Proposal summaries

These are research proposals that have been approved by the ALSPAC exec. The titles include a B number which identifies the proposal and the date on which the proposals received ALSPAC exec approval.

Click here to export results in Word format.

B2745 - Appropriate statistical models for multivariate epigenetic data with focus on prenatal alcohol exposure - 2016/29/09

B number: 
B2745
Principal applicant name: 
Isobel Claire Gormley | University College Dublin (Ireland)
Co-applicants: 
Title of project: 
Appropriate statistical models for multivariate epigenetic data, with focus on prenatal alcohol exposure.
Proposal summary: 

How much alcohol is safe to drink during pregnancy? Many studies focussed on answering this question report discordant results. While the deleterious effect of heavy alcohol consumption by pregnant mothers on their offspring is well established publicly and scientifically, the effect of moderate or intermittent consumption is less well substantiated. This uncertainty leads to unclear advice being delivered by global health organisations and general practitioners. This proposal aims to move towards understanding the influence of prenatal alcohol exposure patterns on newborn children and on their life course, through analysis of epigenetic data arising from the Avon Longitudinal Study of Parents and Children (ALSPAC). This proposal will focus on analysing the multivariate DNA methylation data from offspring and their mothers’ survey data in ALSPAC using appropriate statistical models in order to make correct inference about the influence of prenatal alcohol exposure patterns.

Impact of research: 
I anticipate that the impact of my proposed research will be both in the area of epigenetic epidemiology, through improved understanding of the influence of different prenatal exposure patterns on epigenetic mechanisms, and in the area of statistics, through the development of novel statistical methods for appropriate analysis of high dimensional DNA methylation data.
Date proposal received: 
Wednesday, 7 September, 2016
Date proposal approved: 
Thursday, 29 September, 2016
Keywords: 
Statistics/methodology, Addiction - e.g. alcohol, illicit drugs, smoking, gambling, etc., Pregnancy - e.g. reproductive health, postnatal depression, birth outcomes, etc., Computer simulations/modelling/algorithms, Epigenetics, Statistical methods, Biological samples -e.g. blood, cell lines, saliva, etc., Birth outcomes, Childhood - childcare, childhood adversity, Genetics - e.g. epigenetics, mendelian randomisation, UK10K, sequencing, etc., Methods - e.g. cross cohort analysis, data mining, mendelian randomisation, etc., Offspring, Parenting, Statistical methods

B2752 - Improving the vitamin D instrument - 2016/28/09

B number: 
B2752
Principal applicant name: 
Tom Dudding | IEU - SSCM (UK)
Co-applicants: 
Title of project: 
Improving the vitamin D instrument
Proposal summary: 

The genes associated with vitamin D levels can be utilised to infer whether vitamin D causally effects a particular outcome (for example disease). Traditionally, these genes have been identified by looking at what genes effect the level of vitamin D in circulating blood. This method is likely to miss genes that are important and does not take into account the active form of vitamin D that enters human cells to cause a response. This project will look at diseases and other body chemicals that are known to be linked with vitamin D to identify genes that control the biochemistry around vitamin D.

Impact of research: 
Improvement in the genetic instrument of vitamin D will allow improvements in MR studies in terms of inference and power. Development of a systematic approach for instrument generation that does not solely include taking significant GWAS hits.
Date proposal received: 
Wednesday, 21 September, 2016
Date proposal approved: 
Wednesday, 28 September, 2016
Keywords: 
Genetics, vitamin D deficiency,, Computer simulations/modelling/algorithms, Gene mapping, GWAS, Biological samples -e.g. blood, cell lines, saliva, etc., Biomarkers - e.g. cotinine, fatty acids, haemoglobin, etc., Genetics - e.g. epigenetics, mendelian randomisation, UK10K, sequencing, etc., Hormones - cortisol, IGF, thyroid, Metabolic - metabolism, Methods - e.g. cross cohort analysis, data mining, mendelian randomisation, etc.

