Last updated 24/02/2020
| Project Title | Plain Language Summary | Keywords | Applicant | Meeting date |
| Affiliation | Decision letter | |||
| Co-applicants | Final approval | |||
| Funders | (mm/dd/yyyy) | |||
| Analysis of Biological Aging in Understanding Society II. Etiological Analysis | Humans age at different rates. Some people stay healthy through their 80s and 90s, others die of chronic disease in their 50s and 60s or spend their last decades frail and disabled. Recently, biological theories of aging have evolved. Instead of aging being the outcome of later-life disease, aging is now thought of as a cause – perhaps the most important cause – of chronic disease, disability, and death. New therapies pioneered in animals appear to make the aging process treatable. If human aging could be slowed, it would delay onset of disease and disability, giving us all more healthy years to enjoy. This possibility of prolonging healthy lifespan is critically important in light of an aging global population. To test these new therapies in humans, we need measures that can tell us if the therapies slow aging. In our proposal, we will test if several proposed measures could suit this purpose. | Aging, Biological Aging, Methylation | BELSKY, Professor Daniel | 12 November 2019 |
| Dr David Corcoran
Joseph Prinz Karen Sugden Avshalom Caspi Jonathan Mill |
26 November 2019 | |||
| Columbia University Mailman School of Public Health | 26 November 2019 | |||
| US National Institute on Aging | ||||
| Gene-environment correlations in mental health | It has been suggested that our genes don’t just influence our mental health and wellbeing, but also have an effect on the types of environments we are exposed to (such as whether we smoke or the amount of social support we receive from friends and family). Until recently, the only evidence we had to support this view was from analyses of twins. However, we can now test this idea in unrelated individuals using their genetic data. We will explore whether genes that have been linked to mental health and wellbeing are also linked with the positive and negative experiences reported by the Understanding Society participants throughout their lives. Studying the link between genes and positive and negative experiences helps us to understand how genes influence our mental health and wellbeing and may uncover new targets for the treatment or prevention of mental health problems. | Gene-environment interplay, gene-environment correlation, psychology, mental health | KEERS, Dr Robert | 12 September 2019 |
| Sandra Machlitt-Northen | 29 September 2019 | |||
| Queen Mary University London | 26 November 2019 | |||
| N/A | ||||
| Age Acceleration and the Life Course | Populations around the world are ageing rapidly, increasing pressure on public finances. But is all ageing the same? The process of ageing includes biological changes at the molecular level. We will use the most accurate indicator of ageing, the “epigenetic clock”, which is based on age-related changes to the structure of our DNA. Over time, the epigenetic clock adds chemical markers (called methylation) to various locations on our DNA. This biological clock can tick faster or slower in different individuals so that some people are indeed older or younger than they look. We will look at how the social and economic environment as well as personal characteristics help determine rapid or slow ageing. And, since the biological process of becoming older is reversible, perhaps policy interventions can then help people to “age better” by slowing the ageing of their body. | Socio‐economic status, life events, ageing, epigenetics, age acceleration. | D’AMBROSIO, Professor Conchita | 21 May 2019 |
| Dr. Jonathan Turner
Prof. Claus Vögele Prof. Andrew Clark Prof. Martin Diewald Prof. Simone Ghislandi |
04 June 2019 | |||
| University of Luxembourg | 07 October 2019 | |||
| National Research Fund of Luxembourg | ||||
| The Role of Parental Health Behaviours and Genetic Background on Children’s Health | The study will learn how children’s health can be influenced by their parents. We will use data from the Understanding Society survey. We will look at the genetic background and health behaviour of parents. Health behaviour can include smoking, obesity, and the amount of alcohol that someone drinks. We will also consider the social and economic background of the families. This will include information such as income and education of parents.
We will link this data to the health information of teenage children. This will allow us to find out which parental factors most affect a young person’s health. These links can help explain the general causes of health inequality and may inform national policies. |
Genes, Parental education, Health behaviour, Intergenerational relationship | MCNAMEE, Professor Paul | 21 May 2019 |
| Attakrit Leckcivilize
Zofia Miedzybrodzka |
04 June 2019 | |||
| University of Aberdeen | 28/01/2020 | |||
| N/A | ||||
| Do spouses share similar genetics predicting their attitude to risky decisions? | Spouses tend to have similar attitudes to risk. Household decision-making is affected by these risk attitudes, and these decisions in turn lead to macroeconomic consequences like different couples having different levels of savings. So, similar risk attitudes lead to larger differences in financial (and other) outcomes between couples who accept a lot of risk, and those who are more cautious. It is important to understand why spouses are similar in this way.
