D2K, PEALS, Newcastle University

ELSA: METADAC project approvals

Last updated 20/06/2019

Project Title Plain Language Summary Keywords Applicant Meeting date
Affiliation Decision Letter
Co-Applicants Final Approval
Funders  
Developing risk stratification models for identifying sub-groups of individuals who are at high risk of short-to-long term cognitive decline or dementia It is widely believed that treating Alzheimer’s disease early on before symptoms arise has the best chance of halting or slowing progression of the disease. We aim to use statistical methods to identify characteristics of individuals that may indicate an increased chance of experiencing decline in cognitive functions, such as in memory and problem solving, beyond that due to normal ageing. One possible characteristic could be genetic variants (i.e. bits of DNA that differ between people). We plan to use genetic data and cognitive function measurements from the ELSA Study to attempt to identify such genetic variants. A potential benefit of this work would be to help prioritize future patient recruitment into clinical trials of new Alzheimer’s disease drugs that target early stage of disease. Risk stratification, Risk prediction, Longitudinal modelling, Dementia diagnosis, Cognitive decline TOM, Dr Brian 30 Jan 2019
Dr Steven Hill, Dr Anais Rouanet and Dr Mary Fortune 13 Feb 2019
Medical Research Council 25 April 2019
Gene-Environment Interplay in the Generation of Health and Education Inequalities (GEIGHEI) Both genetic and environmental factors can influence educational attainment and unhealthy behaviours such as smoking, drinking, and obesity. These are the three leading causes of preventable death in countries belonging to the Organisation for Economic Cooperation and Development (OECD). Early-life conditions have a particular influence on these adult outcomes. Although genes cannot be changed, nurturing childhood environments could substantially offset genetic variations. Such childhood environments can be improved by policy interventions such as child care subsidies or paid parental leave. Based on recent advances in collecting and analysing genetic data, we will investigate how genetic predispositions and their interplay with the environment can influence educational attainment and unhealthy behaviours. Additionally, we will look at the influence of these factors on health and socioeconomic inequality in the long run. GxE, education, obesity, smoking, genetics BIROLI, Dr Pietro 24 April 2018
Hans Van Kippersluis, Stephanie von Hinke Kessler Scholder, Amr Elriedy and Chris Zuend 3 May 2018
NORFACE, European Union 24 July 2018
Genetic determinants of iron stores and their association with health outcomes Iron is an essential element involved in a variety of biological processes. It is tightly regulated to ensure a balance between dietary absorption, transport, storage, and use to maintain stable healthy conditions (homeostasis). Blood donors are at an increased risk of iron deficiency due to repeated donation. Understanding the factors affecting an individuals’ iron stores may help stop this occurring. Ferritin (a major iron storage protein that reflects the body’s total iron stores) has been measured in the English Longitudinal Study of Ageing and we will use this, with similar data from other studies, to uncover genetic variants (natural differences in DNA between people) associated with iron levels. We will use the findings from this work to examine how “genetic iron scores” predict health outcomes of major importance to public health such as cardiovascular and neurological diseases, as well as different types of cancer. iron, ferritin, genetic, risk, blood DI ANGELANTONIO , Dr Emanuele 22 Jan 2018
University of Cambridge 02 Feb 2018
Dr Steven Bell, Dr Adam Butterworth, Prof Nicole Soranzo, Prof David Batty, Prof David Roberts, Prof Willem Ouwehand, Prof John Danesh 28 Feb 2018
The National Institute of Health Research (Blood and Transplant)  
The effect of obesity and heart disease on quality of life. Long-term conditions such as obesity and heart disease seem to affect patient quality of life. However, it is difficult to establish if each condition itself has the biggest impact on quality of life, or whether other circumstances are more important. For example, patients with one condition may have other conditions that separately influence quality of life. This project will use information on the relationship between genetic variants and health conditions to avoid this problem. Genetic variants refer to pieces of the genetic code that differ among individuals. Some variants are known to influence health conditions, and may be unrelated to other conditions or patient characteristics that might affect quality of life. This project will use these methodologies – known as Mendelian Randomization – to study the effect of obesity and heart disease on quality of life, measured in ELSA using the CASP-19 questionnaire. Results will be compared to conventional analyses Mendelian Randomisation; quality of life; obesity; coronary artery disease DIXON, Dr Padraig 27 Nov 2017
University of Bristol 05 Dec 2018
N/A 02 Feb 2018
MRC  
Impact of Population Ageing and Prevalence of Chronic Diseases on Labour Market Outcomes, health service utilization, and Social Welfare: A Genetic Assessment Population ageing and the associated high prevalence of chronic diseases, such as diabetes, cardiovascular disease, cancer and mental illnesses, have brought about a great loss in labour force production and challenges to social welfare systems almost everywhere including the UK. In this study, using the data from ELSA we apply a range of state-of-the-art statistical methods to examine the impacts of ageing and chronic illness on individual’s use of health services, decisions on employment, and receipt of social welfare benefits. We also examine the impacts of healthy lifestyle and family and community support on these individual behaviours, taking account of individual genetic information. ELSA is a unique and rich resource of information examined in this study, and it is crucial to this study. Results from this study can provide guidance to both individual elderly people and policy makers. Population ageing, chronic diseases, labour force participation, health service utilization, social welfare, instrumental variable estimation, single nucleotide polymorphism (SNP), polygenic scores, varying-coefficient model, semiparametric method ZHANG , Prof Xaiohui 12 Sept 2017
University of Exeter 19 Sept 2017
Bin Peng, Dr Jess Tyrrell 13 Nov 2017
ESRC (submitted)  
Investigating the genetic relationships between depression and cardiovascular risk factors and disease. This study aims to use cardiovascular risk measurements and diagnosis, together with questionnaire data on mental and physical health in ELSA in two ways: 1- To discover and validate previous findings from large psychiatric genetics studies. These studies identified inherited genetic changes which may increase risk of depression. 2- To investigate shared genetic factors affecting mental and cardiovascular health (heart disease), as these conditions often occur together.

