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Title: Understanding the effect of gene-smoking interactions on lung function and COPD risk utilising UK Biobank
Author: Melbourne, Carl A.
ISNI:       0000 0004 7970 2880
Awarding Body: University of Leicester
Current Institution: University of Leicester
Date of Award: 2019
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Chronic obstructive pulmonary disease (COPD), characterised by severe airflow obstruction, is a leading cause of mortality worldwide. Smoking is the biggest risk factor, however COPD and the lung function measures key to its diagnosis, have a strong genetic component. Known loci account for a small proportion of lung function heritability, and not all smokers develop COPD. This thesis questions whether smoking and genetic effects on lung function are independent, aiming to identify novel gene-smoking interactions, to aid in genetic risk prediction and treatment development. The literature review undertaken here examines a range of approaches for interaction analyses. I applied two such methods using simulation studies to determine the power to detect interaction effects. Interaction analyses were then undertaken utilising UK Biobank (n~500k). Firstly, as regions of the genome containing lung function associated loci might be more likely to contain SNPs producing interaction effects, a candidate region interaction analysis was undertaken. Two SNPs were identified, driven by stronger genetic effects in ever smokers. Secondly, I meta-analysed data from UK Biobank and the SpiroMeta consortium (n~400k) and determined that none of the 279 lung function and COPD associated signals reported to date had individually differing genetic effects between ever and never smokers. Thirdly, I undertook the largest genome-wide gene-smoking interaction analysis to date and identified 53 genetic loci that interact with smoking behaviour. Replication efforts were penalised by small effective sample sizes, but results provide direction for further research. This work informs current estimates of relative and absolute risk for poor lung function and COPD due to genetic risk and smoking status. The 55 loci identified could have clinical importance by providing personalised risk prediction and treatment. To further understand the effect of gene-smoking interactions on lung function and COPD risk, larger replication sample sizes with better imputation quality are needed.
Supervisor: Wain, Louise ; Tobin, Martin Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available