B2753 - Antecedents of obesity in the ALSPAC cohort the role of early diet sugar and fibre antibiotics and gut bacteria - 2016/28/09

B number: 
B2753
Principal applicant name: 
Phil Langton | University of Bristol (United Kingdom)
Co-applicants: 
Dr Helen Kennedy, Dr Pauline Emmett
Title of project: 
Antecedents of obesity in the ALSPAC cohort: the role of early diet (sugar and fibre), antibiotics and gut bacteria
Proposal summary: 

The information collected during the ALSPAC study can be used to answer questions that were not even imagined when the study began. One way that scientists attempt to understand complex systems, and our bodies are complex systems, is by observation. Another approach is to perform experiments but experiments with humans is very expensive and so only small numbers of people are studied. The ALSPAC study is different. By recording lots of facts about a great many babies, and continuing to collect facts from them into adulthood, it is possible to spot patterns that would otherwise be missed.

So, we all eat everyday. Indeed, we have to eat often to remain healthy. We don't all choose the same foods and parents certainly don't all choose the same foods for their children and so we can ask if these differences in early diet may influence the growth, development and health of children. The ALSPAC study has a large enough group of children that broad differences in early diet may result in recognisable patterns of growth and health. We are particularly interested to know if the amount of sugar or sweet-tasting foods in early life results in children actively choosing foods that are sweet as they get older. We are also interested to know if these food choices make it more or less likely that children will grow fatter or have a higher risk of diseases like diabetes.

Impact of research: 
Very recently the risk factors associated with specific macronutrients has been thrown into doubt. The risks associated with fat are being dialled back whilst those associated with carbohydrates, in particular sugars, are being elevated. Other factors that have not been widely regarded to play a prominent role, such as the microbiome, are now being viewed as potentially formative for development and health. The ALSPAC cohort provides a near perfect dataset with which to address these questions. The potential exists to find associations that argue powerfully for or against a role for key factors in our diet as well as things like exposure to siblings, pets, antibiotics and the nature of the environment (urban or rural) in which children grow and develop.
Date proposal received: 
Wednesday, 21 September, 2016
Date proposal approved: 
Wednesday, 28 September, 2016
Keywords: 
Epidemiology, Diabetes, Gastrointestinal, Obesity, Qualitative study, Statistical methods, Biomarkers - e.g. cotinine, fatty acids, haemoglobin, etc., Blood pressure, Physical - activity, fitness, function, Sex differences, Siblings, Statistical methods, BMI, Breast feeding, Development, Environment - enviromental exposure, pollution, Growth, Metabolic - metabolism, Microbiome, Nutrition - breast feeding, diet

B2753 - Antecedents of obesity in the ALSPAC cohort the role of early diet sugar and fibre antibiotics and gut bacteria - 2016/28/09

B number: 
B2753
Principal applicant name: 
Phil Langton | University of Bristol (United Kingdom)
Co-applicants: 
Dr Helen Kennedy, Dr Pauline Emmett
Title of project: 
Antecedents of obesity in the ALSPAC cohort: the role of early diet (sugar and fibre), antibiotics and gut bacteria
Proposal summary: 

The information collected during the ALSPAC study can be used to answer questions that were not even imagined when the study began. One way that scientists attempt to understand complex systems, and our bodies are complex systems, is by observation. Another approach is to perform experiments but experiments with humans is very expensive and so only small numbers of people are studied. The ALSPAC study is different. By recording lots of facts about a great many babies, and continuing to collect facts from them into adulthood, it is possible to spot patterns that would otherwise be missed.

So, we all eat everyday. Indeed, we have to eat often to remain healthy. We don't all choose the same foods and parents certainly don't all choose the same foods for their children and so we can ask if these differences in early diet may influence the growth, development and health of children. The ALSPAC study has a large enough group of children that broad differences in early diet may result in recognisable patterns of growth and health. We are particularly interested to know if the amount of sugar or sweet-tasting foods in early life results in children actively choosing foods that are sweet as they get older. We are also interested to know if these food choices make it more or less likely that children will grow fatter or have a higher risk of diseases like diabetes.