The ultimate aim is to uncover biological pathways underlying the relationship between cardiovascular disease and depression. The proposed work will be achieved through analysis of genetic and health data, using existing methods. This research will assist in risk prediction, informing treatment, and forming a better understanding of the shared genetics between traits.

Depression, polygenic risk scores, cardiovascular disease risk, pleiotropy. LEWIS, Prof Cathryn 25 May 2017
King’s College London 08 June 2017
Gerome Breen, Paul O’reilly, Delilah Zabeneh, Saskia Hagenaars, Karen Hodgson 09 Aug 2017
NIHR Maudsley Biomedical Centre  
Investigation of the genetic overlap between health literacy, cognitive function, education and health outcomes. Health literacy is the ability to understand and use health information to make decisions relating to one’s own health.  Individuals with lower health literacy are more likely to suffer from chronic diseases, such as type 2 diabetes. Cognitive functions, such as memory and problem solving, are also strongly related to health literacy. Individuals with higher health literacy tend to score higher on tests of cognitive function. This has led some researchers to propose that health literacy and cognitive function overlap and are measuring the same skill. To investigate why there is an overlap between health literacy and cognitive function, this project will examine whether the genes that are associated with health literacy are also associated with cognitive function. If there is a large overlap in the genes involved in these two skills, it will provide further support that health literacy and cognitive function measures assess the same underlying ability. Health literacy, cognitive function, genetics, pleiotropy DEARY, Prof Ian 27 Feb 2017
University of Edinburgh 07 Mar 2017
Ms Chloe Fawns-Ritchie; Dr Gail Davies, Dr W David and Dr Sarah Harris 22 Mar 2017
Centre for cognitive ageing and cognitive epidemiology  
Genetic Analyses of Risk Preferences We, the Social Science Genetic Association Consortium (SSGAC), aim to bring together the expertise of geneticists and social scientists to study the genetic architecture of outcomes that are of interest to social scientists (e.g. subjective well-being, risk tolerance, etc.). Risk tolerance—or the willingness to take risks to obtain rewards–is an important concept for a wide range of models in all branches of economics.

