outcomes: a systematic review and meta-analysis. Obstet Gynecol. 2014;124(1):99-110.
3. Gamble KL, Resuehr D, Johnson CH. Shift work and circadian dysregulation of reproduction. Front Endocrinol (Lausanne). 2013;4:92.
4. Baker FC, Driver HS. Circadian rhythms, sleep, and the menstrual cycle. Sleep Med. 2007;8(6):613-622.
5. Goldstein CA, Smith YR. Sleep, circadian rhythms and fertility. 2016. 2016;2(4):206-217.
6. Baker FC, Lee KA. Menstrual Cycle Effects on Sleep. Sleep Med Clin. 2018;13(3):283-294.
7. Eichling PS, Sahni J. Menopause related sleep disorders. J Clin Sleep Med. 2005;1(3):291-300.
8. Zheng J, Erzurumluoglu AM, Elsworth BL, et al. LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis. Bioinformatics. 2017;33(2):272-279.
9. Davey Smith G, Ebrahim S. 'Mendelian randomization': can genetic epidemiology contribute to understanding environmental determinants of disease? Int J Epidemiol. 2003;32(1):1-22.
10. Davey Smith G, Hemani G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum Mol Genet. 2014;23(R1):R89-98.
11. Davies NM, Holmes MV, Davey Smith G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. BMJ. 2018;362:k601.
Dr Anya Skatova (lead), Dr Andy Skinner , Prof Tom Gaunt, Prof Richard Martin
Shopping history records, collected via purchases tracked on loyalty cards, can provide a new perspective on lifestyle choices and behaviours and how these relate to health outcomes such as cancer. Shopping histories can provide information, which is otherwise difficult to measure such as granular, population level, objective data on lifestyle behaviours and risk factors (e.g., smoking, alcohol consumption) that can be tracked longitudinally. However, shopping history data also have inherent biases. For example, despite providing details on purchasing habits and basic individual characteristics, patterns in the data could be explained by other factors (e.g., the gap between purchase and consumption). Reliability of health information that is derived from shopping history data can be assessed through integrating these data with detailed self-reports of behaviour collected through Ecological Momentary Assessment (EMA). This work will improve detection of cancer risk as well assess validity of integrated data sources in risk prediction.
The overall aim of this PhD is to integrate supermarket loyalty cards data with EMA data and conventional epidemiological measures (eg questionnaires, biomarkers, etc) in Avon Longitudinal Study of Parents and Children (ALSPAC) to predict risk factors for cancer. The innovative aspect is the use of transactional and EMA data, which provide higher-density time-series data with different biases from conventional questionnaire/interview data. The ability to predict risk factors using such data could produce novel insights of early cancer symptoms and associated consumption patterns.
Identify patterns in standalone shopping history data that can be reflective of consumption association with known risks of cancer (Years 1 – 2). Collect EMA data on behaviours related to known risks of cancer using wearable technology (e.g. smartwatches) (Year 2). Use statistical methods (e.g., linear and logistic regression) to validate shopping histories patterns through EMA and conventional self-report/biomedical data in ALSPAC (Years 2-3). Use statistical and machine learning methods to predict cancer risk factors in the ALSPAC dataset in a sample of thousands of ALSPAC participants as well as standalone supermarket loyalty cards data in population-wide sample of millions of supermarket customers (Years 2-4)
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Dr Anya Skatova (lead), Prof Deborah Lawlor ,
Shopping history records collected by supermarkets contain population level health information which could be missing from traditional health research data such as medical records. For example, shopping transactions can provide granular and objective data on under/unreported behaviours and outcomes in reproductive health domain – related to pain and weight management, vitamins consumption, infant feeding, etc - that can be tracked longitudinally. Combining shopping history datasets with epidemiological methods has potential for health research and might improve diagnosis, disease prevention and planning of interventions.
The aim of the PhD is to explore the potential of shopping history data to identify key reproductive events and lifestyle choices around these in real time. The specific focus of the PhD will be developed by the student, with potential objectives including: (1) determining the accuracy of shopping history to determine one or more reproductive events, such as conception, pregnancy, breastfeeding or parenthood; (2) whether shopping histories can identify lifestyle changes around these events, such as pre-conception, pregnancy and breastfeeding related changes in diet; (3) the extent to which shopping histories enhance repeat data collected in cohort studies, for example, shopping histories with data in real time might be able to pinpoint the timing of events such as planning a pregnancy and conception, whereas cohort data collected from movement sensors over periods that coincide with the timing of these events might better identify changes in physical activity and sleep patterns. The PhD will work with standalone population level supermarket shopping histories data, as well as a subset of shopping histories data linked into Avon Longitudinal Study of Parents and Children (ALSPAC).
The student will mainly work with shopping histories data of a large UK health and beauty retailer, both standalone (>12.5m customers, >1.5 billion transactions) and linked into ALSPAC (for ~1,500 index ALPSAC participants). There is a scope for additional new quantitative data collection with ALSPAC participants where it is needed to meet research aims of the PhD project.
Shopping histories data will be used first to identify a reproductive life event of interest (e.g., pregnancy) and a time window associated with it. Products that are bought during this time window will be then explored. This will allow to identify other behaviours (e.g., pain management, fertility issues) and health outcomes (e.g., miscarriages) associated with this life event. Those behaviours and outcomes will be then validated through the contextual variables using the data available in ALSPAC (and new data collected through surveys) related to causes and consequences of the life event. The student will be expected to explore the structure of the repeat shopping data and identify appropriate methods for analysing those data. For example, repeat purchasing of sanitary products (indicative of menstruation) which change over time might be analysed by multilevel models or structural equations depending on the structure of the data and the specific research question.
This is a data intensive quantitative PhD. The successful candidate would be expected to have had experience of statistical analysis in their first degree, be competent in handling complex large-scale data and eager to learn new quantitative methods and/or about new topic areas in a multidisciplinary team. Depending on their previous experience the successful candidate will obtain training in epidemiology, survey design and data collection, advanced statistical methods, and data science, including the ethics and governance and management and use of data, through the completion of the research project and through postgraduate short courses.
Dr Amanda Hughes (lead), Professor Laura Howe, Dr Helen Bould
In high-income countries, tackling obesity is seen as a key priority by policymakers and health inequality researchers. As a result, underweight is largely ignored, despite clear links between low body weight and worse health and higher mortality. Underweight has diverse causes: it can result from mental illnesses including eating disorders, but it may also reflect food insecurity and socioeconomic deprivation. In the UK, changes to welfare policy during the 2010s were accompanied by increased foodbank use(1) and a starkly raised risk of underweight was in this period reported among British adult jobseekers(2). More recently, the COVID-19 pandemic brought profound economic and lifestyle changes. Alongside increased food insecurity in low-income families(3) and a record rise in foodbank use(4), there was a stark rise in referrals and hospitalizations for eating disorders(5). These changes mean that the prevalence, risk factors, and social patterning of underweight must now be reassessed.
This project aims to comprehensively investigate the prevalence, social distribution, and risk factors of low body weight in the contemporary UK. To shed light on potential causes, it will explore changes between the early 2000s and the early 2020s. It will consider several stages of the life-course, including adolescence, young adulthood, and middle-age. In each historical period, it will explore the contribution of sociodemographic risk factors, physical and mental health, and genetics.
Data will come from birth cohort studies: the 1946, 1958 and 1970 Birth Cohort Studies, Avon Longitudinal Study of Parents and Children (ALSPAC), and Millennium Cohort Study (MCS). All studies contain rich information including sociodemographic factors, objective height and weight measurements, self-perception of body weight, mental health, and physical health. Repeat measurements allow investigation of change within cohorts. Between-cohort change can be examined by comparing aged-matched participants across surveys. The contribution of mental illness will be examined using measures of common mental disorder (depression/anxiety symptoms) and, where available, of eating disorders. Where genetic information is available, genetic causal inference methods will be used to further unpick the contribution of health and health-related behaviours (e.g. smoking). To capture the effects of the COVID-19 pandemic, the project will use post-pandemic information from ALSPAC, MCS, and the 1958 cohort.
1. Reeves A, Loopstra R. J Soc Policy. 2020.
2. Hughes A, Kumari M. Prev Med. 2017.
3. Parnham JC et al. Public Health. 2020.
4. Baraniuk C. BMJ. 2020.
5. Solmi F et al. Lancet Child Adolesc Heal. 2021.
Josine Min (lead), Caroline Relton, Jonathan Mill (University of Exeter Medical School) Eilis Hannon (University of Exeter Medical School) The student will also collaborate with Dr. Gibran Hemani (University of Bristol), Prof. Tom Gaunt (University of Bristol), Prof. Bastiaan Heijmans (LUMC, The Netherlands) and Dr. Jordana Bell (KCL, UK).
DNA methylation is an epigenetic biomarker that has been shown to reflect lifestyle and biological factors (smoking, alcohol use, chronological age). To date the majority of studies used to link behavioral phenotypes such as cigarette smoking, and alcohol use to health outcomes typically employ self-reported questionnaire data. Multiple DNA methylation (DNAm) sites are strongly associated with (behavioural) traits. DNAm derived scores have been used to predict (or proxy for) these traits providing greater precision and biological proximity than self-reported measures. The DNAm derived smoking score is a widely used biomarker of lifetime exposure to tobacco smoke and may explain the molecular mechanism of the long-term risk of diseases following smoking cessation. There is growing interest in conducting genome-wide association studies (GWAS) and Mendelian randomization (MR) analysis on DNAm scores to identify novel genetic and causal factors influencing behavioural traits. To date, several GWAS on DNAm derived scores of aging have been published (Lu et al. 2018, McCartney et al. 2021). The many benefits to identify novel loci and biological pathways for other phenotypes have yet to be gained. This studentship will provide cross-disciplinary training in state-of-the-art genetic and genomic epidemiological approaches (under the supervision of Dr. Josine Min and Prof Caroline Relton at the Medical Research Council Integrative Epidemiology Unit at the University of Bristol and Prof Jonathan Mill and Dr. Eilis Hannon at the University of Exeter Medical School) to address questions about the molecular mechanism underlying established disease risk factors. The student will combine epigenetic, genetic and causal inference analyses in large-scale epidemiological datasets.
The overall aim of this PhD is to identify genetic variants and biological pathways associated with disease risk factors using DNAm scores. The specific risk factors/diseases for this project would depend on the candidate's research interests, but could include cell counts, smoking or alcohol use. The Genetics of DNA Methylation Consortium (GoDMC; http://www.godmc.org.uk/) has collected genetic and DNAm data across multiple cohorts offering the student an excellent platform for these analyses.
Methods:
1) Novel methodology can be used (and potentially developed) to construct DNAm scores on disease risk factors
2) GWAS on DNAm derived phenotype datasets will be conducted followed by meta-analyses. There will be several challenges with this type of analysis including heterogeneity of datasets in age, sex and tissue type.
3) To understand what aspect of the phenotype is captured by the DNAm phenotype, GWAS meta-analysis results will be compared to GWA results of detailed (self-reported) phenotypes (eg. in UK Biobank) and methylation quantitative loci from blood and brain.
4) MR analysis will be used to investigate causal relationships between DNAm derived measures and self-reported measures and other diseases/risk factors.
5) The heritability component of DNAm derived phenotypes will be estimated.
Lu AT, Xue L, Salfati EL, et al. GWAS of epigenetic aging rates in blood reveals a critical role for TERT. Nat Commun. 2018;9(1):387. Published 2018 Jan 26. doi:10.1038/s41467-017-02697-5
McCartney DL, Min JL, Richmond RC, et al. Genome-wide association studies identify 137 genetic loci for DNA methylation biomarkers of aging. Genome Biol. 2021;22(1):194. Published 2021 Jun 29. doi:10.1186/s13059-021-02398-9
Dr. Josine Min (lead), Prof Caroline Relton, Prof Jonathan Mill (University of Exeter Medical School) Dr. Eilis Hannon (University of Exeter Medical School) The student will also collaborate with Dr. Gibran Hemani (University of Bristol), Prof. Tom Gaunt (University of Bristol), Prof. Bastiaan Heijmans (LUMC, The Netherlands) and Dr. Jordana Bell (KCL, UK).
GWAS studies have discovered many genetic associations for traits and diseases, but it has been difficult to elucidate the functional consequences of these variants. One of the reasons for this is linkage disequilibrium (co-inheritance of many variants with the disease variant). Another reason is that most GWAS signals reside in non-coding regions (outside genes), and it is likely that GWAS variants confer their effects through modulating of regulatory mechanisms. Molecular traits (such as gene expression, DNA methylation, proteins) have increasingly been used to address this question because they are often dysregulated in disease and can act as an intermediate phenotype.
DNA methylation levels can be routinely assayed at a large scale using micro-arrays. Multiple studies have identified genetic variants associated with DNA methylation (mQTL: methylation quantitative trait locus) by combining genome wide genotype information with DNA methylation levels. mQTLs can be defined as cis, which are typically nearby a DNA methylation site and can be found in small sample sizes. Trans mQTLs (defined as associations which are further away or on different chromosomes) have smaller effects and large sample sizes are needed. The Genetics of DNA methylation Consortium (GoDMC, http://www.godmc.org.uk/) brought together a large number of cohorts to identify these small effects in blood and investigated whether the mQTLs play a role in disease etiology. Using several causal inference approaches, they discovered a small number of causal relationships between trait and DNA methylation (Min JL et al.).