Impact of research: 
Very recently the risk factors associated with specific macronutrients has been thrown into doubt. The risks associated with fat are being dialled back whilst those associated with carbohydrates, in particular sugars, are being elevated. Other factors that have not been widely regarded to play a prominent role, such as the microbiome, are now being viewed as potentially formative for development and health. The ALSPAC cohort provides a near perfect dataset with which to address these questions. The potential exists to find associations that argue powerfully for or against a role for key factors in our diet as well as things like exposure to siblings, pets, antibiotics and the nature of the environment (urban or rural) in which children grow and develop.
Date proposal received: 
Wednesday, 21 September, 2016
Date proposal approved: 
Wednesday, 28 September, 2016
Keywords: 
Epidemiology, Diabetes, Gastrointestinal, Obesity, Qualitative study, Statistical methods, Biomarkers - e.g. cotinine, fatty acids, haemoglobin, etc., Blood pressure, Physical - activity, fitness, function, Sex differences, Siblings, Statistical methods, BMI, Breast feeding, Development, Environment - enviromental exposure, pollution, Growth, Metabolic - metabolism, Microbiome, Nutrition - breast feeding, diet

B2754 - Imputation of the ALSPAC data to the new HRC panel using the Mach algorithm - 2016/28/09

B number: 
B2754
Principal applicant name: 
George McMahon | Avon Longitudinal Study of Parent and Children
Co-applicants: 
Nicholas Timpson, Lavinia Paternoster
Title of project: 
Imputation of the ALSPAC data to the new HRC panel using the Mach algorithm
Proposal summary: 

ALSPAC has contributed to a large number of genomewide association studies (GWAS). We have these data imputed to the recent reference panels for 9,321 mothers and 9,115 children (1000 genomes) and recently to the latest release of the 1000 Genomes (B2710). There is a further requirement to impute this data to the Haplotype Reference Consortium (HRC), a much larger reference panel of over 60,000 haplotypes to contribute to ongoing international GWAS studies. Furthermore, imputation can vary in quality according to imputation algorithm. Imputation using the MACH algorithm will bring ALSPACs HRC imputed data in line with other large genetic studies.

Impact of research: 
Continued contribution of ALSPAC data to collaborative papers in medium to high impact journals
Date proposal received: 
Friday, 23 September, 2016
Date proposal approved: 
Wednesday, 28 September, 2016
Keywords: 
Genetics

B2755 - Metabolomics of academic performance and psychosis risk - 2016/28/09

B number: 
B2755
Principal applicant name: 
Hugh Ramsay | Department of Psychiatry, University of Oulu (Finland)
Co-applicants: 
Professor Mika Ala-Korpela
Title of project: 
Metabolomics of academic performance and psychosis risk
Proposal summary: 

This project aims to look at whether blood markers might be associated with two other difficulties seen in adolescence: “psychotic experiences” and academic difficulties in school. Both academic/cognitive difficulties in school and psychotic experiences in adolescence are associated with later higher risk for severe mental disorders. Better understanding the biology behind these associations has the potential to help doctors and others to identify who is at highest risk for later problems and to intervene to help them before the difficulties become more severe.

Impact of research: 
This research has the potential to inform us on both the metabolic precursors of mental illness and the metabolic precursors of poorer academic achievement. We know that cognitive symptoms predate psychosis and are a significant independent risk factor for poorer functional outcomes. Fuller understanding of this (through academic achievement) and of psychosis risk in the population has the potential to inform understanding of biological processes to these outcomes, as well as early identification and intervention to prevent long-term disability.
Date proposal received: 
Monday, 26 September, 2016
Date proposal approved: 
Wednesday, 28 September, 2016
Keywords: 
Mental health - Psychology, Psychiatry, Cognition, Mental health, Metabolomics, Intelligence - memory, Metabolic - metabolism

B2756 - DNA methylation predictors of psychosis-like symptoms - 2016/28/09

B number: 
B2756
Principal applicant name: 
Esther Walton | University of Bristol - IEU
Co-applicants: 
Title of project: 
DNA methylation predictors of psychosis-like symptoms
Proposal summary: 

Psychosis-like symptoms (PLIKS) are experienced by around 15 % of individuals in childhood and adolescence (Zammit et al., 2008) and research suggests that the experience of such symptoms might increase the risk of developing psychotic disorder during adulthood (Fisher et al., 2013; Poulton et al., 2000; van Os, Linscott, Myin-Germeys, Delespaul, & Krabbendam, 2009). However, little is known about early biological predictors (such as DNA methylation) of PLIKS. C-reactive protein (CRP) is an inflammation marker protein found in blood. CRP has been suggested to be involved in psychotic disorders such as schizophrenia (Miller, Culpepper, & Rapaport, 2014). Investigating how DNA methylation linked to CRP at birth associates with PLIKS in adolescence might shed light into potential biological risk pathways for psychosis.

