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        <datestamp>2026-04-17T16:48:35Z</datestamp>
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          <dc:title>Monogenic diabetes in large 
population settings</dc:title>
          <dc:creator>Luke Sharp (21044225)</dc:creator>
          <dc:subject>Diabetes</dc:subject>
          <dc:subject>Genetics</dc:subject>
          <dc:subject>Lipodystrophy</dc:subject>
          <dc:subject>MODY</dc:subject>
          <dc:description>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.&lt;p&gt;&lt;/p&gt;</dc:description>
          <dc:date>2026-03-31T00:00:00Z</dc:date>
          <dc:type>Text</dc:type>
          <dc:type>Thesis</dc:type>
          <dc:identifier>10779/exe.32034930.v1</dc:identifier>
          <dc:relation>https://figshare.com/articles/thesis/Monogenic_diabetes_in_large_population_settings/32034930</dc:relation>
          <dc:rights>All rights reserved</dc:rights>
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