DNA methylation might be on the causal pathway only in the trait-relevant cell-type or context. However, it is unknown to what extent cell-type specific DNA methylation signals differ from bulk tissue (eg. blood has many cell-types and each cell-type might have a different methylation level), and whether cell-type specific DNAm signals are on a causal path to disease as genome-wide mQTL resources across multiple ancestries, tissues and cell types are not currently available. This studentship will provide cross-disciplinary training in state-of-the-art genetic and genomic epidemiological approaches (under the supervision of Dr. Josine Min and Prof Caroline Relton at the Medical Research Council Integrative Epidemiology Unit at the University of Bristol and Prof Jonathan Mill and Dr. Eilis Hannon at the University of Exeter Medical School) to address questions about the human genetic basis of DNA methylation variation. The student will combine epigenetic, genetic, cellular deconvolution and causal inference analyses in large-scale epidemiological datasets.
The overall aim of this PhD is to compile a mQTL catalogue of uniformly processed mQTLs across a wide range of tissues, cell types, ancestries and across the lifecourse. The use of the catalog for this project would depend on the candidate’s research interest. It could be used for Mendelian randomization analyses to gain insights into the regulation of GWAS signals. GoDMC has collected genetic and DNA methylation data across multiple cohorts offering the student an excellent platform for these analyses.
1) mQTL analysis will be conducted followed by meta-analyses. There will be several challenges with this type of analysis including heterogeneity of datasets (eg. age, sex).
2) Novel methodology can be used (and potentially developed) to combine mQTLs across different studies, ethnicities, tissues and timepoints.
3) Cellular deconvolution approaches and analysis of cell-types from bulk tissue will be used to understand whether mQTLs operate in a cell-type specific manner.
4) Mendelian Randomization analysis will be used to investigate causal relationships between DNA methylation and diseases/risk factors.
Min JL et al. https://www.medrxiv.org/content/10.1101/2020.09.01.20180406v1
Dr Duleeka Knipe (lead), Prof Laura Howe, Dr Maria Theresa Redaniel Dr Nanette Mayol Lee
The burden of mental disorders and self-harm are most acutely felt in the poorer nations of the world. Low- and middle-income countries (LMIC) account for 80% of disability-adjusted life years (DALYs -a measure of overall disease burden) to depressive disorders and self-harm (70% of all self-harm occur in LMIC, 80% of suicide deaths).
Despite this our understanding of what contributes to the development of ill mental health is largely unknown in these emerging economies and is limited by the current evidence base. Firstly, much of the existing evidence uses retrospective study designs, which make explorations of causality difficult. Secondly, there is a growing body of evidence from high-income countries that poor mental health such as depression develops as a consequence of factors that work across the life course – this is less well explored in LMIC. Lastly, many of the investigations exploring risk factors for depression and suicidal behaviour in LMIC fail to appropriately account for the different experiences (e.g. type and frequency of adverse childhood experiences) and environmental contexts. The relative importance of certain influences at various developmental stages may differ. Without better quality evidence, progress in reducing the burden of mental ill health and suicide in LMIC will continue to be hindered. An improved understanding of contributing factors to the development of depression and suicidal thoughts is urgently needed to allow for contextually relevant interventions to be developed.
This PhD will address these limitations by utilising an ongoing large cohort dataset (Cebu Longitudinal Health and Nutrition Survey - CLHNS) established in the Philippines. It is a follow-up study of 3327 Filipino women who gave birth in the early 1980s and their 3080 offspring. The offspring of the index children are also now being followed up. This rich data source includes data on physical health (e.g. stunting, morbidity),social (e.g. household composition, educational outcomes), economic (e.g. financial resources), and environmental (e.g. neighbourhood assessment) factors. There are also measures of childhood adversity (e.g. parental domestic violence, child labour). The inclusion of measures over time will allow for the exploration of potential mechanisms by which certain factors might contribute to later mental health outcomes at different developmental stages. Importantly this is one of the only established birth cohort studies which have tracked individuals through their childhood that has data on depression symptoms and suicidal thoughts in adulthood (ages 18, 21, and 35) from a LMIC.
This project will investigate the influence of factors across development on adulthood depression and suicidal thoughts in a LMIC. It will aim to identify potential high-risk groups (including modifiable risk-factors) for the development of early interventions, and modifiable mediators when the exposure is less amenable for intervention (e.g. parental death).
The study will address the following questions:
What factors across development (prenatal to young-adulthood) contribute to depression and suicidal thoughts in adulthood?
What are the mechanisms through which risk-factors impact on mental health?
Are there specific time-points at which the exposure to certain risk factors contributes to a worsening of depression and/or suicidal thoughts in adulthood?
This PhD spans the disciplines of psychiatry, psychology, and epidemiology. It provides a unique opportunity for the student to develop skills across a range of sophisticated epidemiological and statistical techniques, including mediation analysis, strategies for dealing with missing data (such as multiple imputation) and causal inference. Given the paucity of evidence and the high-quality nature of the dataset, this project has the potential to significantly contribute to the knowledge base and identify contextually relevant targets for intervention.
The candidate will learn advanced epidemiological (e.g. causal inference) and statistical (e.g. multilevel modelling) skills, as well as gaining expertise in a range of statistical software packages.
Professor Laura Howe (lead), Dr Amanda Hughes, Dr Matt Dickson, University of Bath, Institute for Policy Research Professor Frances Rice, Cardiff University, Division of Psychological Medicine and Clinical Neurosciences
The transition to higher education (HE) is a key point in a person’s life course; decisions at this time can have lifelong influences. The experience of a mental health or neurodevelopmental condition could lead some people to make decisions that undermine their academic potential, e.g. choose not to participate in HE despite receiving sufficient qualifications, choose a less prestigious university than their grades would make them eligible for, or choose to attend a local university and live at home. These differences may be exacerbated by parental mental health or by genetic factors. As such, own and parental experiences of mental health and neurodevelopmental conditions may entrench intergenerational patterns of socioeconomic (dis)advantage.
1. Assess the influence of life course trajectories of depressive symptoms and neurodevelopmental conditions with decisions about higher education.
2. Assess the influence of parental mental health with decisions about higher education, and the degree to which these are mediated by own mental health.
3. Quantify the role of mental health and neurodevelopmental conditions in the intergenerational transmission of socioeconomic (dis)advantage.
4. Evaluate the influence of genetic risk scores for educational attainment and ADHD on decisions about higher education.
The project involves statistical analysis of data from the Avon Longitudinal Study of Parents and Children (ALSPAC), including:
- Measures of depression, anxiety, ADHD and ASD, repeatedly across childhood through to adulthood
- Measures of parental depression and anxiety
- A detailed questionnaire on reasons for (not) participating in HE, reasons for choices of university and course, and details of university attended
- Linked data on educational test performance across childhood and adolescence
- Genetics
Methods will include regression modelling, techniques for modelling longitudinal data, mediation analysis, and analysis of genetic data. Where possible, we will draw on additional studies for genetic analyses to boost statistical power.
Reflecting the interdisciplinary nature of the project, this PhD involves collaborations with the Institute for Policy Research at the University of Bath and the Division of Psychological Medicine and Clinical Neurosciences at Cardiff University. The student will be based in the MRC Integrative Epidemiology Unit and the Department of Population Health Sciences at the University of Bristol, but will have the opportunity to spend time in all centres.
López-López JA et al. BJPsych Open. 2019:6(1):e6
https://www.medrxiv.org/content/10.1101/2020.05.21.20108928v2
Dardani et al. Int J Epidemiol. 2021 dyab107
Hughes A et al. NPJ Sci Learn. 2021;6(1):1.
Belfields C, et al. The impact of undergraduate degrees on early-career earnings. IFS 2020
Sullivan A, et al. Br J Sociol. 2018 Sep 1;69(3):776–98.
Dr Gemma Sharp (lead), Dr Dan Bernie, Dr Chin Yang Shapland Prof Kate Tilling
Climate change will affect human health through changes to heat stress, sanitation, access to sufficient food and safe drinking water, disease patterns, migration, and frequency of extreme weather events.
Pregnant women and the developing fetus are considered amongst the most vulnerable and marginalised members of society, and could therefore be uniquely sensitive to the effects of climate change. Previous research has shown that babies of mothers exposed to higher ambient temperatures during pregnancy are at higher risk of preterm birth and low birthweight. These perinatal outcomes are associated with greater risk of neonatal ICU admission and infant mortality, as well as long term outcomes such as neurodevelopmental delays and cardiovascular disease.
Research is required to characterise the potential effects of climate change on these outcomes, both now and in the future, and to inform strategies to reduce or manage an increase in their prevalence in line with climate change.
This exciting project aims to explore how epidemiological data and approaches can be best applied to study the effects of climate change. The project focuses on how exposure to specific aspects of climate during pregnancy relate to perinatal health outcomes: gestational age at birth and birth weight.
We will link health and demographic data from sources including UK Biobank, ALSPAC and Born in Bradford to detailed historical climate data from the UK Met Office including 1km resolution data from HadUK-Grid data, supplemented with complimentary observational station data and climate reanalysis products as necessary. We will then explore associations between climate exposures (as measured by ambient temperature, precipitation, barometric pressure, hours of sunlight, or compound measure of environmental stress) during pregnancy in relation to birth weight and gestational age at delivery. Severity and duration of exposure will be sampled on daily to monthly time scales. Results will be then used to define parameters in projection models of the impact of climate change on these outcomes going forward under different scenarios. The application to future climate projections will link to the UK Climate Projections (UKCP18) which for the first time provide national scale climate projections at a resolution comparable to weather forecasts.
In addition to working with a team of highly experienced experts in epidemiology and population health, the student will work with climate experts, including Dr Dan Bernie, who has a joint appointment with the Met office. There will also be opportunities for training and gaining experience in public and policy engagement to help translate findings and create impact.
Climate change and the potential effects on maternal and pregnancy outcomes: an assessment of the most vulnerable – the mother, fetus, and newborn child. Rylander et al. 2013 Glob Health Action https://www.ncbi.nlm.nih.gov/pubmed/23481091
Exploring associations of maternal exposure to ambient temperature with duration of gestation and birth weight: a prospective study. Li et al 2018 BMC Pregnancy Childbirth https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6311008/
Systematic review on adverse birth outcomes of climate change. Poursafa et al 2015 J Res Med Sci https://www.ncbi.nlm.nih.gov/pubmed/26109998
Dr Gemma Sharp (lead), Prof Sarah Lewis, Dr Evie Stergiakouli
Worldwide, roughly one in every 750 babies is born with a cleft of the lip and/or palate. These children face multiple issues with feeding, teeth, speech and hearing, which can last throughout their lives, even after surgery to repair the cleft(s).
There is a high degree of heterogeneity in terms of severity of outcomes. Some individuals have very few issues and require minimal clinical care or adjustment, whereas for others the impact is huge and necessitates lifelong care from multiple disciplines. This presents a challenge to health services looking to allocate finite resources, as a one-size-fits-all approach is unlikely to be economically efficient and could take resources away from those most in need. A better ability to predict which children born with a cleft are at risk of poor outcomes would help clinicians tailor care. This could ultimately result in better cleft care outcomes.
Outcomes like speech, neurodevelopment, mental wellbeing and educational attainment are likely to be influenced by a combination genetics and environment. Epigenetics, which will be influenced by genetics and the environment, could also play a role. Therefore, integrating genetic and epigenetic data with non-molecular data about a child’s cleft phenotype, family history and home environment could help predict risk of outcomes.
This project will use genetic, epigenetic, questionnaire and clinical data to develop and test new risk scores to predict health and social outcomes in children born with a cleft lip/palate. Working closely with the cleft patient and clinical community, the student’s findings will help inform development of clinically useful risk factor profiles to help tailor cleft care and ultimately improve outcomes.
In this project, the student will use information from questionnaires and medical records to identify which clinical (e.g. cleft type, feeding and speech problems) and social (e.g parent’s educational level, parent’s mental health) factors predict worse cleft-related outcomes.
They will use published genetic and epigenetic studies to identify SNPs and CpGs associated with various child health and social outcomes (e.g. obesity, ADHD, social communication, speech traits, depression, etc). They will then generate several polygenic risk scores (PRS) and DNA methylation-based risk scores (MRS) for these outcomes in data from the Cleft Collective cohort studies, which is a detailed longitudinal study of ~3000 children born with a cleft in the UK.
The student will use regression analyses and ROC-curves to assess the ability of these PRSs and MRSs to predict relevant outcomes in children born with a cleft. The student will also examine the predictive ability of non-molecular factors, such as cleft type, socioeconomic position and infant feeding, and compare predictive performance of various combinations of environmental factors, PRS and MRS in predicting different outcomes.
Finally, the student will select the highest performing combination of predictive factors and develop risk scores for each outcome. They will then test the ability of these risk scores to predict outcomes in (an) independent dataset(s) of children born with a cleft. Such datasets could include a subset of the Cleft Collective retained for testing purposes (I.e. not included in the development of the risk scores), or other cohorts of children born with a cleft with information on outcomes and genetics and/or epigenetics from our collaborators in Norway, Germany and/or the USA.
Professor Richard Martin (lead), Dr James Yarmolinsky, Dr Philip Haycock
Circulating proteins play fundamental roles in biology and disease aetiology, including development of cancer, and are the direct targets of many drugs (1). Recent large-scale genome-wide association studies (GWAS) of circulating protein have identified thousands of protein quantitative trait loci (pQTLs) that influence protein levels (2-5). These variants can be used in a Mendelian randomization (MR) framework to estimate the causal effects on circulating proteins on cancer risk or progression (6). Robust identification of circulating proteins that causally influence risk or subsequent progression of cancer can then be used inform development of pharmacological interventions for cancer prevention and treatment.