Using recent work which has created a “polygenic score” for risk attitude – a summary of people’s attitude to risk, as predicted by their genetics – we will test whether spouses have similar genetics for risk attitudes. This will help us to understand why spouses are similar in their attitudes to risk.
Showing that spouses are correlated at genetic level would demonstrate that part of the observed correlation comes from “assortative mating” – i.e. people with similar attitudes tend to marry each other and/or live together – rather than e.g. from spouses becoming more like each other over time. It will also help to explain how different risk attitudes are passed down the generations. |
Risk, GWAS, polygenic score, assortative mating | HUGH-JONES, Dr David | 21 May 2019 |
| Profesor Peter Moffatt
Abdel Abdellaoui |
4 June 2019 | |||
| University of East Anglia | 12 June 2019 | |||
| N/A | ||||
| Linking genetics and environment through epigenetics as a key to understanding different parenting styles
|
Parenting, taking care of a child, is one of the most important relationships and social responsibilities we have during our lives. Researchers found three main styles of parenting. They defined them by different levels of control and affection of the parent over the child. These are the authoritative, authoritarian, and permissive styles. The parenting style someone prefers likely depends on multiple factors, such as the person’s own developmental history and the characteristics of the child. Additionally, the genetic information that is passed on from parent to child is also very important in this process/relationship. We want to investigate how different parental features and characteristics and the children’s behavior interact with genetic information. We also want to know how this interaction contributes to the expression of different parenting styles.
|
epigenetics; genetics; environment; parenting style;
control; responsiveness |
FRANKE, Professor Barbara | 5 April 2019 |
| Mandy Meijer
Marieke Klein |
15 April 2019 | |||
| Radboud University Medical Centre | 29 August 2019 | |||
| Donders Centre for Medical Neuroscience, | ||||
| Using Understanding Society data, in combination with previous findings from genetic studies, to help understand the biological processes leading to disease development
The relationship between personality traits, lifestyle and life satisfaction in the UK. |
This project aims to use the genetic and methylation measurements that have been made in the Understanding Society participants to work out how these two types of measurement relate to one another. Genetic measurements refer to the DNA sequence of an individual, while methylation measurements indicate the presence or absence of a particular chemical change to the DNA sequence. The relationships (between genetic measurements and methylation) will then be used to help predict methylation values in other people that have genetic (but not methylation) measurements available. These predicted methylation values will be compared with the presence or absence of disease, to help understand the biological processes leading to disease development.
The proposed project attempts to examine the relationship between personality and lifestyle in the UK using genetic information as indicators for personality. It attempts to determine if personality leads to a specific lifestyle decision such as exercising or whether exercising for example helps shaping personality. The project further aims to determine if there are significant differences between men and women. Personality can be measured by the big 5 personality traits (Openness, Conscientiousness, Extroversion, Agreeability and Neuroticism) and lifestyle by exercise and nutrition. These two lifestyle choices have been shown to lead to better health outcomes. Moreover, the study attempts to analyse if a better lifestyle leads to a higher life-satisfaction. This is important given that higher life-satisfaction can act as a key motivator to achieve a better lifestyle. If genetic information helps us to find a causal link between a specific personality trait and health behaviours/lifestyle, this might lead to personalised and better tailored health promotion and recommendations. These personalized promotions/recommendations might be more efficient and lead to better results. These issues are especially relevant in the present context of surging healthcare costs related to lifestyle diseases. |
Methylation quantitative trait loci, prediction
Wellbeing, Life-Satisfaction, Happiness, Nutrition, Exercise, Lifestyle, Personality, SNPs, Genetic Information |
CORDELL, Dr Heather | 14 Feb 2019 |
| James Fryett | 13 Mar 2019 | |||
| Biotechnology and Biological Sciences Research Council | 26 Apr 2019 | |||
| GSCHWANDTNER, Dr Adelina | 18 Dec 2018 | |||
| Using Understanding Society data, in combination with previous findings from genetic studies, to help understand the biological processes leading to disease development
Genetic Analysis of Individual Income |
This project aims to use the genetic and methylation measurements that have been made in the Understanding Society participants to work out how these two types of measurement relate to one another. Genetic measurements refer to the DNA sequence of an individual, while methylation measurements indicate the presence or absence of a particular chemical change to the DNA sequence. The relationships (between genetic measurements and methylation) will then be used to help predict methylation values in other people that have genetic (but not methylation) measurements available. These predicted methylation values will be compared with the presence or absence of disease, to help understand the biological processes leading to disease development.