Impact of research: 
Findings could lend novel insights into the epigenetic landscape of psychosis-like symptoms.
Date proposal received: 
Monday, 26 September, 2016
Date proposal approved: 
Wednesday, 28 September, 2016
Keywords: 
Mental health - Psychology, Psychiatry, Cognition, Mental health, Epigenetics, Metabolomics, Genetics - e.g. epigenetics, mendelian randomisation, UK10K, sequencing, etc., Methods - e.g. cross cohort analysis, data mining, mendelian randomisation, etc.

B2757 - Developing and validating a mathematical model to calculate and predict body mass index BMI and obesity in youth - 2016/28/09

B number: 
B2757
Principal applicant name: 
Lisa Kakinami | Department of Mathematics and Statistics, Concordia University (Canada)
Co-applicants: 
Title of project: 
Developing and validating a mathematical model to calculate and predict body mass index (BMI) and obesity in youth
Proposal summary: 

In youth, due to the changes in height and weight during the developmental period, mathematically modeling BMI is complex. Historically, this question has been addressed from one of two perspectives: (1) focused on the proper modeling but suffered from poor interpretability, and (2) focused on the applied, translational appeal but suffered from inadequate modeling. No studies to date have combined the strengths of the two perspectives in modeling children’s growth and development. Thus, this research program will aim to (1) using a large birth cohort, extend the existing models to incorporate all periods across the developmental period with a focus on the translational aspects of BMI and obesity risk, (2) cross-validate the model in another large, separate birth cohort (of which data is already obtained) and (3) compare the performance of the new model with pre-existing cross-sectional growth curves to predict BMI and adiposity changes from DXA. The current program will provide new, validated metrics to track and predict obesity risk across childhood. Study results will be used to study genetic, environmental- and behavioural-induced changes in growth across the developmental period. Given the rapid rise in childhood obesity, the applied statistical work developed here is expected to vastly improve the methodologies to characterize longitudinal growth in children.

Impact of research: 
Results will improve the way we model BMI over time in youth. The results will highlight the deficiencies of the currently used growth curves (which were developed from cross-sectional data) and will validate the longitudinal modeling.
Date proposal received: 
Tuesday, 27 September, 2016
Date proposal approved: 
Wednesday, 28 September, 2016
Keywords: 
Statistics/methodology, Obesity, Statistical methods, BMI, Development, Statistical methods

B2757 - Developing and validating a mathematical model to calculate and predict body mass index BMI and obesity in youth - 2016/28/09

B number: 
B2757
Principal applicant name: 
Lisa Kakinami | Department of Mathematics and Statistics, Concordia University (Canada)
Co-applicants: 
Title of project: 
Developing and validating a mathematical model to calculate and predict body mass index (BMI) and obesity in youth
Proposal summary: 

In youth, due to the changes in height and weight during the developmental period, mathematically modeling BMI is complex. Historically, this question has been addressed from one of two perspectives: (1) focused on the proper modeling but suffered from poor interpretability, and (2) focused on the applied, translational appeal but suffered from inadequate modeling. No studies to date have combined the strengths of the two perspectives in modeling children’s growth and development. Thus, this research program will aim to (1) using a large birth cohort, extend the existing models to incorporate all periods across the developmental period with a focus on the translational aspects of BMI and obesity risk, (2) cross-validate the model in another large, separate birth cohort (of which data is already obtained) and (3) compare the performance of the new model with pre-existing cross-sectional growth curves to predict BMI and adiposity changes from DXA. The current program will provide new, validated metrics to track and predict obesity risk across childhood. Study results will be used to study genetic, environmental- and behavioural-induced changes in growth across the developmental period. Given the rapid rise in childhood obesity, the applied statistical work developed here is expected to vastly improve the methodologies to characterize longitudinal growth in children.