The overall aim of this project is to identify circulating proteins that influence cancer risk and/or progression (i.e. spread or death). The specific objectives of this project are as follows:
i) Develop genetic instruments for circulating protein concentrations using recent GWAS to estimate the effect of these proteins on risk of overall and subtype-specific breast, colorectal, lung, ovarian, and prostate cancer.
ii) Examine the effect of circulating proteins on progression of breast, colorectal, lung, ovarian and prostate cancer.
iii) Examine agreement between the effect of circulating proteins on cancer risk compared with their effect on progression.
iv) Validate top findings from objectives i and ii using observational epidemiological analysis in UK Biobank or the UK wide Clinical Practice Research Datalink (where a protein discovered is also the drug target for an approved medication).
Genome-wide significant (P < 5 x 10-8) single-nucleotide polymorphisms (SNPs) associated with circulating protein concentrations will be used to develop genetic instruments for these proteins to estimate their causal effect on risk and progression of overall and subtype-specific breast, colorectal, lung, and prostate cancer using two-sample Mendelian randomization (1-5). Various sensitivity analyses will be performed to examine robustness of findings to violations of Mendelian randomization assumptions (e.g., colocalization analysis, heterogeneity testing, Steiger filtering, SloperHunter for cancer progression analyses). Concordance of effect estimates across cancer risk and progression analyses (corrected for potential index event bias) will be assessed. Top findings (i.e. those that are robust to multiple testing correction) will then be validated using observational epidemiological analysis within the UK Biobank cohort study or the Clinical Practice Research Datalink (CPRD).
This project will provide comprehensive and advanced training in genetic epidemiology and cancer, specifically Mendelian randomization applied to molecular traits and cancer biology, and experience writing up findings for publication and presentation. You will join a large cohort of fellow doctoral students and be part of a vibrant, intellectually generous and supportive Department.
1. Schmidt et al. Nature Communications, 2020. doi: 10.1038/s41467-020-16969-0
2. Folkersen et al. Nature Metabolism, 2020. doi: 10.1038/s42255-020-00287-2
3. Pietzner et al. Nature Communications, 2020. doi: 10.1038/s41467-020-19996-z
4. Gilly et al. Nature Communications, 2020. doi: 10.1038/s41467-020-20079-2
5. Sun et al. Nature, 2018. doi: 10.1038/s41586-018-0175-2
6. Zheng et al. Nature Genetics 2020. doi: 10.1038/s41588-020-0682-6
Dr Rachael Hughes (lead), Professor Kate Tilling, Dr Jon Heron Dr Jonathan Bartlett (Bath) Dr Rhian Daniel (Cardiff)
Most studies in medical research may suffer bias due to selection (participants are different to non-participants), or dropout (those who leave the study are different to those who remain). One way to investigate and address this bias is via inverse probability weighting (IPW). An important area of application is index event bias, a particular form of selection bias that occurs when examining risk factors for prognosis of a given disease. For example, IPW is of relevance in this context when studying the epidemiology of COVID-19. Currently, IPW has been less studied than other statistical approaches and there remain challenges in its application.
This project will use simulations (computer experiments where the truth is known) and applied examples (data from real-life studies) to develop IPW methods and software that will be of great relevance to many studies, especially much-used resources such as UK Biobank. First, we will investigate the best functional form of the weighting model used by IPW to generate the weights. Second, we will examine strategies for selection of variables for the weighting model and develop diagnostics for assessing the fit of the weighting model. Third, we will develop methods to carry out sensitivity analyses to examine robustness of results (e.g., sensitivity to the specification of the weighting model or stability of the weights).
Most studies in medical research may suffer bias due to selection (participants are different to non-participants), or dropout (those who leave the study are different to those who remain). One way to investigate and address this bias is via inverse probability weighting (IPW). Currently, IPW has been less studied than other statistical approaches and there remain challenges in its application.
This project will use simulations (computer experiments where the truth is known) and applied examples (data from real-life studies) to develop IPW methods and software that will be of great relevance to many studies, especially much-used resources such as UK Biobank. First, we will investigate the best functional form of the weighting model used by IPW to generate the weights. Second, we will examine strategies for selection of variables for the weighting model and develop diagnostics for assessing the fit of the weighting model. Third, we will develop methods to carry out sensitivity analyses to examine robustness of results (e.g., sensitivity to the specification of the weighting model or stability of the weights).
Dr Luisa Zuccolo (lead), Dr Carolina Borges, Dr Rachel Freathy
Breastfeeding is the biological norm and sustainable. It is also potentially life-saving, particularly for premature babies and those without access to clean water. Strategies to support breastfeeding have been successful, but inequalities persist and rates remain low in high-income countries such as the UK.
Although the short-term effects of breastfeeding are well documented, several questions about the epidemiology of breastfeeding remain unresolved, including which maternal and infant long-term outcomes are affected by different breastfeeding practices, and what the mechanisms behind these are.
This project will benefit mothers and babies by improving our understanding of practices and behaviours for optimal child development and long-term maternal health.
This project will improve our understanding of the effects (to the mother and the infant) of successfully establishing and sustaining breastfeeding.
Specific aims:
1. To identify genetic predictors of breastfeeding traits (to be used in genetic analyses to inform Aims 2. and 3.)
2. To estimate causal effects of breastfeeding on maternal and offspring health
3. To explore mechanisms for the long-term effects of breastfeeding
Breastfeeding traits include initiation, successful establishment, duration, exclusivity, breastfeeding problems.
This project aims to answer the above questions by combining cutting-edge methods that improve causal inference in observational studies, e.g. Mendelian Randomization and causal mediation, with classic epidemiological designs, e.g. cross-context comparisons. Results from each of these methods are likely to suffer from different biases, sometimes in opposite directions. We will exploit this using a triangulation approach, consistent results will provide stronger evidence for causality.
A key and novel component of the project will also be the identification of genetic predictors of breastfeeding traits through well-powered genome-wide association studies, to inform the Mendelian randomization analyses.
In order to fulfill the study’s objectives, an international network of collaborating cohorts has been established (N>120,000) to analyse existing data on breastfeeding traits, and putative determinants and consequences.
Victora CG et al. Lancet 2016; 387(10033): 474-90.
Dr Luisa Zuccolo (lead), Dr Gemma Sharp, Dr Doretta Caramaschi (Exeter) Dr Anita Thapar (Cardiff)
There is growing concern that paternal exposures before conception have been greatly neglected. Studying how these impact on future generations’ health could open new avenues for prevention-prospective fathers are not generally advised to change their behaviour. In animal studies, one of the paternal exposures showing the largest and most consistently reported effects relative to the prenatal period is alcohol, however convincing human evidence is lacking to date. In this project, we propose to investigate the relationship between pre-conception paternal alcohol use and offspring mental health, and in particular attention deficit hyperactivity disorder (ADHD).
1 Is paternal alcohol use before conception associated with offspring ADHD, and/or its brain morphology correlates?
2 Is this due to shared genetic influences?
3 Is this association robust to different causal inference methods of analyses such as Mendelian randomization, and the use of negative controls (eg non-biological fathers)?
4 Are offspring cord blood DNA methylation and ADHD structural brain correlates on the causal pathway from paternal alcohol exposure childhood ADHD?
To improve the chance of disentangling correlation from causation, we propose to use a paradigm similar to that of (lab studies of) rodent models of paternal effects: a pseudo-experimental study design (employing Mendelian randomization and other analytical approaches to improve causal inference), restricting to families with no intrauterine exposure to alcohol (like in the animal studies), focusing on early manifestation of the outcome occurring before the onset of own drinking to remove confounding by own drinking, and even earlier potential biological mediators of the effects (eg cord-blood DNA methylation, eliminating confounding by paternal drinking in the postnatal period, both set to ‘zero’ in animal studies).
Finegersh A, Alcohol 2015 49:461-70
Rogers JC, JAMA Psychiatry 2016 73:64-72
Chen YC, Mol Psychiatry 2017 Mar 21
Dr Eleanor Sanderson (lead), Dr Rhian Daniel, Professor Laura Howe
Many of an individual’s traits are observationally associated with their health outcomes. Understanding the relationships between these factors is critical to effective public health intervention. When multiple traits are potentially associated with a disease or health outcome, it is often not clear how much of the observed effect of each single trait is due to the effect of that trait or behaviour on other traits and behaviours, which then affect the outcome, and how much is “direct” in the sense that it is not mediated by the other traits being considered.
Causal mediation analysis is one approach that can be used to determine the proportion of the effect of a trait on an outcome that is via a mediating variable. However, this method relies on many strong assumptions. An alternative approach, relying on different assumptions, is Mendelian Randomisation (MR). MR is a method of instrumental variable analysis which utilises genetic variation between individuals to help understand causal effects.
The aim of this project is to conduct research on the strengths and limitations of MR when trying to understand the causal effects of multiple exposures on a health outcome. This will include investigation of how novel methods of MR analysis such as Multivariable MR relate to mediation analysis and developing and extend existing MR methods to deal with multiple mediators. This project will also explore possibilities of combining the two approaches.
In this PhD you will have the opportunity to work with leading researchers in the fields of population health, statistics, and Mendelian randomisation to further develop statistical methods for causal analysis based around MR mediation analysis.
This project will involve mathematical derivation of the properties of the extended methods and verification using simulation studies. This project will also involve analysis of both individual-level and summary-level data to apply the methods developed.
Although this project will be methodological in focus, the student will have the opportunity to develop a relevant application of these methods based on their personal research interests. Prospective applicants should have a strong quantitative background and an interest in developing methods for causal analysis within a population health setting; however, no particular background knowledge is required.
Dr Liam Mahedy (lead), Dr Emma Anderson, Dr Rebecca Richmond Prof. Marcus Munafò
Dementia is one of the leading causes of death globally and increasing longevity ensures its
prevalence will rise even further (1). As there is no known treatment that prevents the progression of dementia, there is an emphasis to focus on prevention as the best strategy to reduce incidence and prevalence. Since dementia has a long latency period, often up to 20 years before the symptoms are present (2), it is crucial to examine potential risk factors prior to this 'pre-clinical' dementia phase, as pathological accumulation could influence the risk factors. Although individuals with dementia experience sleep disruption (i.e., of duration, quality, and timing) (3), it remains unclear whether sleep disruption is also a risk factor for dementia. One of the main limitations in previous prospective cohort studies is that they do not consider the importance of sleep characteristics from early to midlife as they largely focus on adults aged >65 years at baseline and have relatively short follow-up periods (4,5). This area of research has received little attention from genetic epidemiology. A recent study (6) using a Mendelian randomisation approach found little evidence of a causal relationship between sleep traits and Alzheimer’s Disease; however, the authors recognised their study was underpowered.
The primary research question is to examine whether sleep disruption is a risk factor for dementia, and the alternative, whether dementia pathology and symptomology are a risk factors for sleep disruption. The secondary research question will examine whether critical or sensitive periods can be identified during which sleep disruption may have a greater impact on incident dementia and dementia progression, or vice versa.
This project will use secondary data sources from Dementias Platform UK (7), which contains rich questionnaire and clinic data alongside genetic information for almost 50 cohorts, comprising over 3 million individuals (e.g., the Avon Longitudinal Study of Parents and Children (Mother cohort) (8), and English Longitudinal Study of Aging) (9). Other potential data sources include The HUNT Study (10), and the International Genomics of Alzheimer’s Project (11). This project will use observational methods to examine change over time in cohort studies (i.e., latent growth modelling), and genetic methods to examine potential causal relationships (i.e., two sample Mendelian randomisation, and multivariable Mendelian randomisation).
1. Prince M et al. Alzheimer’s Dis Int. 2015;1–92.
2. Villemagne VL et al. Lancet Neurol. 2013;12,357–67.
3. Brzecka A et al. Front Neurosci. 2018;12:330.
4. Lutsey, PL et al. Alzheimers Dement. 2018;14,157–166.
5. Westwood, AJ et al. Neurology. 2017;88,1172–1179.
6. Anderson, EL et al. Int J Epidemiol. 2021;50,817-828.
7. Bauermeister, S et al. Eur J Epidemiol. 2020;35,601-611.
8. Fraser, A et al. Int J Epidemiol. 2013;42,97-110.
9. Steptoe, A et al. Int J Epidemiol. 2013;42,1640-1648.
10. Krokstad, S et al. Int J Epidemiol. 2013;42,968-977.
11. Lambert, JC et al. Nat.Genet. 2013;45,1452-U1206.
Dr Emma Vincent (lead), Dr Caroline Bull, Professor Nicholas Timpson
Growing evidence suggests that cell extrinsic factors are key in modulating tumor progression. Metabolites are small molecules that act as sources of fuel and building blocks essential for cells and tissues when present at normal levels. Many causal risk factors for cancer (such as obesity or smoking) perturb metabolite levels, meaning cells of the body are exposed to an abnormal metabolic environment in at-risk individuals. It is possible that metabolites may be involved in the causal mechanisms linking risk factors with cancer development, acting to favour the growth and survival of cancer-initiating cells.
This project will improve our understanding of the causal metabolic drivers of cancer development.
Specific aims:
1. To identify genetic predictors of cancer susceptibility and risk factors causally related to cancer
2. To estimate the causal effects of cancer susceptibility and cancer risk factors on circulating metabolites
3. To triangulate evidence to build knowledge of the causal mechanisms linking circulating metabolites with genetic susceptibility to cancer, risk factors for cancer and cancer development using epidemiological and preclinical techniques.
We will construct polygenic risk scores for cancer susceptibility and risk factors for cancer (tissues/specific risk factors will be determined by the student's interests) in mothers and children of the ALSPAC cohort to determine associations with circulating metabolites. We will use Mendelian randomization to estimate causal relationships between genetic susceptibility and risk factors for cancer with circulating metabolites and with cancer risk using summary statistics from large cancer consortia. There will also be the opportunity to work across research disciplines and to conduct mechanistic follow-up analyses in the laboratory using cell culture techniques.
Brennan, P & Davey Smith, G. Identifying Novel Causes of Cancers to Enhance Cancer Prevention: New Strategies are Needed. JNCI. https://doi.org/10.1093/jnci/djab204.