In this project, we will study the relationship between genes and income. People with higher income tend to be more healthy and satisfied with life. Therefore, it is important to understand why some people have higher incomes than others. Previous studies suggested that individual differences in income are partly heritable, i.e. they are partly due to genetic effects. Our study will attempt to identify which specific genes are linked to individual differences in income and why these relationships occur. The results of our research will be useful to better understand how environmental factors (e.g. parenting, education) affect income, how genetic and environmental factors interact, and why higher income tends to be associated with better health. |
Methylation quantitative trait loci, prediction
GWAS, income, social science genomics |
Sarah L. Jewell, Uma Kambhampati, Yanchun Bao and Meena Kumari | 7 Feb 2019 |
| Social Services Research Unit at the University of Kent | 25 April 2019 | |||
| KOELLINGER, Dr. Phillip | 11 Dec 2018 | |||
| The relationship between personality traits, lifestyle and life satisfaction in the UK.
Analysis of Biological Aging in Understanding Society |
The proposed project attempts to examine the relationship between personality and lifestyle in the UK using genetic information as indicators for personality. It attempts to determine if personality leads to a specific lifestyle decision such as exercising or whether exercising for example helps shaping personality. The project further aims to determine if there are significant differences between men and women. Personality can be measured by the big 5 personality traits (Openness, Conscientiousness, Extroversion, Agreeability and Neuroticism) and lifestyle by exercise and nutrition. These two lifestyle choices have been shown to lead to better health outcomes. Moreover, the study attempts to analyse if a better lifestyle leads to a higher life-satisfaction. This is important given that higher life-satisfaction can act as a key motivator to achieve a better lifestyle. If genetic information helps us to find a causal link between a specific personality trait and health behaviours/lifestyle, this might lead to personalised and better tailored health promotion and recommendations. These personalized promotions/recommendations might be more efficient and lead to better results. These issues are especially relevant in the present context of surging healthcare costs related to lifestyle diseases.
Humans age at different rates. Some people stay healthy through their 80s and 90s, others die of chronic disease in their 50s and 60s or spend their last decades frail and disabled. Recently, biological theories of aging have evolved. Instead of aging being the outcome of later-life disease, aging is now thought of as a cause – perhaps the most important cause – of chronic disease, disability, and death. New therapies pioneered in animals appear to make the aging process treatable. If human aging could be slowed, it would delay onset of disease and disability, giving us all more healthy years to enjoy. This possibility of prolonging healthy lifespan is critically important in light of an aging global population. To test these new therapies in humans, we need measures that can tell us if the therapies slow aging. In our proposal, we will test if several proposed measures could suit this purpose. |
Wellbeing, Life-Satisfaction, Happiness, Nutrition, Exercise, Lifestyle, Personality, SNPs, Genetic Information
Aging, Biological Aging, Methylation |
Vrije Universiteit Amsterdam | 19 Dec 2018 |
| Hyeokmoon Kweon | 09 Jan 2019 | |||
| BELSKY, Prof Daniel | 17 July 2018 | |||
| Genetic Analysis of Individual Income | In this project, we will study the relationship between genes and income. People with higher income tend to be more healthy and satisfied with life. Therefore, it is important to understand why some people have higher incomes than others. Previous studies suggested that individual differences in income are partly heritable, i.e. they are partly due to genetic effects. Our study will attempt to identify which specific genes are linked to individual differences in income and why these relationships occur. The results of our research will be useful to better understand how environmental factors (e.g. parenting, education) affect income, how genetic and environmental factors interact, and why higher income tends to be associated with better health. | GWAS, income, social science genomics | Duke University School of Medicine | 24 July 2018 |
| David Corcoran, PhD, Joseph Prinz¸ Karen Sugden, Avshalom Caspi, Jonathan Mill | 17 August 2018 | |||
| US National Institute on Aging | 09 Jan 2019 | |||