Impact of research: 
Results will improve the way we model BMI over time in youth. The results will highlight the deficiencies of the currently used growth curves (which were developed from cross-sectional data) and will validate the longitudinal modeling.
Date proposal received: 
Tuesday, 27 September, 2016
Date proposal approved: 
Wednesday, 28 September, 2016
Keywords: 
Statistics/methodology, Obesity, Statistical methods, BMI, Development, Statistical methods

B2757 - Developing and validating a mathematical model to calculate and predict body mass index BMI and obesity in youth - 2016/28/09

B number: 
B2757
Principal applicant name: 
Lisa Kakinami | Department of Mathematics and Statistics, Concordia University (Canada)
Co-applicants: 
Title of project: 
Developing and validating a mathematical model to calculate and predict body mass index (BMI) and obesity in youth
Proposal summary: 

In youth, due to the changes in height and weight during the developmental period, mathematically modeling BMI is complex. Historically, this question has been addressed from one of two perspectives: (1) focused on the proper modeling but suffered from poor interpretability, and (2) focused on the applied, translational appeal but suffered from inadequate modeling. No studies to date have combined the strengths of the two perspectives in modeling children’s growth and development. Thus, this research program will aim to (1) using a large birth cohort, extend the existing models to incorporate all periods across the developmental period with a focus on the translational aspects of BMI and obesity risk, (2) cross-validate the model in another large, separate birth cohort (of which data is already obtained) and (3) compare the performance of the new model with pre-existing cross-sectional growth curves to predict BMI and adiposity changes from DXA. The current program will provide new, validated metrics to track and predict obesity risk across childhood. Study results will be used to study genetic, environmental- and behavioural-induced changes in growth across the developmental period. Given the rapid rise in childhood obesity, the applied statistical work developed here is expected to vastly improve the methodologies to characterize longitudinal growth in children.

Impact of research: 
Results will improve the way we model BMI over time in youth. The results will highlight the deficiencies of the currently used growth curves (which were developed from cross-sectional data) and will validate the longitudinal modeling.
Date proposal received: 
Tuesday, 27 September, 2016
Date proposal approved: 
Wednesday, 28 September, 2016
Keywords: 
Statistics/methodology, Obesity, Statistical methods, BMI, Development, Statistical methods

B2748 - External validation of a study concerning the effects of prenatal depressive symptoms on DNA methylation in school-aged children - 2016/28/09

B number: 
B2748
Principal applicant name: 
Anna Eichler | (Germany)
Co-applicants: 
PD Dr Hartmut Heinrich, Valeska Stonawski, Dr Stefan Frey
Title of project: 
External validation of a study concerning the effects of prenatal depressive symptoms on DNA methylation in school-aged children
Proposal summary: 

Despite diverse international diagnostic criteria, depression during pregnancy is very common with prevalence between 6 % and 38 % worldwide. Prenatal depressive symptoms are associated with changes in the cortisol system and are accepted as risk factors for future emotional problems in the child. Epigenetic DNA modifications are discussed as possible underlying mechanisms of this risk. DNA methylation is the most abundant epigenetic modification and has been linked to several disorders, such as PTSD, depression, schizophrenia or anxiety. However, studies are quite heterogenous in terms of tissues, methods and participants, as well as results. We conducted an epigenome-wide association study (EWAS) concerning DNA methylation changes due to maternal prenatal depressive symptoms in 167 children aged 6 to 9 years old. DNA was extracted from buccal cells and methylation was analyzed using the Infinium Human Methylation 450K BeadChip. We adjusted for sex, age and birth outcomes, and assessed effects of postnatal and current maternal depression simultaneously to detect the specific prenatal influence. Now we would like to validate our results in a larger external sample, the ALSPAC cohort. Considering the few and diverse EWASs published in this topic, replication of our results seem to be notably important.

Impact of research: 
We would like to confirm our results regarding the effects of prenatal maternal depressive symptoms on DNA methylation in children aged 6 to 9 years old. In our opinion, replication is important, especially in the young field of epigenetics. Valid findings for the considered age-range can then be compared to published results for neonates, assessing lasting effects of prenatal depressive symptoms during childhood. When assessing these long term effects it is quite important to distinguish the prenatal effects of maternal depression from postpartum and current effects. After validating our results it will be worthwhile to investigate the associations of methylation changes with the children’s functional data, e.g. emotional development, psychopathology or cortisol system. This step would allow the validation of the hypothesis that methylation changes are an underlying mechanism of the association between maternal depressive symptoms in pregnancy and higher risk for psychopathology in the child. If we are able to explain the association and underlying mechanisms, it will be possible to develop specific preventive or intervening treatments to support the mothers during pregnancy and protect the children from the negative impact of prenatal affective maternal problems.
Date proposal received: 
Tuesday, 20 September, 2016
Date proposal approved: 
Wednesday, 28 September, 2016
Keywords: 
Mental health - Psychology, Psychiatry, Cognition, Mental health, Pregnancy - e.g. reproductive health, postnatal depression, birth outcomes, etc., Epigenetics, Statistical methods, Biological samples -e.g. blood, cell lines, saliva, etc., Childhood - childcare, childhood adversity, Genetics - e.g. epigenetics, mendelian randomisation, UK10K, sequencing, etc., Offspring, Statistical methods

