D2K, PEALS, Newcastle University

ELSA: METADAC project approvals

Title of Application Plain Language Summary DAC date;
Decision notified;
Final approval
Principal Applicant;
Principal Institution;
Co-applicants;
Funder
Project Summary Keywords
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. 30-Nov-16 REITVELD, Prof Niels Height, Earnings, Education, Ability, Polygenic risk score
14-Dec-16 Erasmus University Rotterdam
06-Apr-16 Prof Dinand WEBBINK, Hans VAN KIPPERSLUIS, Eric SLOB
n/a
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 how a range of social scientific 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 education attainment, through (the third phase of) a large–‐scale genome–‐wide association study (GWAS) meta–‐analysis of educational attainment (EA) as a proxy–‐phenotype for cognitive function and (absence of) dementia.  Additionally, we will exploit the uniquely rich data in the ELSA through analyses that will shed light on the molecular genetic architecture of outcomes related to educational attainment. 30-Nov-16 BENJAMIN, Prof Daniel GWAS, education, cognitive function, dementia, social science genomics
14-Dec-16 University of Southern California
15-Mar-17 (558-578) WATSON, C; OKBAY, A; BURIK, C; CESARINI, D; CONLEY, D; KONG, E; LEE, J; YANG, J; BEAUCHAMP, J; THOM, K; FONTANA, M; ROBINSON, M; ZACHER, M; MAGHZIAN, O; TURLEY, P; VISSCHER, P; KOELLINGER, P; ROYER, R; WEDOW, R; OSKARSSON, S; NGUYEN, T
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 related phenotypes which are of great interest scientifically, though it is standard to analyze them separately. This method asks the question of how to optimally analyze sets of genetically correlated phenotypes. Our proposed method increases power for locus detection and prediction without having to gather new data and also sheds light on which regions of the genome are important to one, several or all of the phenotypes being considered. This will allow researchers to better disentangle the biological underpinnings driving the relationship between phenotypes. We will apply the method to the phenotypes subjective well-being, depressive symptoms, and neuroticism. In our preliminary analyses, applying this method to our existing data increased the predictive power of our polygenic score by 20% and almost doubled the number of genome‐ wide significant hits. We hope that widespread application of this method will result in similar gains across a number of phenotypes. 30-Nov-16 BENJAMIN, Prof Daniel GWAS, health, behaviours, mental health, social science genomics, depressive symptoms, neuroticism, subjective well-being
14-Dec-16 University of Southern California
15-Mar-17 (558-578) WATSON, C; OKBAY, A; BURIK, C; CESARINI, D; CONLEY, D; KONG, E; LEE, J; YANG, J; BEAUCHAMP, J; THOM, K; FONTANA, M; ROBINSON, M; ZACHER, M; MAGHZIAN, O; TURLEY, P; VISSCHER, P; KOELLINGER, P; ROYER, R; WEDOW, R; OSKARSSON, S; NGUYEN, T
National Institutes of Health (NIH)
Genetic Analyses of Risk Preferences We, the Social Science Genetic Association, bring together geneticists and social scientists to study how social scientific outcomes are influenced by specific genetic variants, the environment and their interaction. Risk preferences are a fundamental structural parameter in a wide range of models in all branches of economics. Measures of risk preferences 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. Risk preferences have been found to be moderately heritable, with estimates of their heritability ranging from as low as ~20% or as high as ~60%. We will use the ELSA data to pursue discovery of particular genetic variants that are associated with risk preferences, and to study the predictive power of a polygenic score for risk tolerance for other outcomes available in the ELSA data. 30-Nov-16 BENJAMIN, Prof Daniel GWAS, risk, social science genomics
14-Dec-16 University of Southern California
15-Mar-17 (558-578) WATSON, C; OKBAY, A; BURIK, C; CESARINI, D; CONLEY, D; KONG, E; LEE, J; YANG, J; BEAUCHAMP, J; THOM, K; FONTANA, M; ROBINSON, M; ZACHER, M; MAGHZIAN, O; TURLEY, P; VISSCHER, P; KOELLINGER, P; ROYER, R; WEDOW, R; OSKARSSON, S; NGUYEN, T