Measures of risk tolerance have been shown to predict a wide range of economic and social behaviors, such as portfolio allocation and occupational choice, as well as important health related behaviors, such as smoking cigarettes and drinking alcohol. It has also been shown that genetic factors account for some of the variation in risk tolerance. We will use the ELSA data to pursue discovery of particular genetic variants that are associated with risk tolerance, and to study the extent to which a polygenic score for risk tolerance to predict other outcomes available in the ELSA data.

GWAS, risk, social science genomics KOELLINGER, Prof Philipp 30 Nov 2016
Vrije Universiteit Amsterdam 14 Dec 2016
Fleur Meddens, Richard Karlsson Linner, Aysu Okbay, de Vlaming, Ronald de Vlaming and Niels Rietveld 15 Mar 2017
European Research Council  
Increasing the Power of GWAS Through Multi-Trait Meta-Analysis: Application to Depressive Symptoms, Neuroticism, and Subjective Well-Being There are many human traits that are of interest scientifically but for which sample sizes are too small to produce reliable results in a genetic study. For this project, we have developed a statistical approach to optimally extract information from a set of related traits to improve the reliability of analyses of the trait of interest. An advantage of our method is that it can be applied to results from existing genetic studies, which are nearly always publicly available. We will illustrate the power of this method to jointly analyze subjective well-being, depressive symptoms, and neuroticism. Measures of these traits are available in the ELSA data. We plan to combine results from ELSA with published and newly-collected data for each trait, and we will then jointly analyze all three traits using our proposed method. Preliminary results have shown dramatic improvements in the precision and reliability by using this method. GWAS, health, behaviours, mental health, social science genomics, depressive symptoms, neuroticism, subjective well-being KOELLINGER, Prof Philipp 30 Nov 2016
Vrije Universiteit Amsterdam 14 Dec 2016
Fleur Meddens, Richard Karlsson Linner, Aysu Okbay, de Vlaming, Ronald de Vlaming and Niels Rietveld 15 Mar 2017
European Research Council  
Genetic Analyses of Educational Attainment We, the Social Science Genetic Association Consortium (SSGAC), aim to bring together the expertise of geneticists and social scientists to study the genetic architecture of outcomes that are of interest to social scientists (e.g. subjective well-being, risk tolerance, etc.). Specifically, we study how such outcomes are influenced by specific genetic variants, the environment (including lifestyle), and their interaction.

We will use the ELSA genetics and survey data to pursue discovery of particular genetic variants that are associated with educational attainment. Prior research has established that there is considerable overlap between genetic variants associated with educational attainment and those with cognitive function and (absence of) dementia, we will further exploit the uniquely rich data in the ELSA through analyses that will shed light on the genetics of these outcomes and several others related to educational attainment.

GWAS, education, cognitive function, dementia, social science genomics KOELLINGER, Prof Philipp 30 Nov 2016
Vrije Universiteit Amsterdam 14 Dec 2016
Fleur Meddens, Richard Karlsson Linner, Aysu Okbay, de Vlaming, Ronald de Vlaming and Niels Rietveld 15 Mar 2017
European Research Council  
Genetic Analyses of Risk Preferences We, the Social Science Genetic Association Consortium (SSGAC), aim to bring together the expertise of geneticists and social scientists to study the genetic architecture of outcomes that are of interest to social scientists (e.g. subjective well-being, risk tolerance, etc.). Risk tolerance—or the willingness to take risks to obtain rewards–is an important concept for a wide range of models in all branches of economics.

Measures of risk tolerance have been shown to predict a wide range of economic and social behaviors, such as portfolio allocation and occupational choice, as well as important health related behaviors, such as smoking cigarettes and drinking alcohol. It has also been shown that genetic factors account for some of the variation in risk tolerance. We will use the ELSA data to pursue discovery of particular genetic variants that are associated with risk tolerance, and to study the extent to which a polygenic score for risk tolerance to predict other outcomes available in the ELSA data.