| Utilising machine learning approaches for comparing the contribution of different types of data for predicting an individual’s risk of ill health | Social surveys are collecting an increasing amount of information about individuals. While we have more information than ever before, we have little understanding of the relative importance of such information to be able to identify which individuals will develop particular health outcomes. Data from Understanding Society will be selected for the following ‘types of data’: personal (e.g. age, sex), socioeconomic (e.g. occupation, education), health (e.g. body mass index, grip strength), biomarkers (e.g. testosterone, cholesterol) and genetic. Machine learning, whereby computers are trained to identify complex patterns in data that are difficult for humans to programme, will then be used to evaluate how well each data type predicts the incidence of ill health among individuals. The main output of the project will be a comparison which data type (or combination of data types) and variables are the best predictors of ill health, allowing policy makers to prioritise their data collection. | Machine learning; predictive model; health | GREEN, Dr Mark | 24 Apr 2018 |
| University of Liverpool | 03 May 2018 | |||
| n/a (named advisers are not accessing data) | 22 May 2018
|
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| Understanding Society Biomedical Data Fellowship | ||||
| An assessment of the total variance explained in HbA1c levels by newly discovered genetic loci | The glycated haemoglobin (HbA1c) test gives your average blood sugar levels over the previous two to three months. It is an important marker of how well individuals regulate sugar levels and can be used to diagnose diabetes. In those already diagnosed with diabetes, the results can indicate whether the measures being taken to control their diabetes are working. Levels of HbA1c are thought to be controlled by a combination of genes and the environment. New research aims to identify genes that play a role in controlling HbA1c and, in this project, we plan to assess how important these identified genes are in explaining HbA1c levels. UKHLS represents a valuable data resource for this work. By exploring the genes that play a role in regulating HbA1c, we can better understand its use (and limitations) as a marker for control of blood sugar levels. | HbA1c, genetics, GWAS | TIMPSON, Prof Nicholas | 05 Mar 2018 |
| U of Bristol | 08 Mar 2018 | |||
| Dr Laura CORBIN, Dr Eleanor WHEELER, Dr Gaelle MARENNE, Dr Ji CHEN, Dr Ines BARROSO | 10 Apr 2018 | |||
| n/a | ||||
| Genetic and epigenetic associations of iron stores | 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 in order 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 individual’s 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 Understanding Society 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. Furthermore, we will examine regulatory variations in DNA (DNA methylation) of individuals with different body iron stores and “genetic iron scores”. Our aim is to gain new insights into iron homeostasis and how disruptions in the system lead to disease. | 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 Roberts, Prof Willem Ouwehand, Prof John Danesh | 28 Feb 2018 | |||
| The National Institute of Health Research (Blood and Transplant) | ||||
| The genetics of favourable adiposity | We would like to access the Understanding Society genetic data and some trait data in order to identify “favourable adiposity” genes. By this we mean versions of genes (alleles) that result in a higher BMI and body fat but lower risk of diseases such as type 2 diabetes, hypertension and heart disease. Current studies have already identified 14 alleles associated with higher body fat percentage but lower risk of these diseases. We know that some of these “favourable adiposity” genes operate by putting more fat in the lower body, resulting in a lower waist to hip ratio (“pear” rather than “apple” shape). We would like to identify more of these alleles through a genome wide association study technique called “multivariate” analysis. This approach identifies alleles associated with higher body fat but a favourable profile of blood-based markers of body fat – for example lower triglycerides and fatty liver proteins but higher good cholesterol (HDL). We will also look to see how alleles that increase body fat link to fertility, as there is a known link between body fat and pregnancy/fertility complications. These alleles will teach us about the body’s ability to store fat in the safest places. | Body fat percentage (%), waist, LFTs, lipids, Blood pressure, adiposity, waist
hip ratio |
Prof Celia Lindgren | 22 Jan 2018 |
| University of Oxford | 02 Feb 2018 | |||
| Hanieh Yaghootkar, Jon Mill, Eilis Hannon, Tim Frayling, Sara Pulit | 06 Apr 2018 | |||