Martinez-Reyes, I & Chandel, N.S. Cancer metabolism: looking forward. Nature Metabolism. https://doi.org/10.1038/s42255-021-00478-5
Sarah Lewis (lead), Dr Kostas Tsilidis,
Diet and lifestyle are likely to play an important role in colorectal cancer risk; obesity, low levels of physical activity, red and process meat consumption and low intake of dietary fibre have all been shown to predict colorectal cancer risk. In addition, we have previously used Mendelian randomization (MR) analyses to identify physical activity (1) and iron, vitamin-B12 and selenium as potential risk factors for colorectal cancer risk (2). However, it is not clear which are the independent risk factors and which are confounded by other diet and lifestyle factors and the mechanisms via which these risk factors contribute to colorectal cancer are not well understood.
This project will; identify independent diet and lifestyle risk factors for colorectal cancer, integrate multi-omics data in a hypothesis free analysis to uncover novel risk factors and elucidate the biological mechanisms between modifiable risk factors and colorectal cancer.
This project will use many different MR methods, integrate omics datasets and perform mediation analyses to identify risk factors for colorectal cancer risk and elucidate the biological mechanisms involved in this disease.
Firstly the student will use a Bayesian framework (3), multivariable MR, co-localisation analyses and bi-directional MR methods incorporating instruments across many different potential risk factors in order to identify independent factors for colorectal cancer risk.
Once independent risk factors have been identified, the student will use two-step mendelian randomization to determine whether the risk factor-colorectal cancer link is mediated by any of the following four pathways; inflammation, growth hormones, sex hormones and insulin signalling.
Zuber V, Gill D, Ala-Korpela M, Langenberg C, Butterworth A, Bottolo L, Burgess S. High-throughput multivariable Mendelian randomization analysis prioritizes apolipoprotein B as key lipid risk factor for coronary artery disease. Int J Epidemiol. 2021 Jul 9;50(3):893-901. doi: 10.1093/ije/dyaa216. PMID: 33130851; PMCID: PMC8271202.
Tsilidis KK, Papadimitriou N, Dimou N et al. Genetically predicted circulating concentrations of micronutrients and risk of colorectal cancer among individuals of European descent: a Mendelian randomization study. Am J Clin Nutr. 2021 Jun 1;113(6):1490-1502. doi: 10.1093/ajcn/nqab003. Erratum in: Am J Clin Nutr. 2021 Jun 1;113(6):1715. PMID: 33740060; PMCID: PMC8168352.
Papadimitriou N, Dimou N, Tsilidis KK et al. Physical activity and risks of breast and colorectal cancer: a Mendelian randomisation analysis. Nat Commun. 2020 Jan 30;11(1):597. doi: 10.1038/s41467-020-14389-8. PMID: 32001714; PMCID: PMC6992637.
Dr Gemma Sharp (lead), Dr Hannah Jones, Dr Katherine Ruth, Genetics of Complex Traits, University of Exeter Medical School Dr Anna Murray, Genetics of Complex Traits, University of Exeter Medical School Dr Arianna Di Florio, Cardiff University
Problematic menstrual symptoms such as heavy menstrual bleeding (HMB) are common and distressing, impacting substantially on quality of life and mental wellbeing of affected women. As well as this likely effect of HMB on poor mental health (MH), there could also be an effect in the reverse direction: psychological stress is also a known disruptor of menstrual cycles and can be associated with heavier bleeding. Additionally, HMB is almost always self-reported and measured subjectively (with reduced quality of life now being part of the official diagnosis), so an association between HMB and MH might be explained by women with mood disorders being more likely to assess their level of bleeding as abnormal and substantially affecting their quality of life.
Both HMB and MH are influenced by inflammation. Increasing evidence suggests that inflammation plays a causal role in the pathogenesis of psychiatric disorders, including depression, and inflammation is also known to show cyclical variation, partly influenced by fluctuations in reproductive hormones throughout the menstrual cycle. There is also some evidence that systemic and local endometrial inflammation is associated with severity of menstrual symptoms like HMB.
This project aims to explore direct effects of HMB on MH, and of MH on HMB, as well as the potential confounding or mediating role of inflammation. A better understanding of these relationships would help inform ways to predict whether women with HMB are at greater risk of MH conditions, and whether women with MH conditions are at greater risk of menstrual issues like HMB. This would help clinicians tailor care to improve outcomes for groups of women. Understanding whether these associations are likely to be causal, and whether inflammation plays a role in the manifestation of both HMB and MH conditions will help shed light on the pathogenesis of HMB (and underlying pathologies) and MH and suggest whether both could be effectively treated using anti-inflammatory medications.
This project will use a genetic epidemiology approach to examine bi-directional associations between HMB and MH, as well as the role of inflammation in explaining any relationship. The student will first examine the association between HMB and MH phenotypes in UK Biobank and the Avon Longitudinal Study of Parents and Children (ALSPAC). Then, using results from our previous genomewide association study (GWAS) of HMB in UK Biobank, the student will generate polygenic risk scores (PRS) for HMB UKB and ALSPAC and conduct reduced-phenomewide association studies (PheWAS) to identify MH variables and inflammatory biomarkers associated with genetic liability to HMB. The student will also use LD score regression (LDSR) to explore the genetic correlation between all traits of interest using our HMB GWAS results and results from previously published GWAS of MH conditions and inflammatory biomarkers. Any associations from the pheWAS and LDSR will be followed up using Mendelian Randomization (MR) to explore if relationships are likely to be causal. The student will learn to apply 2 MR approaches: 1) 1-sample MR (this will allow us to test MR assumptions using individual-level data), 2) 2-sample MR using GWAS summary statistics (this will improve power above the one sample MR). Sensitivity analyses will allow the student to explore assumptions of both methods.
The student will also explore effects in the opposite direction by using results of previous GWAS of MH conditions and inflammatory biomarkers to generate PRS in ALSPAC and UK Biobank and test for associations with HMB, as well as in 1 and 2 sample MR analyses to explore causality.
The student will triangulate findings from across the PRS and MR analyses to inform network MR analysis including HMB, MH and inflammation to explore the strength and direction of direct and indirect and combined and independent causal effects.
Dr Evie Stergiakouli (lead), Prof Laura Howe, Dr Rachel Blakey, Christina Dardani
Neurodevelopmental and mental health problems in childhood have been linked to adverse physical health outcomes in both childhood and adult life. For example, genetic risk scores for ADHD have been associated with adverse lifestyle and physical health outcomes even in individuals from the general population who do not necessarily exhibit the disorder (1). Causal analyses have linked ADHD to childhood obesity and coronary artery disease (2). However, neurodevelopmental disorders and more specifically ADHD are also associated with adverse socioeconomic factors and impaired educational attainment (3).
In this project, we aim to investigate the effect of neurodevelopmental and mental health problems in childhood on child and adult physical health while taking into account the strong associations of neurodevelopmental problems and mental health with socioeconomic factors, education and parental psychopathology.
We will construct polygenic risk scores for childhood neurodevelopmental and mental health conditions in parents and children from the general population to investigate their associations with adult physical health and perform Polygenic Transmission Disequilibrium test (4) to asses genetic risk transmitted from parents to children. We will perform Multivariable Mendelian randomization (5) to disentangle the effects of genetic liability to neurodevelopmental problems on physical health accounting for socioeconomic factors. We will apply sensitivity analyses including weighted median, weighted mode, MR-Egger regression, MR-PRESSO and colocalization analyses as well as other causally informative designs to assess and adjust for pleiotropy. There is also the opportunity to contribute to large-scale meta-analyses including the Norwegian Mother, Father and Child Cohort Study (MoBa) and initiate collaborations with external researchers.
1) Leppert et al. Association of Maternal Neurodevelopmental Risk Alleles With Early-Life Exposures. JAMA Psychiatry 2019;76(8):834–842. doi:10.1001/jamapsychiatry.2019.0774
2) Leppert et al. The Effect of Attention Deficit/Hyperactivity Disorder on Physical Health Outcomes: A 2-Sample Mendelian Randomization Study, AJE 2021; 190 (6), 1047–1055, https://doi.org/10.1093/aje/kwaa273
3) Dardani et al. Is genetic liability to ADHD and ASD causally linked to educational attainment?, IJE 2021;, dyab107, https://doi.org/10.1093/ije/dyab107
4) Weiner et al. Polygenic transmission disequilibrium confirms that common and rare variation act additively to create risk for autism spectrum disorders. Nat. Genet. 2017 49, 978
5) Sanderson et al. An examination of multivariable Mendelian randomization in the single-sample and two-sample summary data settings. IJE 2018; 1;48(3):713-727. doi: 10.1093/ije/dyy262.
Dr Amanda Hughes (lead), Prof Laura Howe, Dr Helen Bould
Internalized weight stigma, the belief that negative obesity-related stereotypes apply to oneself(1), is associated with poorer health(2). Among higher-weight individuals, it is linked to disordered eating(3) maladaptive coping(4), and worse quality of life(5). In normal-weight and underweight individuals, it predicts disordered eating and drive for thinness(6,7), and is thus relevant to mental health across the body weight range. However, most research on internalized weight stigma has been based on small, non-representative samples (for example recruited from weight loss groups, or undergraduate psychology students). Consequently, very little is known about risk factors for weight stigma internalization, or its consequences for health, in the general population. Advancing current understanding of these relationships would help identify groups especially at risk of internalizing weight stigma, and would help inform policy interventions to mitigate the effects of weight stigma.
This project will use new data from a general population birth cohort survey (the Avon Longitudinal Study of Parents and Children, ALSPAC) to:
1. explore life-course and intergenerational predictors of internalized weight stigma in young adulthood
2. explore health and social consequences of internalized weight stigma in young adulthood
Methods such as multivariable regression and growth curve modelling will be used to explore associations with internalized weight stigma at age 31, measured using the Modified Weight Bias Internalization Scale (8). This scale is suitable for people across the bodyweight range and will be completed by all participants as part of a wider questionnaire in 2022.
Possible risk and protective factors could include socioeconomic factors, gender, trajectories of body weight through childhood and adolescence, current and former mental health, previous eating behaviours and eating disorder symptoms, and intergenerational factors (e.g. parents’ weight, body image and weight-related attitudes).
Possible consequences of internalized weight stigma could include aspects of physical and mental health such as body weight, depressive symptoms, and overall self-rated health.
1. Durso LE, Latner JD. Obesity. 2008. 2. Pearl RL, Puhl RM. Obesity Reviews. 2018. 3. O’Brien KS et al. Appetite. 2016. 4. Hayward LE et al. Obesity. 2018. 5. Latner JD et. al. Eating Behaviors. 2014. 6. Marshall RD et al. Frontiers in Psychology. 2020. 7. Schvey NA, White MA. Eating Behaviors. 2015. 8. Pearl RL, Puhl RM. Body image. 2014.
Dr Eleanor Sanderson (lead), Professor Kate Tilling, Professor George Davey Smith
Mendelian randomization (MR) uses genetic variants as instrumental variables to estimate the causal effect of an exposure on an outcome, free from bias due to unobserved confounding. E.g. the effect of BMI on cancer incidence.
Multivariable MR (MVMR) is an extension of MR that jointly estimates the causal effect of multiple exposures on an outcome. MVMR can be used to adjust for pleiotropic effects, where genetic variants are associated with multiple traits biasing MR estimates.
Many exposures, such as BMI, vary across an individual’s lifetime, however genetic variants are fixed. Recent research has focused on (1) how MR estimates should be interpreted when the exposure varies across the lifecourse (2) the degree to which it is possible to separate the causal effect of the same exposure at different stages of the lifecourse. Understanding the effect of an exposure across the lifecourse would identify windows for prevention or treatment, and shed light on the aetiology of disease.
The aim of this project is to conduct research on the estimation and interpretation of different MR methods with time varying exposures. There will be a particular focus on the estimation of the causal effects of multiple exposures that vary over time through MVMR. MVMR can be used to estimate the proportion of an effect that is mediated through other exposures and this project will also consider how MVMR for mediation can be interpreted when both the exposure and mediators vary over time.
The focus of this project is on developing and applying methods for MVMR. The project will use simulation analysis to understand the methods and interpretation considered. This project will also involve the analysis of individual and summary level data for MR and MVMR estimation to illustrate the results obtained.
The project is methodological in focus, and the student will have the opportunity to develop an application of the methods considered. This could be on a topic that is relevant to their own applied research interests, or can be developed in consultation with the supervisors.
Dr Duleeka Knipe (lead), TBC,
Suicide bereavement is a strong predictor of suicidal behaviour, with evidence that paternal suicide (especially maternal) increases risk of suicide and self-harm behaviour in offspring. There is evidence that suggests that the time since suicide bereavement also impacts on suicide and self-harming behaviour in the individual bereaved.
This evidence primarily originates from high income countries. Roughly 80% of all suicide and self-harm occurs in low- and middle-income countries (LMICs), but only 15% of research evidence originates from these settings. Given the difference in suicide and self-harm rates between countries, and the important contextual and cultural differences (including responses to suicidal behaviour), research evidence generated from high income countries may not be applicable in LMICs . Additionally in contexts, like Sri Lanka, where extended family networks are stronger, the impact of suicidal behaviour in other members of family (and community) may increase suicide or self-harm risk in the individual exposed to that behaviour.
This project will investigate the influence of exposure to self-harm behaviour and suicide in others (family and community) on subsequent suicide and self-harm risk using an established cohort dataset in Sri Lanka.
It will answer the following questions:
1) What is the risk of suicide and self-harm in individuals who are exposed to these behaviours in family and community members?
2) Does the risk vary by the sex of the individual, the kinship relationship, and the timing of the exposure (especially in young people)?