GWAS, risk, social science genomics BENJAMIN, Prof Daniel 30 Nov 2016
University of Southern California 14 Dec 2016
558-57. A WATSON; A OKBAY; C BURIK; D CESARINI; D CONLEY; E KONG; J LEE; J YANG; J BEAUCHAMP; K THOM;  M FONTANA; M ZACHER; O MAGHZIAN; P TURLEY; P VISSCHER; P KOELLINGER; R WEDOW; S OSKARSSON; T NGUYEN.N; J BECKER; R ROYER; M ROBINSON 15 Mar 2017
National Institutes of Health (NIH)  
Increasing the Power of GWAS Through Multi-Trait Meta-Analysis: Application to Depressive Symptoms, Neuroticism, and Subjective Well-Being There are many human traits that are of interest scientifically but for which sample sizes are too small to produce reliable results in a genetic study. For this project, we have developed a statistical approach to optimally extract information from a set of related traits to improve the reliability of analyses of the trait of interest. An advantage of our method is that it can be applied to results from existing genetic studies, which are nearly always publicly available. We will illustrate the power of this method to jointly analyze subjective well-being, depressive symptoms, and neuroticism. Measures of these traits are available in the ELSA data. We plan to combine results from ELSA with published and newly-collected data for each trait, and we will then jointly analyze all three traits using our proposed method. Preliminary results have shown dramatic improvements in the precision and reliability by using this method. GWAS, health, behaviours, mental health, social science genomics, depressive symptoms, neuroticism, subjective well-being BENJAMIN, Prof Daniel 30 Nov 2016
University of Southern California 14 Dec 2016
558-57. A WATSON; A OKBAY; C BURIK; D CESARINI; D CONLEY; E KONG; J LEE; J YANG; J BEAUCHAMP; K THOM;  M FONTANA; M ZACHER; O MAGHZIAN; P TURLEY; P VISSCHER; P KOELLINGER; R WEDOW; S OSKARSSON; T NGUYEN.N; J BECKER; R ROYER; M ROBINSON 15 Mar 2017
National Institutes of Health (NIH)  
Genetic Analyses of Educational Attainment We, the Social Science Genetic Association Consortium (SSGAC), aim to bring together the expertise of geneticists and social scientists to study the genetic architecture of outcomes that are of interest to social scientists (e.g. subjective well-being, risk tolerance, etc.). Specifically, we study how such outcomes are influenced by specific genetic variants, the environment (including lifestyle), and their interaction.

We will use the ELSA genetics and survey data to pursue discovery of particular genetic variants that are associated with educational attainment. Prior research has established that there is considerable overlap between genetic variants associated with educational attainment and those with cognitive function and (absence of) dementia, we will further exploit the uniquely rich data in the ELSA through analyses that will shed light on the genetics of these outcomes and several others related to educational attainment.

GWAS, education, cognitive function, dementia, social science genomics BENJAMIN, Prof Daniel 30 Nov 2016
University of Southern California 14 Dec 2016
558-57. A WATSON; A OKBAY; C BURIK; D CESARINI; D CONLEY; E KONG; J LEE; J YANG; J BEAUCHAMP; K THOM;  M FONTANA; M ZACHER; O MAGHZIAN; P TURLEY; P VISSCHER; P KOELLINGER; R WEDOW; S OSKARSSON; T NGUYEN.N; J BECKER; R ROYER; M ROBINSON 15 Mar 2017
National Institutes of Health (NIH)  
The genetic basis of the height premium We analyze the contribution of height towards economic performance. Many studies have reported a significant positive correlation between height and outcomes such as educational attainment, earnings and productivity. No earlier study investigated this question from a genetic perspective. In genetic data from the Health and Retirement Study we do find a significant correlation between a polygenic risk score for height and outcomes such as education and earnings. Our plan is to replicate these findings in the English Longitudinal Study of Ageing dataset. Height, Earnings, Education, Ability, Polygenic risk score REITVELD, Prof Niels 30 Nov 2016
Erasmus University Rotterdam 14 Dec 2016
Prof Dinand WEBBINK, Hans VAN KIPPERSLUIS, Eric SLOB 06 Apr 2017
n/a