| The genetics of favourable adiposity | We would like to access the Understanding Society genetic data and some trait data in order to identify “favourable adiposity” genes. By this we mean versions of genes (alleles) that result in a higher BMI and body fat but lower risk of diseases such as type 2 diabetes, hypertension and heart disease. Current studies have already identified 14 alleles associated with higher body fat percentage but lower risk of these diseases. We know that some of these “favourable adiposity” genes operate by putting more fat in the lower body, resulting in a lower waist to hip ratio (“pear” rather than “apple” shape). We would like to identify more of these alleles through a genome wide association study technique called “multivariate” analysis. This approach identifies alleles associated with higher body fat percentage but a favourable profile of blood-based markers of body fat – for example lower triglycerides and fatty liver proteins but higher good cholesterol (HDL). These alleles will teach us about the body’s ability to store fat in the safest places. | FRAYLING, Prof Tim | 22 Jan 2018 | |
| University of Exeter | 02 Feb 2018 | |||
| Hanieh Yaghootkar, Jon Mill, Eilis Hannon, Sara Pulit | 03 Mar 2018 | |||
| Unemployment and mental health: A sociogenomic approach | The aim of this project is to investigate the link between underlying genetic factors that are related to mental health and employment and how different social backgrounds and environments (including economic) might influence this relationship. We draw from recent genetic discoveries from large studies who have uncovered multiple genes related to depression, neuroticism and bipolar disorder. Specifically, this project aims to answer the following questions. First, how is a person’s genetics in relation to mental health linked to (un)employment histories. Second, how is the influence of these genetic factors buffered by their social backgrounds and environments, the nature of their job or previous stressful life events. This research works towards a deeper understanding of the complex relationship between genes and our social environment. Better evidence on how social factors moderate genetic influences may broadly benefit our society. | Unemployment, Mental Health, Sociology, polygenic socres, G x E | MILLS, Prof Melinda | 22 Jan 2018 |
| University of Oxford | 02 Feb 2018 | |||
| Evelina Akimova; Riley Taiji | 26 Mar 2018 | |||
| ESRC/NCRM (National Centre for Research Methods) | ||||
| Gene-Environment Interplay in the Generation of Health and Education Inequalities | Inequalities in education and health are pervasive, persistent and deeply intertwined. Neonatal health affects education achievements, and education success is associated with longer life expectancies. These inequalities are well documented, and better understanding of the mechanisms underlying them could increase the potential to address them through public policies.
Understanding Society is one of only a few datasets that contains genetic data from people of different ages, under different policy exposures, and follows the same people over time. Our project intends to investigate how genes interact with a dynamic environment (e.g., changing smoking policies, changes in the working family tax credits, temporal variation in exposure to influenza, etc.) to generate inequalities in education and health behaviours. We innovate by combining methods from genetics and social science. Building on the discovery of genetic variants that exhibit strong and replicated associations with behavioural outcomes, we will grasp unprecedented opportunities to fill the gap in knowledge about the combined role of genes and environments in causing inequality. We will examine how Genes and the Environment (GxE) interact to generate inequalities in education and health over the life course. We will go beyond the old nature versus nurture debate by testing two novel hypotheses: (i) children born into advantaged environments are better able to reach their genetically conditioned education potential, and (ii) a privileged environment protects against genetic susceptibility to risky health behaviour. |
Inequality; Gene; Environment; Interaction; Education; Health; Health Behaviours | KIPPERSLUIS, Prof Hans van | 18 Oct 2017 |
| Erasmus University Rotterdam | 24 Oct 2017 | |||
| Rita Pereira; Niels Rietveld; Stephanie von Hinke; Pietro Biroli; Tonu Esko | 26 Oct 2017 | |||
| NORFACE (New Opportunities for Research Funding Agency Co-operation in Europe – Dial programme | ||||
| Understanding the genetics of neurodevelopmental disorders | We are studying a large group of children with severe intellectual disability, referred from genetics clinics across the UK, in order to find the genes that cause their disorders. We have found evidence that genetic variants (differences in DNA sequence between people) that are common in the healthy population also influence risk of having severe intellectual disability. Some of this common variation is also known to affect educational attainment, i.e. how many years of school someone attends. We want to use the genetic data from people in Understanding Society with information about their educational attainment to compare this to our group of children with intellectual disability. Our eventual goal is to improve our understanding of the biology underlying severe intellectual disability, and to improve our ability to predict the chance that parents with one affected child might have another. | genetics, intellectual disability | BARRETT, Prof Jeffrey | 12 Sept 2017 |