The project involves statistical analysis of data from the locked boxed trial in Sri Lanka. This will involve developing skills in survival and spatial analysis, as well as logistic regression techniques.
Pearson M, Metcalfe C, Jayamanne S, et al. Effectiveness of household lockable pesticide storage to reduce pesticide self-poisoning in rural Asia: a community-based, cluster-randomised controlled trial. Lancet 2017
Pitman A, Osborn D, King M, Erlangsen A. Effects of suicide bereavement on mental health and suicide risk. Lancet Psychiatry 2014; 1(1): 86-94.
Professor Alastair Hay (lead), Dr Ashley Hammond,
Acute otitis media (AOM) is a common, painful condition of childhood, often treated inappropriately with antibiotics.
Optical coherence tomography (OCT), considered the optical analogue of ultrasound imaging, uses a low-intensity light source to produce real-time structural images with micron-scale resolution. This technology is coupled with high-resolution digital otoscopy imaging in the OtoSightTM Middle Ear Scope. The novel OCT images produced by the reflected near-infrared light are analysed and can be used to objectively differentiate air from middle ear fluid, as well as characterize fluid properties due to scattering of the imaging signal from particulates in the fluid. Its use within this feasibility study has two purposes: (i) confirmation of the presence of middle ear fluid (and therefore the AOM diagnosis); and (ii) daily serial imaging to visualize and chronicle the natural history of middle ear fluid resolution in a primary care population of children with suspected AOM. The latter is of particular interest because it could help understand whether the mechanism by which anaesthetic/glycerol drops work is to reduce the quantity, and therefore pressure exerted by, middle ear fluid.
Serial daily microbiological evaluation also has two purposes: (i) recording the possible cause of the infection; and (ii) describing the quantitative natural history of upper respiratory tract microbiota in a primary care population of children with diagnosed AOM.
The aim of this PhD is to establish the feasibility and acceptability of daily OCT-otoscopy and microbiology in children who have presented to primary care with suspected AOM. Specifically, the objectives are:
1. Systematically review the literature for evidence regarding the use if serial OCT and microbiology
2. Conduct a prospective cohort study to
2.1 To establish parental acceptability of the study design (consent rate)
2.2 To record child and parental acceptability of daily serial middle ear fluid measures (OCT-otoscopy) and microbiology (nasal swabs)
2.3 To explore the association between quantitative OCT image signal changes for any middle ear contents present and ear pain between presentation and symptom resolution
2.4 To explore quantitative changes in bacterial/viral load and ear pain between presentation and symptom resolution.
Using methods employed successfully in the past, we propose to assemble a cohort of children with AOM who have recently presented to primary care.
For the mixed methods study, recruitment and follow up will be according to the following steps:
1. GP/practice nurse requests parental permission to notify study centre of potentially eligible children (following face-to-face or ‘COVID secure’ telephone assessment)
2. Student contacts parent to confirm interest in, and eligibility for, the study
3. E-consent taken
4. Student visits parent/child (at place of parent’s choosing – usually the family home)
5. Student confirms consent and willingness of parent to receive daily visits
6. Student conducts bilateral OCT-otoscopy and takes nasal swab, and advises parent in the use of the daily symptom diary (validated, and as used in previous studies)
7. Student visits daily until symptom resolution (usually no more than eight days)
8. Student administers brief questionnaire investigating parental acceptability of study procedures.
1. Venekamp RP, Schilder AGM, van den Heuvel M, Hay AD. Acute otitis media in children. BMJ. 2020;371:m4238.
2. Hay AD, Downing H, Francis NA, Young GJ, Clement C, Harris SD, et al. Anaesthetic–analgesic ear drops to reduce antibiotic consumption in children with acute otitis media: the CEDAR RCT. Health Technology Assessment. 2019;23:34.
3. Preciado D, Nolan RM, Joshi R, Krakovsky GM, Zhang A, Pudik NA, et al. Otitis Media Middle Ear Effusion Identification and Characterization Using an Optical Coherence Tomography Otoscope. Otolaryngol Head Neck Surg. 2020;162(3):367-74.
4. Monroy GL, Pande P, Shelton RL, Nolan RM, Spillman DR, Porter RG, et al. Non-invasive optical assessment of viscosity of middle ear effusions in otitis media. J Biophotonics. 2017;10(3):394-403.
5. Monroy GL, Shelton RL, Nolan RM, Nguyen CT, Novak MA, Hill MC, et al. Noninvasive depth-resolved optical measurements of the tympanic membrane and middle ear for differentiating otitis media. Laryngoscope. 2015;125(8):E276-82.
6. Hay AD, Redmond NM, Turnbull S, Christensen H, Thornton H, Little P, et al. Development and internal validation of a clinical rule to improve antibiotic use in children presenting to primary care with acute respiratory tract infection and cough: a prognostic cohort study. Lancet Respir Med. 2016;4:902–10.
7. Hay AD, Sterne JA, Hood K, Little P, Delaney B, Hollingworth W, et al. Improving the Diagnosis and Treatment of Urinary Tract Infection in Young Children in Primary Care: Results from the DUTY Prospective Diagnostic Cohort Study. Ann Fam Med. 2016;14(4):325-36.
8. Watson L, Little P, Moore M, Warner G, Williamson I. Validation study of a diary for use in acute lower respiratory tract infection. Family Practice. 2001;18(0263-2136):553-4.
9. Thompson M, Vodicka T, Cohen H, Blair P, Biuckley T, Heneghan C, et al. Duration of symptoms of respiratory tract infections in children: systematic review. British Medical Journal. 2013.
Prof Laura Howe (lead), Prof Abigail Fraser, Prof Alun Hughes, UCL
The response to exercise is an integrative measure of circulatory function and predicts future cardiovascular disease. Lifetime maximum aerobic capacity (VO2peak) is established by age ~30 and subsequently declines. The determinants of peak circulatory capacity are not well understood. This PhD will investigate key likely influences on peak adult circulatory capacity: adiposity/physical inactivity, and adversity/disadvantage. Measurements of Avon Longitudinal Study of Parents and Children (ALSPAC) participants at age 30 are currently underway, including assessing VO2peak using cycle-ergometry/gas- exchange, concurrent cardiac output by inert gas-rebreathing, and skeletal muscle microvascular function using near-infrared spectroscopy. The availability of multiple repeated measurements means that outcomes can be related to key antecedent exposures over the whole of fetal, child and early adult life. This will provide important insights into improvement of future cardiovascular health.
The aims of this PhD are:
1. To assess the association of life course trajectories of adiposity and physical (in)activity with VO2peak.
2. To assess socioeconomic inequalities in VO2peak, and the degree to which these are explained by adiposity and physical (in)activity.
3. To assess the association between adverse childhood experiences and VO2peak, and the degree to which these are explained by adiposity and physical (in)activity.
In this PhD, you will draw on the detailed longitudinal data available in ALSPAC, to look at the life course determinants of VO2peak. In particular, you will draw on repeated measures of adiposity (including BMI and DXA-determined fat mass) and physical (in)activity (including accelerometry-based measured), and detailed data from two generations on socioeconomic position (occupation, education, financial difficulties) and adversity (child maltreatment and measures of family dysfunction).
You will have the opportunity to develop skills in advanced statistical methodology, including multilevel models and structural equation models to estimate trajectories, techniques for mediation analysis, and methods for dealing with missing data. You will join a supportive and collaborative research team, in the vibrant environment of a leading centre for epidemiology, and will have the opportunity to lead on publishing the results of your research.
Dr Lavinia Paternoster (lead), Prof George Davey Smith, Dr April Hartley
Typical epidemiological studies aim to identify causal risk factors for onset of disease by comparing diseased cases with disease free controls. However, if the aim is to identify effective treatments for use after the onset of disease, then factors that explain why some patients progress through disease stages quickly whilst others recover or progress more slowly may be the more pertinent comparison.
To identify genetic and non-genetic causal risk factors for disease progression that may be useful for identification of new treatments
You will use existing in-house datasets for a disease of your interest.
You will use state-of-the-art methods developed in the Integrative Epidemiology Unit to identify factors associated with progression of disease, taking account of collider/selection bias that can be introduced in such analyses.
You will use Mendelian Randomization to determine if relationships observed are causal.
doi.org/10.1371/journal.pgen.1006944
Prof Sarah Lewis (lead), Dr Miguel Renteria, Dr Hannah Jones
Sleep has long been known to play a role in physical and mental wellbeing and sleep disruption has been observed to co-occur with a number of health conditions including: depression, chronic pain, diabetes, chronic kidney disease, ADHD, impulsivity, anxiety, and bipolar disorder (1). Recent large genome wide association studies have uncovered genetic correlations between sleep traits and health conditions. For instance, there is evidence of genetic correlation at the whole-genome level between snoring and schizophrenia( 2), and between sleep apnea and and self-harm. However, the precise molecular pathways, causal relationships and mechanisms are not clear. Does sleep disturbance cause health problems or do health problems lead to disrupted sleep or are the two linked by shared biological pathways early in development?
Mendelian randomization and state-of-the-art statistical genetics methods will be used to disentangle the genetic relationships between these complex traits.
This project will investigate the causal pathways between sleep traits and physical and mental health using publicly available genome wide association study data, in order to identify risk factors for sleep apnea and downstream effects of sleep apnea on health and wellbeing.
During this project the student will:
1. Conduct a systematic review of the published literature to determine the strength of the evidence for the association between sleep and mental health traits of interest.
2. Refine genetic instruments for snoring, sleep apnea and mental health traits.
3. Conduct Mendelian randomization analyses of the effect of snoring and sleep apnea on a series of mental health outcomes.
4. Conduct sensitivity analyses to test whether the results could be influenced by the underlying assumptions of Mendelian randomization analyses.
5. Use analytical methods such as multi-trait analysis of genome wide association studies (GWAS), genomic structural equation modelling or co-localisation analysis and functional annotation to characterize the shared molecular pathways between sleep and physical and mental health problems.
1. Tarokh L et al. Sleep in adolescence: Physiology, cognition and mental health. Neurosci Biobehav Rev. 2016;70:182-188.
2. Campos AI et al. Insights into the aetiology of snoring from observational and genetic investigations in the UK Biobank. Nat Commun. 2020 Feb 14;11(1):817.
3. Davey Smith G, Ebrahim S. 'Mendelian randomization': can genetic epidemiology contribute to understanding environmental determinants of disease? Int J Epidemiol. 2003;32:1-22.
Professor Sarah Lewis (lead), Dr Evie Stergiakouli, Professor Heather Cordell (University of Newcastle).
Orofacial clefts (OFCs) are the most prevalent craniofacial congenital disabilities globally, occurring at a frequency of 1 out of 700 newborns. Children born with a cleft usually require more than one surgery and ongoing support for speech, psychological wellbeing and educational need. Thus individuals and families affected by OFCs suffer from a significant burden on physical and psychological health, socioeconomic well-being, and quality of life).
Generally, OFCs can be divided into two main types with different etiologies, cleft palate only (CPO) and cleft lip or without cleft palate (CL/P). Most OFCs are isolated non-syndromic (nsOFCs), while OFCs with other clinical signs are classified as syndromic. The syndromic form is often related to genetic mutations and chromosomal aberrations, but the occurrence of nsOFCs is complex and involves genetic susceptibility, environmental factors (e.g., smoking, alcohol drinking and dietary factors), and gene-environment interactions. Thus, a better understanding of the genetic architecture of nsOFCs may contribute to identifying populations at high risk, providing prevention strategies, and reducing the incidence of the anomaly. Previous genome-wide association studies (GWAS) have identified over 40 loci associated with nsOFCs risk(1), but most studies have focused on the overall nsCL/P and have not had sufficient power to study subtypes. In addition previous studies have not been able to consider the contribution of parental genotypes to cleft and the interaction between child and parent genotypes.
To carry-out trio analysis of genome wide association study data to investigate the genetic architecture of nsOFCs of European ancestry and identify genetic variants that are associated with its subtypes, CLO, CPO, CLP.
To study the contribution of parental genotype to cleft, to determine whether interactions between parent and child genotypes contribute to cleft and to investigate parent of origin effects.
The current study will conduct a genome wide association study using already genotyped data from case-parent trios of European ancestry recruited from the Cleft Collective and the Bonn Cohort using a transmission disequilibrium test design (2) to identify the new common variants that are associated with nsOFCs and its subtypes. The study will carry-out statistical analyses using the trio data to determine whether interactions between parent and child genotypes contribution to cleft phenotypes and investigate parent of origin effects for those genetic variants which are implicated as risk factors.
1. Yu Y, Zuo X, He M, Gao J, Fu Y, Qin C, Meng L, Wang W, Song Y, Cheng Y, et al. Genome-wide analyses of non-syndromic cleft lip with palate identify 14 novel loci and genetic heterogeneity. Nat Commun 2017;8:14364. doi: 10.1038/ncomms14364.
2. Ruiz-Narvaez EA, Campos H. Transmission disequilibrium test (TDT) for case-control studies. Eur J Hum Genet 2004;12(2):105-14. doi: 10.1038/sj.ejhg.5201099
Dr. Paul Yousefi (lead), Professor. Richard Martin, Dr. Matthew Suderman Dr. Athene Lane Dr. Sam Merriel
Prostate Cancer (PCa) is a leading cause of male mortality, with 336,000 deaths worldwide each year (1). Although most PCa cases are indolent, slow-growing, and tend not to progress, a subset of PCa cases are more aggressive and will progress to metastases, treatment resistance and death. Aggressive cases are a major driver of PCa mortality, which is the second most frequent cause of UK male cancer deaths (2). Currently, performance of even NICE recommended post-diagnostic clinical prediction models of PCa progression (based on clinical variables and prostate biopsy) is limited, with C-index at 10 years ranging from 0.73 to 0.81. Tools that improve discrimination of PCa progression and reduce harm due to both under- and over-treatment are required to guide treatment clinically.
