Last updated 24/02/2020
| Project Title | Plain Language Summary | Keywords | Applicant | Meeting date |
| Affiliation | Decision Letter | |||
| Co-Applicants | Final Approval | |||
| Funders | ||||
| A holistic statistical approach for determining the relationships between social, economic and health markers using the English Longitudinal Study of Ageing | The elderly population faced a steep increase over the last decades and it is expected to grow even more in the upcoming years. A better understanding of the different needs of the elderly is essential to be able to sustain the societal well-being and maintain the overall quality of life. This research aims to contribute to this by identifying the factors affecting healthy ageing, i.e. getting older while maintaining fitness and well-being. We aim to use the English Longitudinal Study of Ageing data to identify these factors. Apart from demographic characteristics, it has been found that genetic factors could potentially account for some of the variation within the ageing population. We will develop and test statistical modelling approaches aiming to identify how demographic characteristics such as gender and education and people’s genetic makeup influence healthy ageing over time. We plan to measure healthy ageing using five indexes constructed using the ELSA data. Relationships between these indexes will be additionally investigated over time. Our new statistical methodology would help us to provide a more comprehensive picture on the healthy ageing and our findings could provide a quantitative evidence towards assessing the future needs to deal with the ageing population. | ageing process, individual heterogeneity, healthy ageing phenotype, English Longitudinal Study of Ageing, non-parametric, longitudinal data analysis, genome wide microarray assay | PARK, Dr Juhyun | 11 November 2019 |
| Frank Dondelinger
Evanthia Koukouli |
19 December 2019 | |||
| University of Lancaster | 14 December 2019 | |||
| North Western Social Science Doctoral Training Programme part of the Economic and Social Research Council | ||||
| Investigating the genetics of cognitive resilience in healthy ageing | As we grow older most people find it harder to complete tasks that involve mental ability. This can affect our quality of life. Our ability to withstand the effects of age on our mental ability is called cognitive resilience. Some people show better resilience than others. We know that one of the reasons for this is the genes they inherited from their parents. If we can identify these genes, we can have a better understanding of the ageing process. This knowledge may help people improve their quality of life. We have started by looking at The UK biobank which has 500,000 people whose genes and mental ability were tested. This was our first step to identify the genes involved in cognitive resilience. We need the ELSA data to confirm our findings as it has excellent measures of mental ability over a time. | Cognition, resilience, genetics | MORRIS, Dr Derek | 12 November 2019 |
| Prof. Gary Donohoe Ms Joan Fitzgerald Ms Laura Fahey | 28 November 2019 | |||
| National University Ireland Galway | 28 November 2019 | |||
| NUIG Research | ||||
| The genetic epidemiology between osteoporosis and arthritic diseases | The main aim of this project is to clarify the genetic relationship between osteoporosis, a bone disease, and two arthritic joint diseases (rheumatoid arthritis and osteoarthritis). The relationship between osteoporosis and rheumatoid arthritis is well researched. Patients with rheumatoid arthritis commonly have lower bone mineral density, an attribute of osteoporosis. On the other hand, the relationship between osteoporosis and osteoarthritis remains unclear. This relationship varies by skeletal site and measurement tools. Regarding the relationship between osteoporosis and both arthritic diseases, the process and the extent of the genetic relationship remains unclear. Thus, the aim of this project is to clarify the extent of the genetic component of arthritic diseases and osteoporosis. Additionally, this project aims to clarify and compare the potential genetic relationship between the two arthritic diseases and attributes of osteoporosis, namely bone mineral density. | Rheumatoid Arthritis, Osteoarthritis, Osteoporosis, Bone mineral density, Genetics | LIVSHITS, Dr Gregory | 12 September 2019 |
| Dr Stacey Cherny Melody Kasher | ||||
| Tel Aviv University | ||||
| Israel Science Foundation | ||||
| 30 September 2019 | ||||
| 30 September 2019 | ||||
| Is there a sex difference in genetic predisposition to chronic back pain? | We know from many studies that women have back pain more often than men. Women are also more likely to have back pain for a long time. This is partly because of differences between men and women in weight, type of job and other factors. However, even if we take all these things into account, women are still more affected by back pain than men. We think this may be because of genetics. Previous studies of back pain in twins provided some evidence that genes may affect back pain in men and women differently. In this study, we will analyse millions of variants in genes across our DNA. We will check what variants are associated with back pain in women and in men. This will help us confirm if there are genetic variants that could potentially lead to the difference in the risk of back pain in men and women. | Back pain, Genetics, Genome-wide association study, SNP-by-sex interaction | FREYDIN, Dr Maxim | 23 July 2019 |
| Prof. Frances Williams | 12 September 2019 | |||
| King’s College London | 30 September 2019 | |||
| N/A | ||||
| 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 |
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