| The Wellcome Trust Sanger Institute | 19 Sept 2017 | |||
| n/a | 19 Sept 2017 | |||
| Wellcome Trust / Dept. of Health | ||||
| Characterising the genetic basis of mental health related symptoms in the general population | While most people do not have psychiatric disorders, it is possible to experience mental health symptoms that are related to some of these disorders, for example feeling low or anxious for a period of time. In the UKHLS study, participants answered a questionnaire about this kind of symptoms. We would like to study the biological background of these symptoms. Specifically, we are trying to find out how much the biological factors causing mental health symptoms overlap with the ones causing psychiatric disorders. The way we approach these biological factors is to compare the genetics between mental health symptoms and psychiatric disorders. If they overlap, it may be possible to learn more about psychiatric disorders in cohort studies that have collected genetic and mental health data, even if they have no direct information on psychiatric diagnoses. Therefore, our study can provide crucial guidance for future research to improve diagnosis and treatment of psychiatric disorders. | biomarkers; gene-environment interactions | KUCHENBAECKER, Dr Karoline | 12 Sept 2017 |
| University College London | 19 Sept 2017 | |||
| NONE | 24 Oct 2017 | |||
| (Grant application) | ||||
| Family background, genetics and educational achievement. A follow-up study. | A number of studies have found big differences in educational achievement between individuals from high and low income backgrounds. Narrowing this achievement gap has now become an issue of great public policy concern. The presumption by many policymakers is that such differences in educational achievement are driven simply by the different quality and quantity of resources (e.g. books, schools, tutoring) that high and low income families can provide. However, an alternative explanation is that at least part of this achievement gap is due to genetic differences.
I will explore this issue using UKHLS data. There are almost 100 genetic variants associated with educational achievement. I will consider whether parts of the DNA code which have been shown to be related to educational achievement differ between high and low income groups. I will then investigate the extent that accounting for these genetic differences can explain variation in educational achievement between individuals from high and low income backgrounds. |
Educational achievement, socio-economic gaps | JERRIM, Prof John | 12 Sept 2017 |
| UCL | 19 Sept 2017 | |||
| n/a | 02 Oct 2017 | |||
| The Genetic Basis of Political Behaviours and Orientations | The aim of this project is to develop numerical scores for combinations of genes that are associated with
educational attainment, personality, and cognitive ability. Previous political science research has shown each of these traits to be strongly related to political behaviours and attitudes. For example, educational attainment is one of the strongest predictors of who participates in elections. Our project builds on this literature by testing whether genes that are associated with educational attainment, personality, and cognitive ability are indirectly related political behaviors and orientations. More specifically, we will test whether these genes influence educational attainment, personality, and cognitive ability and these traits then shape political behaviors and orientations. The scores will be derived from the Understanding Society dataset by linking individuals’ responses to questions regarding political behaviours, identity, and affiliation to their genetic data. |
Voting behaviour; educational attainment; genetics; polygenic scores | MILLS, Prof Melinda | 12 Sept 2017 |
| University of Oxford | 19 Sept 2017 | |||
| Felix Tropf, Chris Dawes (NYU) | 24 Oct 2017 | |||
| National Institutes of Health | ||||
| Characterising the genetic basis of mental health related symptoms in the general population | While most people do not have psychiatric disorders, it is possible to experience mental health symptoms that are related to some of these disorders, for example feeling low or anxious for a period of time. In the UKHLS study, participants answered a questionnaire about this kind of symptoms. We would like to study the biological background of these symptoms. Specifically, we are trying to find out how much the biological factors causing mental health symptoms overlap with the ones causing psychiatric disorders. The way we approach these biological factors is to compare the genetics between mental health symptoms and psychiatric disorders. If they overlap, it may be possible to learn more about psychiatric disorders in cohort studies that have collected genetic and mental health data, even if they have no direct information on psychiatric diagnoses. Therefore, our study can provide crucial guidance for future research to improve diagnosis and treatment of psychiatric disorders. | biomarkers, gene-environment interactions | KUCHENBAECKER, Dr Karoline | 03 July 2017 |