DNA methylation (DNAm) is an epigenetic modification that regulates tissue-specific gene expression and is disrupted in cancer development where it’s a hallmark of oncogenesis and progression pathophysiology (3). Attempts to identify DNAm differences in PCa have been underpowered and featured substantial design flaws (e.g. insufficient treatment adjustment, inappropriate follow-up window, etc.). However, blood DNAm is increasingly being appreciated as valuable for predicting cancer progression and prognosis (4), which merits further exploration with adequate sample size and sufficient methodology.
Given the role of DNAm in tumorigenesis, the potential for DNAm to capture signal of early disease progression, and the poor performance of existing biomarkers, this project aims to improve discrimination of the rate and severity of PCa progression by:
(1) Identifying genome-wide blood DNAm patterns that differ prospectively between aggressive and indolent forms of PCa
(2) Developing a DNAm signature using machine learning techniques for predicting aggressive PCa among patients with confirmed localised disease that could inform the need for radical therapeutic intervention
(3) Evaluating whether such a DNAm signature can improve upon existing NICE-recommended clinical progression methods
This project will use participants from the Prostate Testing for Cancer and Treatment (ProtecT) trial which included 1,600 UK men with confirmed, localised PCa and prospectively evaluated two forms of radical PCa treatment (prostatectomy and radiotherapy) to active monitoring. Median follow-up was 10-years and PCa mortality was the primary outcome. Using DNA isolated from whole blood samples collected at baseline, genome-wide DNAm levels will be quantified by Illumina HumanMethylationEPIC BeadChip for N = 850 (125 aggressive cases, 725 indolent controls) age and treatment matched ProtecT participants.
An Epigenome Wide Association Study (EWAS) will be performed to identify where DNAm is differentially methylated between aggressive and indolent PCa cases at over >850k CpG sites measured on the Illumina HumanMethylationEPIC BeadChip. Analysis for identification of differentially methylated regions (DMRs) will also be performed. To determine the CpGs and CpG combinations most predictive of PCa progression, we will apply feature selection and engineering approaches, and a library of pre-specified supervised machine learning methods (e.g. elastic-net regression, tree-ensembles, etc.). All models will be evaluated by outcome-stratified repeated k-fold cross-validation to robustly assess out-of-sample predictive performance and tune relevant hyperparameters. C-statistic/AUC will be the primary performance metric, but several alternatives will be considered.
1. Pernar CH, Ebot EM, Wilson KM, Mucci LA. The Epidemiology of Prostate Cancer. Cold Spring Harb Perspect Med TA - TT -. 2018;8(12):a030361.
2. Prostate Cancer incidence statistics: Cancer Research UK (CRUK) [Internet]. 2017. Available from: https://www.cancerresearchuk.org/health-professional/cancer-statistics/statistics-by-cancer-type/prostate-cancer/incidence
3. Zhang J, Huang K. Pan-cancer analysis of frequent DNA co-methylation patterns reveals consistent epigenetic landscape changes in multiple cancers. BMC Genomics. 2017. Available from: https://pubmed.ncbi.nlm.nih.gov/28198667/
4. Yousefi PD, Suderman M, Langdon R, Whitehurst O, Davey Smith G, Relton CL. DNA methylation-based predictors of health: applications and statistical considerations. Nat Rev Genet. 2022 Mar 18;1–15.
Dr Ashley Budu-Aggrey (lead), Dr Lavinia Paternoster,
Several traits such as cardiovascular disease[1] and mental health disorders[2] have been reported to be observationally associated with atopic dermatitis (AD). The most recent genome-wide association study for AD has also uncovered genetic correlations[3]. However, the causal relationships and direction of effect is yet to be determined. Establishing causality will aid the early detection of AD or later health outcomes and determine whether intervention on one condition will affect the other.
To investigate causal risk factors and outcomes of AD using Mendelian Randomization (MR)
1. The literature will be screened to identify hypotheses for examination with MR.
2. Observational analysis will be performed using datasets available in-house such as the UK Biobank
3. Suitable datasets will be identified, and genetic instruments for AD and other traits will be refined
4. 2 sample MR analyses will be performed to investigate causal relationships
5. Sensitivity analyses will be performed to ensure the assumptions of the MR analyses have not been violated
1. Standl, M. et al. Association of Atopic Dermatitis with Cardiovascular Risk Factors and Diseases. Journal of Investigative Dermatology 137, 1074–1081 (2017).
2. Budu-Aggrey, A. et al. Investigating the causal relationship between allergic disease and mental health. Clin Exp Allergy 51, 1449–1458 (2021).
3. Budu-Aggrey, A. et al. European and multi-ancestry genome-wide association meta-analysis of atopic dermatitis highlights importance of systemic immune regulation. Submitted to Nature Communications (2022).
Prof Julian Higgins (lead), Prof Kate Tilling, Prof Marcus Munafò
Triangulation, in which multiple methods are strategically used to answer a single question, is a currently developing area. Lawlor, Tilling and Davey Smith (2016) explained how causal inferences can be strengthened by integrating results from several approaches with different key sources of potential bias. The statistical methods for combining the results from multiple sources of evidence within a triangulation framework are, however, underdeveloped. This PhD seeks to develop, illustrate and evaluate such methods.
The project seeks to develop and implement quantitative methods for triangulation of multiple lines of evidence addressing the same underlying epidemiological question
Work is expected to focus on three key areas as follows.
1) At its simplest, triangulation involves comparison and combination of studies of the same exposure-outcome effect that use different designs or analytic methods. For example, randomized trials, Mendelian randomization studies and traditional multivariable regression analyses of observational evidence might all tackle a question relating to the same exposure-outcome effect. The studies may produce different effect estimates because they are (i) asking subtly different questions (e.g. in relation to the period or patterns of exposure), (ii) compromised by different biases and/or (iii) subject to chance. Triangulation combines these issues in a statistical model and assesses the extent to which the observed data fit together – an approach known as multiparameter evidence synthesis. Methods for producing these models, assessing coherence and drawing conclusions about causal effects of the exposure on the outcome will be developed. The project will primarily explore Bayesian methods, because they are flexible and allow incorporation of external information through prior distributions.
2) Another form of triangulation arises when some (or all) studies address only a component of the underlying question. For example, if the exposure-outcome effect occurs through an intermediate, then studies of the exposure-outcome effect might be triangulated with a combination of studies (i) of the effect of exposure on the intermediate and (ii) of the effect of the intermediate on the outcome. Methods will be developed to synthesise these three sets of studies, and account for true differences, biases and chance.
3) In addition to working on novel statistical methods, the student may explore other methodological questions. First, how should we define and identify studies suitable for a triangulation exercise? Automation tools may help here, such as MELODI (http://melodi.biocompute.org.uk), which we have developed to identify studies examining intermediates between exposure and outcome. Second, how should we evaluate the risk of bias in studies for which formal frameworks (such as RoB 2 and ROBINS-I; http://riskofbias.info) have not been developed? Third, what sources of information are available about biases, to inform prior distributions, and how can more information be generated?
Methods developed in these three areas will be illustrated through application to important causal questions in epidemiology.
Lawlor DA, Tilling K, Davey Smith G. Triangulation in aetiological epidemiology. Int J Epidemiol. 2016 Dec 1;45(6):1866-1886. doi: 10.1093/ije/dyw314.
Munafò MR, Davey Smith G. Robust research needs many lines of evidence. Nature. 2018 Jan;553(7689):399-401. doi: 10.1038/d41586-018-01023-3.
Munafò MR, Higgins JPT, Davey Smith G. Triangulating Evidence through the Inclusion of Genetically Informed Designs. Cold Spring Harb Perspect Med. 2021 Aug 2;11(8):a040659. doi: 10.1101/cshperspect.a040659.
Turner RM, Spiegelhalter DJ, Smith GC, Thompson SG. Bias modelling in evidence synthesis. J R Stat Soc Ser A Stat Soc. 2009 Jan;172(1):21-47. doi: 10.1111/j.1467-985X.2008.00547.x.
Ades AE, Welton NJ, Caldwell D, Price M, Goubar A, Lu G. Multiparameter evidence synthesis in epidemiology and medical decision-making. J Health Serv Res Policy. 2008 Oct;13 Suppl 3:12-22. doi: 10.1258/jhsrp.2008.008020.
Dr Evie Stergiakouli (lead), Prof Laura Howe, Alexandra Havdhal, University of Oslo Department of Psychology
Attention Deficit Hyperactivity Disorder (ADHD) is a chronic neurodevelopmental condition, characterised by persistent difficulties in the areas of attention span/impulse control. Approximately 65% of children diagnosed with ADHD have symptoms and impairment that persist into adulthood and ADHD can lead to educational, social, and occupational difficulties (1).
We have previously shown that higher genetic risk for ADHD is associated with younger maternal age at birth, lower educational attainment and other indicators of social disadvantage in mothers from the general population (2). Using Mendelian randomization (MR) we have also found evidence of higher genetic liability to ADHD causing lower educational attainment, and evidence of genetic liability to lower educational attainment increasing risk to ADHD independent of cognitive ability (3). Since ADHD manifests at a very young age, the causal effects of genetic liability to education on ADHD are likely to indicate parental effects. The causal link between parental education and ADHD could be mediated by optimal lifestyle and general health factors during pregnancy and/or socioeconomic factors linked to better access to educational resources. Disentangling the individual effects of each factor as well as assessing for genetic confounding is required.
In this project, we will explore the links between educational attainment, reproductive outcomes (age at first birth, number of live births), prenatal factors, socioeconomic status and other indicators of social disadvantage on ADHD.
Our aims are: 1. To assess the contribution of parental educational attainment on trajectories of ADHD traits in offspring and 2. To disentangle it from the offspring own genetic background and investigate the causal pathways linking parental educational attainment and offspring ADHD.
This is an exciting opportunity for a student to perform advanced genetic epidemiological analyses on large multigenerational longitudinal cohorts from two countries: the Avon Longitudinal Study of Parents and Children in the UK and the Norwegian Mother and Child Cohort Study (MoBa) in Norway.
For aim 1, we will compare the associations of maternal and paternal genetic liability of educational attainment on ADHD trajectories from two general population samples with very different educational systems and social structures. Polygenic Transmission Disequilibrium tests will be used to assess if genetic liability to lower educational attainment is overtransmitted to offspring with ADHD (4).
For aim 2, we will use within-families MR (5) in MoBa to investigate causal effects of the offspring’s own genetic liability to education attainment while adjusting for parental genetic liability. We will also perform Multivariable Mendelian randomization (6) to account for multiple exposures (educational attainment, reproductive outcomes, prenatal factors, socioeconomic status) simultaneously. Finally, we will apply sensitivity analyses including weighted median, weighted mode, MR-Egger regression, MR-PRESSO and colocalization analyses to assess and adjust for pleiotropy.
Thapar A, Cooper M. Attention deficit hyperactivity disorder. Lancet. 2016 Mar 19;387(10024):1240-50. doi: 10.1016/S0140-6736(15)00238-X.
Leppert B et al. Association of Maternal Neurodevelopmental Risk Alleles With Early-Life Exposures. JAMA Psychiatry. 2019;76(8):834–842. doi:10.1001/jamapsychiatry.2019.0774
Dardani et al. Is genetic liability to ADHD and ASD causally linked to educational attainment?, Int. J. Epidemiol. 2021;, dyab107, https://doi.org/10.1093/ije/dyab107
Weiner D et al. Polygenic transmission disequilibrium confirms that common and rare variation act additively to create risk for autism spectrum disorders. Nat Genet 49, 978–985 (2017). https://doi.org/10.1038/ng.3863
Brumpton, B et al. Avoiding dynastic, assortative mating, and population stratification biases in Mendelian randomization through within-family analyses. Nat Commun 11, 3519 (2020). https://doi.org/10.1038/s41467-020-17117-4
Sanderson E et al. An examination of multivariable Mendelian randomization in the single-sample and two-sample summary data settings. Int J Epidemiol. 2019 Jun 1;48(3):713-727. doi: 10.1093/ije/dyy262.
Dr Kaitlin Wade (lead), Professor Golam Khandaker, Dr Christina Dardani
Low-grade systemic inflammation is associated with risk of neuropsychiatric conditions such as depression, schizophrenia, autism, with emerging genetic evidence suggesting a potentially causal role of inflammatory cytokines such as interleukin 6 (IL-6) in these conditions (1, 2). Gut microbiome alterations are thought to be an important source of systemic low-grade inflammation, and disruptions in the “gut-brain axis” are implicated in neurodevelopment, cognitive function, and risk of neuropsychiatric conditions (3). However, it is unclear whether gut microbiome alterations play a causal role in neuropsychiatric conditions, and whether immune activation represents a potential mechanism through which gut microbiome alterations influence neuropsychiatric risk. Uncovering causal pathways between the gut microbiome, immune response and neuropsychiatric conditions may offer unique insights into novel intervention targets.
1. Summarize current evidence on the associations of the gut microbiome and neuropsychiatric conditions (depression, schizophrenia, and autism).
2. Examine evidence for causality in these associations.
3. Assess whether inflammatory cytokines represent a potential mechanism through which gut microbiome alterations influence neuropsychiatric conditions.
4. Assess the predictive utility of the gut microbiome (directly or through genome-wide predictors) and neuropsychiatric conditions.
This PhD will provide training in the following important research methods:
1. Systematic review and meta-analysis of studies on the gut microbiome and neuropsychiatric conditions (depression, schizophrenia, and autism).