B2749 - ADHD and DNA methylation a repeated measures EWAS - 2016/28/09

B number: 
B2749
Principal applicant name: 
Esther Walton | University of Bristol - IEU
Co-applicants: 
Title of project: 
ADHD and DNA methylation: a repeated measures EWAS
Proposal summary: 

Attention-deficit/hyperactivity disorder (ADHD) is a prevalent developmental disorder, associated with a range of long-term impairments. However, the potential role of DNA methylation, an epigenetic mechanism, in ADHD symptoms is currently unclear. We plan to examine peripheral measures of DNA methylation at birth and ADHD symptoms (4–18 years) in different cohorts (GenerationR, ALSPAC, other PACE cohorts). Findings could lend novel insights into the epigenetic landscape of ADHD symptoms.

Impact of research: 
Findings could lend novel insights into the epigenetic landscape of ADHD symptoms.
Date proposal received: 
Tuesday, 20 September, 2016
Date proposal approved: 
Wednesday, 28 September, 2016
Keywords: 
Mental health - Psychology, Psychiatry, Cognition, Behaviour - e.g. antisocial behaviour, risk behaviour, etc., Developmental disorders - autism, Mental health, ADHD, Epigenetics, Development, Genetics - e.g. epigenetics, mendelian randomisation, UK10K, sequencing, etc., ADHD

B2730 - Early life predictors of child socioemotional development - 15/09/2016

B number: 
B2730
Principal applicant name: 
Carine Parent | Ludmer Centre for Neuroinformatics and Mental Health (Canada)
Co-applicants: 
Dr. Michael J. Meaney, Dr. Patricia Silveira, Dr. Helene Gaudreau
Title of project: 
Early life predictors of child socioemotional development
Proposal summary: 

Our group at the Ludmer Centre for Neuroinformatics and Mental Health at the Douglas Mental Health University Institute in Montreal, Quebec have received funding from the JPB Foundation to investigate the factors that could best predict the risk for the development of mental health problems in children and adolescents. We seek to determine how certain factors related to the maternal environment and pediatric health in early life could predict the risk for the development of socioemotional problems in children and the risk for mental health problems in adolescents. We are seeking to identify which environmental factors predict an increased risk for psychopathology or a protective influence against the development of psychopathology. If we could identify what the main risk factors or protective factors are for the development of mental illness we could intervene early in life and reduce the risk for the future development of mental health disorders in children and adolescents.

Date proposal received: 
Monday, 15 August, 2016
Date proposal approved: 
Thursday, 15 September, 2016
Keywords: 
Mental health - Psychology, Psychiatry, Cognition

B2746 - Understanding Linkage to Hospital Episode Statistics - 15/09/2016

B number: 
B2746
Principal applicant name: 
Rosie Cornish | School of Social and Community Medicine (United Kingdom)
Co-applicants: 
Andy Boyd, Professor John Macleod, Mr Leigh Johnson
Title of project: 
Understanding Linkage to Hospital Episode Statistics
Proposal summary: 
Date proposal received: 
Thursday, 8 September, 2016
Date proposal approved: 
Thursday, 15 September, 2016
Keywords: 
Data linkage and management, Linkage, Methods - e.g. cross cohort analysis, data mining, mendelian randomisation, etc.