| University College London | 12 July 2017 | |||
| Ka Wai (Kathy) CHAN | 24 Oct 2017 | |||
| (Grant application) | ||||
| Investigating the genetic relationships between anxiety, depression, stressful life outcomes, 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 Understanding Society 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 | 03 July 2017 |
| King’s College London | 07 July 2017 | |||
| Delilah Zabeneh, Karen Hodgson, Saskia Hagenaars, Gerome Breen, Paul O’Reilly | 09 Aug 2017 | |||
| NIHR Maudsley Biomedical Centre | ||||
| Impact of economic conditions in year of birth on DNA methylation age acceleration | Studies have found that being born in a good or bad year for the national economy can have long term effects on one’s health. For example, those born in an economic downturn experience greater risk of dying from cardiovascular disease. The social and biological mechanisms of this effect are poorly understood. One possisble explanation is that a poor economy at birth may contribute to factors (e.g. malnutrition, stress, etc.) impacting regulatory features of cells, called DNA methylation, increasing the chances of aging more rapidly. Understanding Society is ideally suited to address this knowledge gap: having DNA methylation from many adults born before 1960, when this effect is likely most powerful. We propose to investigate whether poor economic conditions at birth are related to altered DNA methylation in adults, and in particular to a measure of one’s ‘DNA methylation age’ to determine if this contributes to accelerated biological aging. | DNA methylation, age acceleration, business cycle | RELTON, Prof Caroline | 25 May 2017 |
| SSCM, University of Bristol | 07 June 2017 | |||
| Dr Paul Yousefi, Prof Gerard Van den berg, Dr Matthew Suderman | 25 June 2017 | |||
| ESRC and BBSRC | ||||
| Investigating epigenetic changes in shiftwork – a possible mechanism for its impact on health and the body clock | Shift work is a widespread feature of our society, with a third of men and a fifth of women in the UK engaged in it. However, shift work has been linked with a number of poor health outcomes, including obesity, diabetes, heart disease, depression and some types of cancer. The mechanisms by which shift work might lead to these diseases are poorly understood. A major area of interest is the effect shift work, and night shift work in particular, has on altering a person’s body clock. There is information available on shift work patterns at multiple time points in Understanding Society. The availability of blood samples for some study participants provides an opportunity to look at biological changes associated with shift work, including the impact shift work might have on epigenetic modifications, which influence how our genes are turned on or off . This work will help us better understand how the occupational exposure of working shifts might become embodied in human biology, with the potential for long term health consequences. | Shift work, sleep, DNA methylation, circadian | RELTON, Prof Caroline | 25 May 2017 |
| SSCM, University of Bristol | 07 June 2017 | |||
| Dr Rebecca Richmond, Prof George Davey Smith, Prof Meena Kumari | 03 June 2017 | |||
| MRC IEU, Bristol | ||||
| A meta-analysis of genome-wide association studies of general cognitive
function in the CHARGE and COGENT consortia |
People’s differences in cognitive functions are associated with important life outcomes. Previous genome-wide association (GWA) studies have shown that these differences are partly heritable and are controlled by many genes each contributing a small effect. GWA studies of other complex traits, for example height, have shown that very large sample sizes are required to detect these small effect sizes. This study substantially increases the number of individuals (N > 100,000) to more than double that of the current largest GWA study of general cognitive function (N = 53949). This study aims to identify new genetic associations with general cognitive function and further investigate previous findings. We will also explore if genes that are associated with general cognitive function are also associated with health outcomes and physical measures of ageing, for example, walking speed. | Genetics, GWAS, general cognitive function, health | Prof Ian Deary | 27 Feb 2017 |
| University of Edinburgh | 07 Mar 2017 | |||
| Dr Gail Davies | 21 Apr 2017 | |||
| Centre for Cognitive Ageing and Cognitive Epidemiology | ||||
