Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.801578
Title: Geographical gene-environment interaction and correlation for mental health in the UK and Sweden
Author: Reed, Zoe E.
Awarding Body: University of Bristol
Current Institution: University of Bristol
Date of Award: 2020
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Abstract:
Risk for mental health traits can vary based on where people live, however it is unclear how this may be reflected in the underlying aetiology. There may be both aetiological interactions with geographical environments (GxE) and genetic influences on geographical environments, known as gene-environment correlation (rGE). Identifying such interactions and correlations would be useful for identifying environmental risk factors and causal inference. I have used a number of quantitative genetic methods to investigate the existence of both of these. To examine geographical GxE I have used both weighted twin analyses and polygenic risk score analyses with outcomes of ASD and ADHD. To examine geographical rGE I have conducted genome-wide association studies (GWAS) of geographical environments. My findings suggest that geographical GxE is present on both national and local scales, in different populations, for both ASD and ADHD. The twin analyses suggest both GxE and also interactions of geographical environments with the non-shared environment. The polygenic risk score analyses suggest interactions of geographical environments with known genetic influences on ASD and ADHD. I also present a novel method, using twin analysis, to investigate whether a non-geographical gene-environment interaction can vary geographically. Additionally, results from the GWAS suggest that rGE does exist in terms of geographical environments. These findings will help us to identify environments or risk factors for ASD and ADHD that can draw out or mask genetic influences in different contexts, such as local versus national scales and across countries. The GWAS results can be used in Mendelian Randomisation analyses for causal inference. This could be applied to a range of mental health outcomes and geographical environments to see if they are causally related.
Supervisor: Davis, Oliver ; Davey Smith, George Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.801578  DOI: Not available
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