2. Multivariable linear/logistic regressions assessing these associations in available individual-level data (e.g., Flemish Gut Flora Project and those in the MiBioGen consortium) (4, 5).
3. Bi-directional Mendelian randomization analyses testing potential causality (and direction) in relationships between gut microbiota and neuropsychiatric conditions (6).
4. Mediation analysis to test whether inflammation (e.g., levels/activity of immune proteins such as C-reactive protein [CRP], IL-6, tumour necrosis factor alpha [TNF-a]), mediate the association between gut microbiome alterations and neuropsychiatric conditions.
5. Prediction models of the association between gut microbiota and neuropsychiatric conditions, outside causal framework analyses.
1. Khandaker et al. JAMA Psychiatry 2014;71:1121.
2. Perry et al. Brain Behaviour and Immunity 2021;97:176.
3. Carabotti et al. Ann Gastroenterol. 2015;28:203.
4. Hughes et al. Nat Microbiol. 2020;5:1079.
5. Kurilshikov et al. Nature Genetics 2021;53:156.
6. Wade and Hall. Wellcome Open Res 2019;4:199
Professor Nicholas Timpson (lead), Professor Paul Martin,
Repair of adult tissues involves a complex interplay of several key cell lineages and inevitably leads to formation of a fibrotic collagenous scar. We use human phenotypic variation to examine genetic correlates of scarring and to better understand biological contributions to wound healing. As an introduction into using population approaches to identify scar-associated genes, we have already assessed a sample of mothers from the Bristol-based Avon Longitudinal Study of Parents and Children (ALSPAC, www.bris.ac.uk/alspac) for their scarring response to a standard lesion that they all will have received as teenagers. The first natural experiment in this case was that of receiving a BCG vaccination in early life. Approximately 800 scars were measured and these exhibited a normal, bell shaped distribution of scar diameters (mean 6mm SD 3.37). Genome-wide association analysis of this phenotype revealed a low frequency single nucleotide polymorphism (SNP) in a regulatory intronic region of the GPCR, LGR4, that we find associates with the tendency to scar less.
1-Complete current ALSPAC studies of scarring genetics – across human association studies and lab-based model.
2-Combine existing recall by genotype (RbG) and GWAS results, developing bioinformatic analysis of established and possibly new signals for scarring traits.
3-Extend results from the ALSAC to other data sets and other pertinent phenotypes – specifically measured BCG and C-section scars in the Pelotas cohort, Brazil.
4-Work with a collaborator consortium focused on fibrosis traits more generally to widen the analysis of genetic contributions to scarring.
Extension of existing population health data will be to combine the existing evidence available from GWAS and the RbG undertaken in ASPAC in order to provide best estimates of association between known loci and scarring. Work will expand on the initial GWAS analysis through the incorporation of the Pelotas 1982 birth cohort where we examine BCG scar size at the current whole cohort clinic and exapand this to include the study of C-section scars which – in Brazil – are far higher in frequency than in the UK and provides an excellent opportunity to examine both the observational factors and genetic factors associated with differential scarring. Lastly, as part of a broad and exploratory portfolio of approaches to the assessment of genetic contributions to scarring events which may flag loci of interest for mechanistic studies in model organisms (below), we propose to incorporate the systematic followup of new findings for pulmonary fibrosis through a GWAS study that is currently being collaboratively led by colleagues at the University of Leicester.
[1] Hopkinson-Woolley J HD, Gordon S and Martin P. Journal of Cell Science
107, 1159-1167 (1994). 1994;107:1159-67.
[2] Hemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D, et al. Elife. 2018;7.
[3] Victora CG, Barros FC. Int J Epidemiol. 2006;35(2):237-42.
[4] Horta BL, Gigante DP, Goncalves H, dos Santos Motta J, Loret de Mola C,
Oliveira IO, et al. Int J Epidemiol. 2015;44(2):441, a-e.
Professor Nicholas Timpson (lead), Dr Laura Corbin, Dr Kaitlin Wade
There is strong evidence that BMI causally influences a wide range of health outcomes, but there is little understanding of the mechanisms driving BMI effects and at the level of the population we are limited in our ability to alter BMI. The main theme of this work is to dissect the causal effects which have been shown to lie between BMI and health outcomes; i.e. to better understand how body mass index (BMI) exerts an effect on human health. The aim is to do this using multiple-omic data sets in complementary study designs and through applied genetic epidemiology. This is a unique opportunity to study in a research team dedicated to this and to get access to a series of mature data sets able to address this question. This is a PhD which will be coordinated with a Wellcome Trust funded programme of research that explicitly seeks to use the most contemporary and powerful study designs, multi-omic data (including that from the metabolome, microbiome and proteome) and analytical techniques to explore BMI as a risk factor.
This work aims to identify omic intermediates important in the link between BMI and disease. The work sets out to better understand how body mass index (BMI) exerts an effect on human health using in complementary study designs and through applied genetic epidemiology. Proposed work will aim to integrate data from multi-omic data collections in efforts to assess these as mediating routes between BMI variation and disease.
The proposed project will focus on multiple, overlapping omic measurements and how they can be used to understand the mechanisms by which BMI contributes to disease. This work will involve the use of standard epidemiological analysis approaches (such as linear regression and applied approaches like Mendelian randomisation), but will also lean on data driven approaches to rich omic data sets collected across trials of BMI/weight targeted interventions to describe shared and unique relationships between change and omic intermediate read-out (principal component and variable analysis along with systematic approaches to omic profile effects). There will be an opportunity to develop new methods across these data sets and study designs. This will include a causal mapping of the human faecal microbiome onto the blood metabolome (using data from the Flemish Gut Flora Project, which includes ~3,000 individuals with questionnaire, clinic, genetic and microbiome data), but also other sources of metabolomic and proteomic data available across multiple studies with marked examples of variation (induced and natural) in body composition and specifically BMI.
[1] doi.org/10.1371/journal.pmed.1003786
[2] doi: 10.2337/db21-0397
[3] doi: 10.1038/s41564-020-0743-8
[4] doi.org/10.1038/s41366-021-00896-1
[5] doi.org/10.1101/2022.07.15.22277671
[6] doi.org/10.1002/oby.23441
Professor Nicholas Timpson (lead), Dr Laura Corbin (University of Bristol), Dr Brian Lam (University of Cambridge)
The hypothalamus is central to controlling energy homeostasis and reproduction1. The leptin-melanocortin pathway in the hypothalamus senses hormonal signals, namely leptin and insulin from the body and regulates energy2. Recently we showed that loss-of function (LoF) mutations in MC4R are associated with increased BMI, which is driven by both elevated linear growth and fat mass accrual3. MC3R, is also implicated in linear growth puberty4.
To understand the neuro-architecture of the hypothalamus we aggregated single-cell studies and created an integrated atlas5. Cell populations identified are known to regulate energy balance (e.g. POMC and GLP1R neurons) and growth (GHRH neurons) and gene markers characteristic for these populations are druggable and are targeted for treatments of obesity T2D6.
Working across the MRC Integrative Epidemiology Unit and the Wellcome-MRC Institute of Metabolic Science (Professor Sir Stephen O’Rahilly), we will systematically characterise genes expressed in hypothalamic cells, identify associated human genetic variation and study potential effects on anthropometric and reproductive development.
1.Spatially characterise gene expression in the mouse and human hypothalamus using state-of-the-art spatial transcriptomic techniques
2.Identify rare, non-synonymous Human hypothalamic genetic variations in the 9,000 exomes of Avon Longitudinal Study of Parents and Children (ALSPAC7) and investigate their association with anthropometric measurements (cross-sectional and trajectories) and reproductive development (e.g. Tanner stages, age at menarche, peak height velocity)
3.Experimental studies on the effects of selected genetic mutations in-vitro cellular models
The methods deployed within a PhD nested in this initiative will be tailored to the specific project work undertaken. Two main of focus areas will be the characterisation of locus contributions within model studies and the analysis of specific genetic variation in population-based studies:
• Histological techniques including tissue processing and sectioning
• Spatial transcriptomics
• Bioinformatics analysis and integration of single-cell and spatial datasets
• Tissue cell culture and in-vitro functional assays
• Molecular biology and immunoblotting
• Confocal microscopy and imaging
• Preparation of population-based longitudinal phenotype data.
• Pooled sample and whole population exome sequence data analysis.
• Association testing within population-based studies with longitudinal phenotypic data based on genomewide array data using basic genome-wide association approaches.
• Similarly focused whole exome and targeted sequencing data using rare variant and structural variant approaches.
1. Lowell, N. Engl. J. Med. 380, 459–471 (2019).
2. Yeo, Mol Metab, 48 101206.3. Wade, Nature medicine 27, 1088-1096 (2021).
4. Lam, Nature 599, 436-441
5. Steuernagel, Nat Met 4, 1402–1419 (2022).
6. Vilsbøll BMJ (2012) 7771(January):1–11. doi: 10.1136/bmj.d7771
7. Boyd, IJE 42 (1), 111–127 (2013)
Dr Kushala Abeysekera (lead), Prof Matthew Hickman , Prof Nicholas Timpson
Historically, cirrhosis (irreversible liver scarring), often due to alcohol-related liver disease (ARLD) and non-alcoholic fatty liver disease (NAFLD), presents in the 5th and 6th decade. 1 in 6 inpatients die during hospital admission (1), with over 70% of new liver disease presenting acutely, many dying without the chance to change their lifestyle (2).
Liver disease presents is now one of the leading causes of death in 35-49 year olds, overtaking suicide (3). Early detection of liver disease is crucial to prevent progression to advanced disease and facilitate treatment. Young adults represent the next frontier in tackling the public health crisis of liver disease (4). In this regard, the Avon Longitudinal Study of Parents and Children (ALSPAC) is uniquely placed to provide normative data and evaluate the rising burden of liver disease.
When aged 17 years, ALSPAC participants were offered liver scans to assess NAFLD, revealing a 2.7% prevalence (5). When aged 24 years, 20.7% of participants had steatosis and 1 in 40 had liver scarring, the precursor to cirrhosis (6). Our participants are now 30 and over 4000 are set to receive further liver scan measurements.
1. Estimating prevalence of NAFLD and ARLD in young adults in the general population aged 30 years.
2. Studying early life course determinants to understand why some participants are developing liver scarring earlier than others.
3. Evaluating if existing polygenic risk scores can be used to detect and predict individuals with subclinical liver scarring.
Examining and presenting normative data from the 30 year liver clinic to determine the prevalence of NAFLD and ARLD amongst ALSPAC participants. Use of linear and logistic regression to explore the strength of associations of common exposures associated with liver disease e.g. alcohol use, body mass index and sociodemographics. Mapping progression of disease from participants who had fibrosis aged 24 years.
Using the existing data within ALSPAC with outcomes of steatosis and fibrosis across ages 17, 24 and now 30 years, evaluate if there are sensitive periods for an impact of change in, or accumulation of, exposures including adiposity, cardiometabolic factors, and alcohol on liver steatosis/fibrosis at 30 years and its progression from 17 to 30 years. This will involve linear and logistic regression modelling and interaction analyses.
Finally, polygenic risk scores for detection of chronic liver disease exist (7). Using existing genomic data from ALSPAC, work will be done to explore if such scores are useful in this different ages (17, 24, 30 years), and whether they can be combined with clinical markers to improve detections and prediction of liver fibrosis.
1. TORCH-UK. Gut. 2022;71.
2. Williams et al. The Lancet. 2014;384(9958):1953-97.
3. Trust. The alarming impact of liver disease in the UK: Facts and statistics. 2019.
4. Williams et al. Lancet. 2018;391(10125):1097-107.
5. Lawlor et al. J Clin End Met. 2014;99(3):E410-7.
6. Abeysekera et al. The Lancet G & H. 2020.
7. De Vincentis et al. Clin G H. 2021.
Prof Tom Gaunt (lead), Dr Maria Sobczyk,
Mendelian Randomization (MR) is a genetic epidemiology method which utilises variants sourced from genome-wide association studies (GWAS) to assess causality between risk/protective factors and disease outcomes in a manner less biased to observational studies(1).
Recently, applications of MR to drug target prioritization gained a lot of interest. One approach is to use expression or protein quantitative trait loci (QTL) for a drug target gene/protein as exposures with the aim of establishing the effects of perturbing the intended target directly (“on-target”)(2). However, most drugs will have broader molecular consequences, either in parallel (“off-target”) or downstream of the intended target which can be exploited to repurpose drugs for novel indications. One way to identify such off-targets is to mine high-throughput expression datasets of various cell lines exposed to small molecule drugs and genetic perturbations(3).
Using a variety of high-throughput genomics/other -omics resources, the project will aim to discover and triangulate with Mendelian Randomization:
1) Drug off-target side effects
2) Drug repurposing opportunities
You will use data on transcriptional responses to drugs from expression perturbation databases and other resources to identify additional genes and proteins for off-target side effect prediction, using multivariable MR to identify direct effects. In addition, you will use protein-protein interaction data collated in our locally developed resource: EpiGraphDB(4) to further refine gene sets to instrument.
You will also use drug perturbation data sources to evaluate the potential to repurpose drugs with a previously-published approach(5) that compares disease-associated transcriptomic profiles to in vitro drug transcriptomic profiles to identify profiles that may reverse the effect of disease on gene expression. You will consider tissue-specific and pathway-specific transcriptomic profiles for a variety of diseases and explore whether this identifies additional repurposing opportunities. You will validate drug repurposing and off-target side effect predictions using observational data from UK Biobank and electronic health records, in addition to MR.
1. Sanderson, E. et al. Mendelian randomization. Nat. Rev. Methods Prim. 2, 6 (2022).
2. Gill, D. et al. Mendelian randomization for studying the effects of perturbing drug targets. Wellcome open Res. 6, 16 (2021).