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 how a range of social scientific 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 education attainment, through (the third phase of) a large–‐scale genome–‐wide association study (GWAS) meta–‐analysis of educational attainment (EA) as a proxy–‐phenotype for cognitive function and (absence of) dementia.  Additionally, we will exploit the uniquely rich data in the ELSA through analyses that will shed light on the molecular genetic architecture of outcomes related to educational attainment. 30-Nov-16 KOELLINGER, Prof Philipp GWAS, education, cognitive function, dementia, social science genomics
14-Dec-16 Vrije Universiteit Amsterdam
15-Mar-17 MEDDENS, Fleur; KARLSSON LINNER, Richard; OKBAY, Aysu; de VLAMING, Ronald; RIETVELD, Niels
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 related phenotypes which are of great interest scientifically, though it is standard to analyze them separately. This method asks the question of how to optimally analyze sets of genetically correlated phenotypes. Our proposed method increases power for locus detection and prediction without having to gather new data and also sheds light on which regions of the genome are important to one, several or all of the phenotypes being considered. This will allow researchers to better disentangle the biological underpinnings driving the relationship between phenotypes. We will apply the method to the phenotypes subjective well-being, depressive symptoms, and neuroticism. In our preliminary analyses, applying this method to our existing data increased the predictive power of our polygenic score by 20% and almost doubled the number of genome‐ wide significant hits. We hope that widespread application of this method will result in similar gains across a number of phenotypes. 30-Nov-16 KOELLINGER, Prof Philipp GWAS, health, behaviours, mental health, social science genomics, depressive symptoms, neuroticism, subjective well-being
14-Dec-16 Vrije Universiteit Amsterdam
15-Mar-17 MEDDENS, Fleur; KARLSSON LINNER, Richard; OKBAY, Aysu; de VLAMING, Ronald; RIETVELD, Niels
National Institutes of Health (NIH)
Genetic Analyses of Risk Preferences We, the Social Science Genetic Association, bring together geneticists and social scientists to study how social scientific outcomes are influenced by specific genetic variants, the environment and their interaction. Risk preferences are a fundamental structural parameter in a wide range of models in all branches of economics. Measures of risk preferences 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. Risk preferences have been found to be moderately heritable, with estimates of their heritability ranging from as low as ~20% or as high as ~60%. We will use the ELSA data to pursue discovery of particular genetic variants that are associated with risk preferences, and to study the predictive power of a polygenic score for risk tolerance for other outcomes available in the ELSA data. 30-Nov-16 KOELLINGER, Prof Philipp GWAS, risk, social science genomics
14-Dec-16 Vrije Universiteit Amsterdam
15-Mar-17 MEDDENS, Fleur; KARLSSON LINNER, Richard; OKBAY, Aysu; de VLAMING, Ronald; RIETVELD, Niels
National Institutes of Health (NIH)
Genetic studies of mental health and dementia-related phenotypes in ELSA Genome-wide association studies are a relatively new way for scientists to investigate genes that contribute to human health. It involves scanning a large number of genetic variants to see which ones are more common in people with particular diseases. Each study can investigate hundreds of thousands of variants at the same time, and this kind of study can be helpful to identify genes that may contribute to certain diseases. This approach has already been applied to investigate aspects of mental health and dementia, including depression and Alzheimer’s disease. We hope that future studies incorporating ELSA data will identify additional genetic variants, as well as giving us clues as to the causes of these important conditions. 27-Feb-17 LLEWELLYN, Dr David James GWAS, mental health, dementia, alzheimer’s, depression
07-Mar-17 University of Exeter
n/a STEPTOE, Prof Andrew; LOURIDA, Dr Ilianna; KUZMA, Dr Elzbieta; MILL, Prof Jonathan; HANNON, Dr Eilis; WEEDON, Prof Michael
n/a
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. 27-Feb-17 DEARY, Prof Ian Health literacy, cognitive function, genetics, pleiotropy
07-Mar-17 University of Edinburgh
22-03-17 FAWNS-RITCHIE, Ms Chloe; DAVIES, Dr Gail; HILL, Dr W David; HARRIS, Dr Sarah
Centre for cognitive ageing and cognitive epidemiology
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