Monogenic diabetes in large population settings

In this thesis we aim to utilise large population cohorts to analyse monogenic diabetes. This will involve gene discovery searching for novel disease genes and variants, assessing the penetrance and comorbidities risk in carriers of pathogenic monogenic diabetes variants, and studying disease prevalence in the general population and clinically understudied groups. Chapter one is an introductory chapter that is divided into three main sections. The first two sections discuss MODY and lipodystrophy, respectively, and describe the varying phenotypes, diagnosis, treatment, and genetic causes of these diseases. The final section provides an overview of how large population cohorts can be used to study rare diseases and the techniques utilised to complete this. In chapter two, we used whole exome sequencing data from the UK Biobank to identify everybody who carries a pathogenic MODY variant. Using these individuals, we assess the prevalence of MODY in the general population and assess the makeup of MODY subtypes in the general population. We additionally assessed the disease penetrance and mortality. These results provide important information for population screening, interpretation of identified variants and risk management of patients and their family members. In chapter three, we once again used a genotype first approach to identify individuals in the UK Biobank who have a pathogenic monogenic lipodystrophy genotype. We then found that no individual with a pathogenic genotype had a clinical diagnosis of lipodystrophy, despite having a genotype and phenotype suggestive of the disease. Additionally, we provide the most accurate prevalence of monogenic lipodystrophy to date, estimate the comorbidities risk and mortality in carriers and compare these carriers against clinically ascertained cases. In chapter four, we identified everybody who has MODY in individuals who were diagnosed with diabetes after the age of 40 years. Due to the later onset of diabetes in these individuals, they are generally not offered genetic testing for MODY. In this study we determined the prevalence of MODY in this later onset diabetes group, assessed these individuals’ phenotypes and designed a 3 strategy to optimise genetic testing in this later onset group as assess its feasibility. In chapter five, we assess how to analyse the penetrance of monogenic disease in a large population cohort, and we document common mistakes that could be made during the analysis. To aid in writing this and prove the effectiveness of these suggestions we replicate a previous study, which underestimates the penetrance of multiple diseases. Additionally, we then replicate this study again whilst correcting some of their mistakes to achieve more accurate estimates of penetrance. This paper is important as it provides advice to estimate penetrance using large population cohorts to avoid mistakes which could lead to misinterpretation of variants and negatively affect patients. In chapter six, we use a gene-based burden analysis on BMI adjusted waist hip ratio, to identify novel disease-gene associations. As waist hip ratio can be used as a proxy for unfavourable adiposity, genes associated with this trait often also cause partial lipodystrophy. This analysis identified MIB1 as a novel gene associated with BMI adjusted waist hip ratio. Upon further analysis this gene was found to have a sex specific effect on BMI and diabetes risk.<p></p>

Author dc:creator

• Luke Sharp (21044225)

• Diabetes
• Genetics
• Lipodystrophy
• MODY

Statement dc:rights

• All rights reserved

Identifier

10779/exe.32034930.v1

OAI identifier oai:identifier

oai:figshare.com:article/32034930