| Assortative mating and genetics | This project aims to use socioeconomic and genetic data from the Understanding Society. We will examine variation in spouses’ characteristics to investigate the similarity of partners to one another – this is also called assortative mating. We will examine different characteristics such as socioeconomic status (e.g., educational attainment) and health (e.g., weight and height). Assortative mating may have direct implications for the transmission of socioeconomic status and inequality across generations. Similarities between partners may be due to preferences for specific characteristics or could also capture underlying (unobservable) traits and constraints that people face when forming a relationship. In this project, we will first investigate whether we can use genetic variation (differences in family traits) among partners to better measure the similarity in their attributes. This information will help us to learn whether and how individuals value one or more characteristics in a partner. It will also help us to learn which type of characteristics matter (socioeconomic and/or health ones?). The project will then calculate measures of genetic predisposition (polygenic scores) to assess the degree of assortative mating in the partners’ characteristics. | Matching Models, Marriage, Schooling, Exclusion Restriction, Instrumental Variables, Polygenic Scores | QUINTANA-DOMEQUE, Dr Climent | 27 Feb 2017 |
| University of Oxford and St Edmund Hall | 07 Mar 2017 | |||
| Dr Nicola Barban, <male>; Dr Elisabetta De Cao ; Dr Sonia Oreffice | 02 June 2017 | |||
| n/a | ||||
| Inflammatory cytokines interleukin-6, interleukin-1-beta, and C-reactive protein as causal risk factors for depressive symptoms: A Mendelian randomisation study | The purpose of this study is to determine whether poor immune system functioning can cause symptoms of depression. We aim to do this by examining whether genetic variants that are associated with inflammatory factors predict a higher likelihood of having depressive symptoms, a method which reduces several types of bias. First, genetic variants that are known to be associated with specific inflammatory factors (interleukin-6, interleukin-1-beta, and C-reactive protein) would be identified and used to construct a genetic score which represents these inflammatory factors. Data from several other cohort studies including the UK Household Longitudinal Study would then be used to examine causal links between genetic scores for inflammation and depressive symptoms. Confirmation of these causal links may reveal pathways that can be targeted for the prevention and treatment of depression; the absence of causal links may shift the focus of future research onto other targets for therapy. | Inflammation; Depressive symptoms; Causality; Mendelian randomisation | CARVALHO, Dr Livia | 14 July 2016 |
| University College London | 18 July 2016 | |||
| Dr Joshua Bell, Dr Golam Khandaker | 18 July 2016 | |||
| MRC (ImmunoPsychiatry Consortium) | ||||
| Genomics of social support, personality and cognition and their relation to mental health and cognitive ageing | Genes play an important role in shaping the social behaviour and cognition/cognitive ageing as they modulate the brain activity through molecular pathways; therefore, it can be said that genes regulate the expression of behaviour. Social support and cognition are correlated with an individual’s mental health as these social interactions require effective communication and participation. We would like to use information from the 1985 birth cohort to: (1) assess the impact and associations between social behaviour (social support) and cognition in individuals with and without symptoms of depression and anxiety, (2) perform a genetic analysis of social support and cognition within the same population by studying changes in the DNA of individuals, and (3) harmonise these data with data from different cohorts | Social support, social dysfunction, personality, cognition, GWAS, mental health, cognitive ageing | NICODEMUS, Dr Kristin | 07 June 2016 |
| University of Edinburgh | 17 June 2016 | |||
| Elvina Gountouna (contact), Thalia Perez Suarez, Kathy Evans, Rosie Walker, Lara Neira Gonzalez, Daniel McCartney | 17 June 2016 | |||
| No outside funding. The project will be undertaken in the laboratories of Dr. Nicodemus and in collaboration with Dr. Kathy Evans, Reader, University of Edinburgh. | ||||
| FPLD1 and Severe Insulin Resistance GWAS | Familial Partial Lipodystrophy Type 1 (FPLD1) is a rare disorder of fat distribution with an extreme phenotype, associated with severe resistance to the glucose lowering effects of insulin. This study aims to investigate whether individuals with this disorder carry an excess of markers for known measures of “metabolic syndrome” risk (insulin and lipid measurements, blood pressure, BMI, waist-hip ratio) when compared to unaffected control participants from UKHLS. It also aims to discover whether these individuals carry any new markers that might contribute to FPLD1 disease risk, again, in comparison with unaffected control UKHLS participants. | Familial Partial Lipodystrophy, Severe Insulin Resistance, GWAS, genetic risk score | BARROSO, Dr Inês | 01 Dec 2015 |
| Wellcome Trust Sanger Institute | 17 Dec 2015 | |||
| Felicity PAYNE, Eleanor WHEELER & Allan DALY | 17 Dec 2015 | |||
| Human Genetics Working Group, Wellcome Trust Sanger Institute Core funding | ||||
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