B2747 - Application of the joint linear mixed effect IOU modelaccounting for autocorrelation and informative dropout - 15/09/2016

B number: 
B2747
Principal applicant name: 
Rachael Hughes | School of Social and Community Medicine (United Kingdom)
Co-applicants: 
Professor Kate Tilling, Professor Jonathan Sterne
Title of project: 
Application of the joint linear mixed effect IOU model:accounting for autocorrelation and informative dropout
Proposal summary: 

In normal pregnancy there is a decrease in blood pressure in early pregnancy followed by a rise in late pregnancy. Hypertensive disorders of pregnancy (HDP), defined by high blood pressure in late pregnancy (after 20 weeks’ gestation), are associated with risk of adverse health outcomes for both the mother and offspring. Using a statistical model we can describe the change in blood pressure over time during pregnancy, and, for a woman in early pregnancy, we can use this model to predict her blood pressure measurements in late pregnancy given her observed measurements.

Modelling blood pressure measurements during pregnancy may be subject to two statistical complications. First, women who give birth prematurely are more likely to have high blood pressure compared to those women who have a full term pregnancy (a statistical complication known as informative dropout). Second, the blood pressure measurements are usually measured very frequently during pregnancy, such that a woman’s blood pressure measurements may be highly correlated (a statistical complication known as serial correlation or autocorrelation). Modelling the data using a method that ignores either or both of these complications may result in incorrect conclusions about the how blood pressure changes over time during pregnancy in a given population, and affect the accuracy of any predictions.

We propose a method that can account for informative dropout and autocorrelation. Also, a useful feature of the proposed method is that it can be used to: (1) describe the association between “time to birth” and blood pressure in very early pregnancy and change in blood pressure over time. (2) Estimate the average blood pressure measurement in very early pregnancy and the average change in blood pressure over time among women with a common gestation period. And, (3) provide future predicted blood measurements and a prediction of time to birth.

Date proposal received: 
Monday, 12 September, 2016
Date proposal approved: 
Wednesday, 14 September, 2016
Keywords: 
Statistics/methodology, Hypertension, Pregnancy - e.g. reproductive health, postnatal depression, birth outcomes, etc., Statistical methods, Biomarkers - e.g. cotinine, fatty acids, haemoglobin, etc., Birth outcomes, Blood pressure, Statistical methods

B2740 - Sleep over the lifecourse influences consequences and costs - 16/09/2016

B number: 
B2740
Principal applicant name: 
Yvonne Kelly | University College London (UK)
Co-applicants: 
Amanda Sacker, Anne McMunn, Meena Kumari, Tarani Chandola, Steve Morris
Title of project: 
Sleep over the lifecourse: influences, consequences and costs
Proposal summary: 

In the UK it is estimated that 25% of adults and 20% of children experience insufficient sleep. Insufficient and/or disrupted sleep has been linked to many aspects of human health and wellbeing. Much of the evidence about sleep and health comes from small scale studies, and little is understood about factors that influence sleep in the general population, and how such influences might vary across different stages of the lifecourse. The proposed work will look at the influences on, and consequences of sleep across the lifecourse, by exploiting rich contextual, health, education and biomarker data from the UK’s longitudinal studies.

Date proposal received: 
Tuesday, 6 September, 2016
Date proposal approved: 
Wednesday, 14 September, 2016
Keywords: 
Epidemiology, Mental health, Obesity, Cognitive development and educational attainments, Statistical methods, Biomarkers - e.g. cotinine, fatty acids, haemoglobin, etc., BMI, Psychology - personality, Sleep, Social science, Statistical methods, Childhood - childcare, childhood adversity, Cognition - cognitive function, Environment - enviromental exposure, pollution, Growth, Hormones - cortisol, IGF, thyroid, Intelligence - memory, Methods - e.g. cross cohort analysis, data mining, mendelian randomisation, etc., Parenting

B2739 - An atlas of phenotypic correlations and a correction of multiple testing across human traits and diseases using GWAS summary sta - 08/09/2016

B number: 
B2739
Principal applicant name: 
JIE ZHENG | (United Kingdom)
Co-applicants: 
Dr. Tom Gaunt
Title of project: 
An atlas of phenotypic correlations and a correction of multiple testing across human traits and diseases using GWAS summary sta
Proposal summary: 

