Incorporating genetic data to better understand the social and environmental drivers of children's outcomes
Lead Research Organisation:
University of Bristol
Department Name: Social Medicine
Abstract
This fellowship contains an interdisciplinary programme of quantitative research to examine the impact that home and neighbourhood environments have on children's health and social outcomes. It uses data from a Bristol based cohort of children born in the early 1990's to investigate how social factors such as geographical relocation in early life relate outcomes including mental health and cannabis use.
This fellowship will investigate ways to overcome a statistical limitation common to population-based health research; that studies are often biased by underlying differences between people that are not observed (such as family history, personality differences or genetics). Novel statistical methods can be used to investigate the impact of underlying differences even where they are unmeasured, and provide an excellent tool for determining whether relationships between social factors and health may be due to potential causal factors or if they arise due to correlation structures within the data.
To further understanding of the ways in which unmeasured differences between people may impacts health outcomes, I will investigate the extent to which genetic differences between individuals may underlie the associations revealed in social science research. For example, there is a genetic architecture to cannabis use that helps to explain why some people are more likely to be users than others, and it is possible that this 'hidden' genetic architecture influences the links observed between social environments and cannabis use. This fellowship will examine this and seek to further knowledge on the way in which genetics contribute towards complex health and social outcomes.
This fellowship will investigate ways to overcome a statistical limitation common to population-based health research; that studies are often biased by underlying differences between people that are not observed (such as family history, personality differences or genetics). Novel statistical methods can be used to investigate the impact of underlying differences even where they are unmeasured, and provide an excellent tool for determining whether relationships between social factors and health may be due to potential causal factors or if they arise due to correlation structures within the data.
To further understanding of the ways in which unmeasured differences between people may impacts health outcomes, I will investigate the extent to which genetic differences between individuals may underlie the associations revealed in social science research. For example, there is a genetic architecture to cannabis use that helps to explain why some people are more likely to be users than others, and it is possible that this 'hidden' genetic architecture influences the links observed between social environments and cannabis use. This fellowship will examine this and seek to further knowledge on the way in which genetics contribute towards complex health and social outcomes.
Organisations
- University of Bristol (Fellow, Lead Research Organisation)
- Department for Education (Collaboration)
- University of Southern California (Collaboration)
- Norwegian Mother and Child Cohort (MoBa) (Collaboration)
- Free University of Amsterdam (Collaboration)
- University of Queensland (Collaboration)
- HUNT Research Centre (Collaboration)
- UNIVERSITY OF EDINBURGH (Collaboration)
People |
ORCID iD |
| Tim Morris (Principal Investigator / Fellow) |
Publications
Barry CS
(2022)
Investigating how the accuracy of teacher expectations of pupil performance relate to socioeconomic and genetic factors.
in Scientific reports
Boyd A
(2019)
Data Resource Profile: The ALSPAC birth cohort as a platform to study the relationship of environment and health and social factors
in International Journal of Epidemiology
Brumpton B
(2020)
Avoiding dynastic, assortative mating, and population stratification biases in Mendelian randomization through within-family analyses
in Nature Communications
Howe L
(2020)
Assortative mating and within-spouse pair comparisons
Morris T
(2019)
Can education be personalised using pupils' genetic data?
Morris TT
(2020)
Population phenomena inflate genetic associations of complex social traits.
in Science advances
| Description | The work funded by this award made a number of key findings, as follows: 1) A greater understanding of the formation of educational outcomes. As part of this award we have published research demonstrating the importance of school enjoyment in the formation of educational outcomes. School enjoyment represents a plausible modifiable factor that can be more easily influenced than other drivers of educational outcomes such as family socioeconomic background. We have also published work demonstrating that educational and health differences between groups of children arise due to both social and genetic differences. 2) Better knowledge of the social processes that can bias genetic analyses. As part of this award we have published research demonstrating that most genetic analyses are biased by complex social and demographic processes, which include assortative mating, migration and the creation of family/household environments by parents. We have also demonstrated how family data using sibling-pairs or mother-father-offspring trios are robust to these biases and may offer more accurate conclusions from genetic data. 3) Improved methodological approaches for understanding the potential role of genetics on health and educational outcomes. As part of this award we have published research demonstrating how repeat-measure data can be used to develop a better understanding of the role that genetics may play in the formation of health and educational outcomes, particularly regarding the different trajectories that people experience. |
| Exploitation Route | Academic. The research outcomes in this award are relevant to a broad range of academics from Social Science, Education, Epidemiology and Genomics. The development of methods for genomic analysis and biases within genomic analyses will be important to geneticists and academics in other disciplines conducting interdisciplinary research using genetic data. The empirical analyses investigating the formation of educational and health outcomes will be important to Social Science, Education and Epidemiology academics. Non-academic. The research outcomes are relevant to the Department for Education, particularly with regards to the formation of educational outcomes. |
| Sectors | Education,Healthcare |
| Description | SLLS Conference Bursary |
| Amount | € 450 (EUR) |
| Organisation | Society for Longitudinal and Lifecourse Studies SLLS |
| Sector | Charity/Non Profit |
| Country | United Kingdom |
| Start | 09/2019 |
| End | 09/2019 |
| Description | Collaboration with Department for Education |
| Organisation | Department for Education |
| Country | United Kingdom |
| Sector | Public |