3. Keenan, A. B. et al. The Library of Integrated Network-Based Cellular Signatures NIH Program: System-Level Cataloging of Human Cells Response to Perturbations. Cell Syst. 6, 13–24 (2018).
4. Liu, Y. et al. EpiGraphDB: a database and data mining platform for health data science. Bioinformatics 0–0 (2020).
5. Wu, P. et al. Integrating gene expression and clinical data to identify drug repurposing candidates for hyperlipidemia and hypertension. Nat. Commun. 13, 46 (2022).
Professor Laura Howe (lead), Dr Annie Herbert, Associate Professor Jon Heron Dr Alison Teyhan
Around 1 in 3 young adults in the UK have experienced intimate partner violence and abuse (IPVA; psychological, physical, or sexual abuse from an intimate partner) by the time they are 21. Effective interventions delivered in healthcare settings (e.g. GP practices or Accident & Emergency) aim to prevent further abuse, and address current social or health problems (e.g. access to housing or poor mental health). Such interventions have been trialled in older adults. However, little is known about the population of young adult IPVA survivors who come into contact with healthcare settings, and whether and how these health service contacts alter their health trajectories. Such understanding can indicate whether interventions trialled in adults may also be effective in young adults, for which outcomes, and what can be done to better tailor interventions towards this age group. This project will use rich birth cohort data linked to administrative primary and secondary healthcare data, combined with sophisticated longitudinal statistical methods, to build a picture of the life course health and health care trajectories of young adult survivors of IPVA.
Aim: The overall aim of this project is to describe health and healthcare service use trajectories of young adult IPVA survivors, and the impact of service use on subsequent health outcomes
Objectives:
1) To describe the health trajectories of young adults who do and do not experience IPVA, using both cohort and administrative data.
2) To describe the patterns of health service use amongst young adults who do and do not experience IPVA.
3) To assess the degree to which interaction with the health service may mitigate the health trajectories of people who experience IPVA, or conversely, how patterns of health service use may be a red flag for health problems or continued IPVA exposure.
4) To investigate whether the associations of IPVA with health trajectories and patterns of health service use differ by sex, socioeconomic circumstances, early life adversity, or type, timing, and severity of IPVA.
This project will use data from the Avon Longitudinal Study of Parents and Children (ALSPAC; Children of the 90’s). Participants reported on their current and past IPVA experiences at age 21, including IPVA subtypes (psychological, physical, sexual), frequency, and impact. Rich longitudinal health data is collected through questionnaires and research clinics. The ALSPAC data have been linked to primary and secondary care data, which will be used to capture healthcare contacts and referrals until age 28. READ codes in primary care data and ICD-10 codes in secondary care data, supplemented by ALSPAC clinic and survey data, will be used to capture health and health care trajectories. The PhD will provide training in standard regression models, life course and longitudinal modelling techniques (e.g. multi-level and latent growth models, difference-in-difference), and in the use of both cohort and administrative data sources. These models will incorporate interaction analyses, and multiple imputation to deal with missing data.
- Herbert A, Heron J, Barter C et al. Risk factors for intimate partner violence and abuse among adolescents and young adults: findings from a UK population-based cohort [version 3; peer review: 2 approved]. Wellcome Open Res 2021, 5:176 (https://doi.org/10.12688/wellcomeopenres.16106.3)
- Ogbe E, Harmon S, Van den Bergh R, Degomme O. A systematic review of intimate partner violence interventions focused on improving social support and/ mental health outcomes of survivors. PLoS One. 2020 Jun 25;15(6):e0235177. doi: 10.1371/journal.pone.0235177. PMID: 32584910; PMCID: PMC7316294.
Feder G. Responding to intimate partner violence: what role for general practice? Br J Gen Pract. 2006 Apr;56(525):243-4. PMID: 16611508; PMCID: PMC1832227.
- Kristoffer S. Berlin, PhD, Gilbert R. Parra, PhD, Natalie A. Williams, PhD, An Introduction to Latent Variable Mixture Modeling (Part 2): Longitudinal Latent Class Growth Analysis and Growth Mixture Models, Journal of Pediatric Psychology, Volume 39, Issue 2, March 2014, Pages 188–203, https://doi.org/10.1093/jpepsy/jst085
Professor Abigail Fraser (lead), Professor Tom Gaunt, Prof Laura Howe; Dr Emma Hart
Hypertension (defined as systolic/diastolic pressures >140/90 mmHg) is the leading risk factor for cardiovascular disease (CVD) and events, including stroke, myocardial infarction and heart failure. Both systolic and diastolic blood pressures (BP) are associated with CVD end points, albeit differentially across different stages of the life course.
Systolic and diastolic BP reflect different aspects of the cardiovascular system, and likely reflect different underlying pathologies. The physiological determinants of diastolic BP are more complex than determinants of systolic BP and include 1) the level of the systolic BP, 2) the level of stiffness of the arteries and arterioles and 3) the resistance of arterioles to blood flow. It is also thought to reflect different pathologies at different life stages: in young adults, higher diastolic BP is associated with greater CVD risk in later life but in older populations, lower diastolic BP (for a given systolic BP) is associated with greater mortality risk.
Diastolic BP has been understudied compared to its systolic counter-part, with the latter more commonly used in CVD prediction models and as a clinical end point in RCTs. However diastolic BP at different stages of the lifecourse, and in relation to the level of systolic BP, may help improve risk stratification, and have translational impact.
To better understand diastolic blood pressure changes across the life course, its determinants, and if/how it relates to cardiovascular outcomes independently of systolic blood pressure.
The student will use population data (from ALSPAC, UK Biobank and other population based cohorts) and epidemiological methods, as well as experimental approaches and bioinformatics (should they wish). The student will be encouraged to identify specific objectives within the general scope of the project that are of interest to them.
We envisage that the project will include some, not necessarily all, of the following. The student will be supported to tailor the project to their interests.
1. Literature review to identify determinants of DBP at both a mechanistic (e.g. microvascular stiffness) and population level (e.g. sodium intake).
2. Assembling life course trajectories of diastolic blood pressure using multilevel models, equivalent to those constructed by Wills et al Plos Medicine 2011 and investigating potential sex-differences, as well as effects of puberty and menopause on diastolic blood pressure in women.
4. Using Mendelian Randomization approaches, including multivariable MR to identify
a. causal risk factors for diastolic blood pressure (independent of systolic blood pressure) and
b. the cardiovascular consequences of variation in diastolic blood pressure across the life course.
5. Use bioinformatics to interrogate the molecular pathways mediating the effects of genetic variants uniquely associated with diastolic blood pressure.
Gibran Hemani (lead), George Davey Smith,
Estimating the influence of genotypes and traits on fitness is a fundamental question in biology. While the question requires causal inference, many analytical frameworks (e.g. Price equation) are embedded in a statistical framework that are unable to separate cause from correlation. Mendelian randomisation is an analytical framework that is widely used in epidemiological studies, and we have shown it can be used to make causal inference of traits on fitness. In this project we will investigate the theoretical properties of this approach and apply it to a vast array of genetic data to understand the landscape of the genotype-phenotype map on fitness. This mapping will allow us to make inference about critically important questions, such as the degree to which pleiotropy modulates directional selection, and the degree to which stabilising selection maintains natural genetic variation.
1. One approach to estimate the influence of traits on fitness is to estimate the causal influences on reproductive traits such as fecundity, age of first menarche, age of first birth and age of menopause. We will investigate the way in which these traits approximate fitness, using multi-generation samples and latent modelling through genomic structural equation modelling
2. We will perform an exhaustive scan of traits on measures of fitness determined from (1), to build a profile of the genotypes-trait-fitness landscape
3. We will use (2) to model and infer the degree to which important mechanisms such as pleiotropy and trait network effects either accelerate or constrain directional selection, and to what degree they maintain natural genetic variation.
Throughout the project we will use a mixture of genome-wide association studies, a suite of methods developed for Mendelian randomisation, as well as theoretical and simulation analyses of selection processes.
Evershed RP, Davey Smith G et al. Dairying, diseases and the evolution of lactase persistence in Europe. Nature. 2022 Aug;608(7922):336-345. doi: 10.1038/s41586-022-05010-7 – Example of how, in principle, Mendelian randomization could be used in future to infer fitness relationships
Hemani G, Knott S, Haley C. An evolutionary perspective on epistasis and the missing heritability. PLoS Genet. 2013 Feb;9(2):e1003295. doi: 10.1371/journal.pgen.1003295 – Exploration of the mechanisms maintaining genetic variation in the context of widespread additive genetic variation
Mills, M.C., Tropf, F.C., Brazel, D.M. et al. Identification of 371 genetic variants for age at first sex and birth linked to externalising behaviour. Nat Hum Behav 5, 1717–1730 (2021). https://doi.org/10.1038/s41562-021-01135-3 - A GWAS on reproductive traits
Okasha, S. & Otsuka, J. The Price equation and the causal analysis of evolutionary change. Philos. Trans. R. Soc. B Biol. Sci. 375, 20190365 (2020) – The relationship between causal inference and evolutionary modelling
Prof Sarah Lewis (lead), Prof Richard Martin, Dr Richard Pulsford (University of Exeter) Prof Russ Jago Dr Freddie Bray (International Agency for Research on Cancer)
Around 40% of all cancers are thought to be avoidable by modification of lifestyle factors. Obesity has been found to be a risk factor for several cancers and was estimated to have caused around 3.6% of all new cancers which occurred in 2012. There is now an emerging evidence base which shows that low levels of physical activity can increase the risk of cancer. The World Cancer Research Fund (WCRF), as part of their continuous update project, have concluded that there is strong evidence that high levels of physical activity decreases the risk of cancers of the breast, endometrium and colon and rectum. In addition, we have recently shown using common genetic variation (a technique called Mendelian randomization) that physical activity protects against prostate, colorectal and breast cancer risk. In our analyses we found larger protective effects of physical activity on cancers of the breast, prostate, colon and rectum than were estimated by observational studies. It is possible that cancers at other sites are similarly influenced by physical activity but they have not yet been investigated in this way.
The aims of the project are to:
1. Identify the patterns of physical activity which are most likely to be causing cancer using wrist-worn accelerometer data and cancer outcomes in UK Biobank.
2. Test whether low levels of physical activity is a causal risk factor for cancer at several sites using Mendelian randomization and data from large cancer consortia
3. Use the best publicly available data to estimate the global prevalence of low levels of physical activity by country/region/ethnicity.
4. Estimate the global burden of cancer attributable to low levels of physical activity using cancer surveillance data compiled by IARC broken down by country/region and cancer type.
The student will be access wrist worn accelerometry data from UKBiobank or other similar population based cohorts. The student will characterised in a variety of different ways in order to investigate different features or patterns in behaviour and will use epidemiological techniques to understand the relationship between patterns of activity and cancer.
The student will be trained on the theory and application of Mendelian radomization and will use this to determine which cancers are caused by low levels of physical activity (and other patterns of physical activity).
The student will receive training in the interpretation of cancer registry data and will use this data alongside global data on physical activity to determine the proportion of cancers which can be attributed to insufficient physical activity worldwide.
https://bjsm.bmj.com/content/52/13/826.long
https://www.wcrf.org/dietandcancer/exposures/physical-activity
https://www.nature.com/articles/s41467-020-14389-8
Josine Min (lead), Gibran Hemani, Johann Hawe (Illumina)
Genome wide associations studies (GWASs) have discovered many genetic associations with a large range of human traits, but the functional consequences of GWAS signals often remain elusive, as most GWAS signals reside in non-coding genomic regions. However, GWAS signals are enriched in DNA regulatory elements and cell type specific annotations, and thus it is likely that GWAS signals confer their effects through modulating gene regulatory mechanisms.
Genetic factors for molecular traits (DNA methylation, gene expression, protein levels) are being discovered at an astonishing rate. A major hope for these genetic factors is that they can be used to identify causal mechanism of complex traits.[1] Fascinatingly, the dimensionality of molecular phenotyping is bound to surpass the density of human genetic variation, meaning that genetic pleiotropy (where one variant influences multiple phenotypes) is a necessary feature amongst molecular phenotypes. This has critical downstream implications for being able to use genetics to make valid causal inference of putative molecular targets on disease incidence and progression.
This project will build a resource for storing and querying harmonized molecular QTL data in a computational efficient manner, and then use that resource to build pleiotropy maps of human molecular phenotypes. These maps will subsequently be used in evolutionary modelling and in collaboration with Illumina using machine learning and artificial intelligence approaches to understand the basis of molecular pleiotropy. This will include a research visit to Illumina AI lab in Germany.
1. Develop a computational framework for storing and querying molecular QTLs that will integrate with the OpenGWAS project
2. Generate pleiotropy maps using fine mapping and colocalization
3. Use evolutionary models to understand the impact of pleiotropy on natural selection processes
4. Use deep learning to predict disease mechanisms and disease progression from molecular pleiotropy maps
Currently summary statistics are stored for each GWA dataset separately. However this is not sustainable for QTL summary statistics with millions of molecular features. Therefore a new framework will be developed to store complete molecular QTL statistics for each dataset. Fine mapping and colocalization analysis will be used to integrate methylation QTL statistics from the Genetics of DNA Methylation Consortium, expression QTL statistics from eQTLGen and protein QTL statistics from SCALLOP and ALSPAC. This will result in maps of colocalized molecular traits. We will investigate biological models of pleiotropy, for example by using evolutionary models and gene-environmental interactions. We will use deep learning to identify molecular pleiotropy maps that correspond to distinct phenotypic patient subgroups.
1. Neumeyer S, Hemani G, Zeggini E. Strengthening Causal Inference for Complex Disease Using Molecular Quantitative Trait Loci. Trends Mol Med. 2020;26(2):232-41.