Identifying phenotypic correlations between complex traits and diseases can provide useful etiological insights and help correct multiple testing. Lack of centralized individual-level phenotypes database makes it almost impossible to estimate phenotypic correlations across human traits and diseases as a whole picture. A useful alternative is to use the genome-wide association study (GWAS) summary statistics to estimate phenotypic correlations via the method metaCCA. We applied this method to the centralized GWAS summary results database we created to estimate 358,281 phenotypic correlations among 846 traits and diseases. The atlas of phenotypic correlations systematically scan hypotheses across a large scale of human traits and diseases, which can further tested using methods such as Mendelian randomization, LD score regression and PheWAS. The matrix also informed the selection of covariates for genetic, epigenetic and epidemiology analysis. In addition, we compared the phenotypic correlation and genetic correlation amongst 173 traits. The results of metaSpD suggest a 562 number of independent variable across 846 traits and diseases (P-value threshold of 9e-05). Additionally, metaSpD includes principal-component analysis which enables selection of a subset of traits in a complex molecular network, e.g. metabolites.

Date proposal received: 
Thursday, 1 September, 2016
Date proposal approved: 
Thursday, 8 September, 2016
Keywords: 
Epidemiology

B2737 - MRC IEU DNA methylation as a potential mediating mechanism linking early life events and subsequent obesity - 08/09/2016

B number: 
B2737
Principal applicant name: 
Natassia Robinson | Newcatle University
Co-applicants: 
Prof Caroline Relton
Title of project: 
MRC IEU: DNA methylation as a potential mediating mechanism linking early life events and subsequent obesity
Proposal summary: 

Genetic factors cannot exclusively explain the recent rapid increase in obesity; its aetiology is likely a multi-faceted and complex mix of genes and environment. A proposed mechanism is the establishment of epigenetic patterns early in development, known as developmental programming. One epigenetic modification, DNA methylation, can modulate gene expression and can be influenced by environment factors i.e. diet and lifestyle. Thereby there is potential for DNA methylation to be a mediating mechanism in a disease such as obesity. Life-course epidemiological data from three birth cohorts will be used to investigate if this epigenetic marker could mediate early life risk factors and obesity.

Date proposal received: 
Wednesday, 31 August, 2016
Date proposal approved: 
Thursday, 8 September, 2016
Keywords: 
Genetics, Obesity, Epigenetics, Statistical methods, BMI, Childhood - childcare, childhood adversity, Genetics - e.g. epigenetics, mendelian randomisation, UK10K, sequencing, etc., Nutrition - breast feeding, diet

B2554 - Identifying patterns in accelerometer data and investigating their association with other factors - 09/09/2016

B number: 
B2554
Principal applicant name: 
Louise Millard | Integrative Epidemiology Unit, UoB (Bristol)
Co-applicants: 
Dr Tom Gaunt
Title of project: 
Identifying patterns in accelerometer data, and investigating their association with other factors
Proposal summary: 

ALSPAC includes Actigraph accelerometer data for approximately 5.5K and 3.2K participants at age 11 and 14 respectively. To date the data recorded by the accelerometer has been used to generate only a small number of phenotypes – moderate to vigorous physical activity (MVPA) and average counts per minute (CPM), a measure of total activity. There is much potential to extract other useful patterns from this data that may be 1) risk factors for 2) causally affected by or 3) causally affect, other phenotypes.

We will seek to identify common patterns in the accelerometer data and then investigate their relationship with other factors.

Date proposal received: 
Friday, 9 October, 2015
Date proposal approved: 
Wednesday, 7 September, 2016
Keywords: 
Statistics/methodology, Statistical methods

B2744 - Age estimation of an adolescent population - 08/09/2016

B number: 
B2744
Principal applicant name: 
Pål Skage | National Insitute of Public Health (Norway)
Co-applicants: 
Oyvind Bleka
Title of project: 
Age estimation of an adolescent population
Proposal summary: 

The aim of the Project is to predict age by measuring the degree of methylation at selected sites of the human DNA.

Date proposal received: 
Wednesday, 7 September, 2016
Date proposal approved: 
Wednesday, 7 September, 2016
Keywords: 
Bioinformatics, We aim to estimate chronological age of adolescents of unknown age. This is of great importance faced With Young unaccompanied immigrants that do not know their true age or can not document it. It is also important for the judicial system, where age is a Critical factor. Further, there is a need in international sports to estimate age in cases of doubt., Epigenetics, Statistical methods, Ageing, Biological samples -e.g. blood, cell lines, saliva, etc., Genetics - e.g. epigenetics, mendelian randomisation, UK10K, sequencing, etc., Statistical methods

Pages