| PI Contribution | We have started a collaboration with the Department for Education (DfE) that arose from Dr Tim Morris' approach to the DfE Head of Research in 2019. We are now developing further grant applications in collaboration with the DfE (one of which has been submitted to the ESRC SDAI scheme) and are exploring the possibility of Dr Tim Morris (as well as other colleagues at the MRC IEU) sitting on research advisory boards at the DfE. |
| Collaborator Contribution | The DfE are contributing in a supportive capacity towards grant applications that we are preparing. Where possible, these will involve two-way development of research ideas. The DfE have also agreed in principle to host PhD placement visits for students at the MRC IEU, subject to the suitability of the students and their research projects. |
| Impact | None currently |
| Start Year | 2019 |
| Description | Within-family Consortium |
| Organisation | Free University of Amsterdam |
| Country | Netherlands |
| Sector | Academic/University |
| PI Contribution | In collaboration with colleagues at the MRC IEU (Dr Neil Davies and Prof George Davey Smith), Dr Tim Morris established an international consortium focused on within-family analysis. This consortium currently involves over 50 academics from around the world and through these academics the consortium has access to over 30 international datasets. In addition to establishing this consortium, we are leading on the analysis and publication of multiple research projects. |
| Collaborator Contribution | The international partners have contributed through data provision and analytical advice. Future contributions will include the writing of papers and grant applications. |
| Impact | Brumpton et al. Within-family studies for Mendelian randomization: avoiding dynastic, assortative mating, and population stratification biases. https://doi.org/10.1101/602516. Under Review. |
| Start Year | 2019 |
| Description | Within-family Consortium |
| Organisation | HUNT Research Centre |
| Country | Norway |
| Sector | Learned Society |
| PI Contribution | In collaboration with colleagues at the MRC IEU (Dr Neil Davies and Prof George Davey Smith), Dr Tim Morris established an international consortium focused on within-family analysis. This consortium currently involves over 50 academics from around the world and through these academics the consortium has access to over 30 international datasets. In addition to establishing this consortium, we are leading on the analysis and publication of multiple research projects. |
| Collaborator Contribution | The international partners have contributed through data provision and analytical advice. Future contributions will include the writing of papers and grant applications. |
| Impact | Brumpton et al. Within-family studies for Mendelian randomization: avoiding dynastic, assortative mating, and population stratification biases. https://doi.org/10.1101/602516. Under Review. |
| Start Year | 2019 |
| Description | Within-family Consortium |
| Organisation | Norwegian Mother and Child Cohort (MoBa) |
| Country | Norway |
| Sector | Public |
| PI Contribution | In collaboration with colleagues at the MRC IEU (Dr Neil Davies and Prof George Davey Smith), Dr Tim Morris established an international consortium focused on within-family analysis. This consortium currently involves over 50 academics from around the world and through these academics the consortium has access to over 30 international datasets. In addition to establishing this consortium, we are leading on the analysis and publication of multiple research projects. |
| Collaborator Contribution | The international partners have contributed through data provision and analytical advice. Future contributions will include the writing of papers and grant applications. |
| Impact | Brumpton et al. Within-family studies for Mendelian randomization: avoiding dynastic, assortative mating, and population stratification biases. https://doi.org/10.1101/602516. Under Review. |
| Start Year | 2019 |
| Description | Within-family Consortium |
| Organisation | University of Edinburgh |
| Department | Edinburgh Genomics |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | In collaboration with colleagues at the MRC IEU (Dr Neil Davies and Prof George Davey Smith), Dr Tim Morris established an international consortium focused on within-family analysis. This consortium currently involves over 50 academics from around the world and through these academics the consortium has access to over 30 international datasets. In addition to establishing this consortium, we are leading on the analysis and publication of multiple research projects. |
| Collaborator Contribution | The international partners have contributed through data provision and analytical advice. Future contributions will include the writing of papers and grant applications. |
| Impact | Brumpton et al. Within-family studies for Mendelian randomization: avoiding dynastic, assortative mating, and population stratification biases. https://doi.org/10.1101/602516. Under Review. |
| Start Year | 2019 |
| Description | Within-family Consortium |
| Organisation | University of Queensland |
| Department | Queensland Institute of Medical Research |
| Country | Australia |
| Sector | Academic/University |
| PI Contribution | In collaboration with colleagues at the MRC IEU (Dr Neil Davies and Prof George Davey Smith), Dr Tim Morris established an international consortium focused on within-family analysis. This consortium currently involves over 50 academics from around the world and through these academics the consortium has access to over 30 international datasets. In addition to establishing this consortium, we are leading on the analysis and publication of multiple research projects. |
| Collaborator Contribution | The international partners have contributed through data provision and analytical advice. Future contributions will include the writing of papers and grant applications. |
| Impact | Brumpton et al. Within-family studies for Mendelian randomization: avoiding dynastic, assortative mating, and population stratification biases. https://doi.org/10.1101/602516. Under Review. |
| Start Year | 2019 |
| Description | Within-family Consortium |
| Organisation | University of Southern California |
| Country | United States |
| Sector | Academic/University |
| PI Contribution | In collaboration with colleagues at the MRC IEU (Dr Neil Davies and Prof George Davey Smith), Dr Tim Morris established an international consortium focused on within-family analysis. This consortium currently involves over 50 academics from around the world and through these academics the consortium has access to over 30 international datasets. In addition to establishing this consortium, we are leading on the analysis and publication of multiple research projects. |
| Collaborator Contribution | The international partners have contributed through data provision and analytical advice. Future contributions will include the writing of papers and grant applications. |
| Impact | Brumpton et al. Within-family studies for Mendelian randomization: avoiding dynastic, assortative mating, and population stratification biases. https://doi.org/10.1101/602516. Under Review. |
| Start Year | 2019 |