Andrew Wood

Mid-career Tenured Researcher, University of Exeter

2 active projects

Genetics of Diabetes and its related Metabolic Traits

Hundreds of genetic variants have been linked with diabetes and related metabolic conditions, including for obesity, type-two diabetes, HbA1c (glycated haemoglobin).. However, the biological pathways by which they act is not yet clear, nor have all causal genetic variants been…

Scientific Questions Being Studied

Hundreds of genetic variants have been linked with diabetes and related metabolic conditions, including for obesity, type-two diabetes, HbA1c (glycated haemoglobin).. However, the biological pathways by which they act is not yet clear, nor have all causal genetic variants been identified, especially those in the fewest individuals. We intend to run genome-wide association studies between these phenotypes (e.g. individuals who suffer from diabetes versus those who do not, or BMI as a continuous trait), and other diabetes-related phenotypes (HbA1c, glucose etc) against genetic variants from the Whole Genome Sequencing data. Our results will improve the field’s understanding of the biological pathways for these conditions. The All of Us data set is crucial for answering these questions, due to its diverse genetic ancestry. Whole genome sequencing contains nearly all genetic variants, down to the rarest (<1% of individuals), which aids our ability to identify causal genetic mutations.

Project Purpose(s)

  • Disease Focused Research (type 2 diabetes mellitus)
  • Ancestry

Scientific Approaches

We will be performing genome-wide association studies, comparing the frequency of variants between individuals who do and do not suffer from diabetes and other complications, or looking to identify variants that are associated with increased or decreased BMI/HbA1c. We will use the software tool REGENIE to perform our analysis, which rigorously controls for known confounders. Our association analyses will be performed using the most recent release of whole-genome sequencing data. We will also perform downstream analyses, such as Fine Mapping, which inform us about which variants are and are not causal.

Anticipated Findings

We anticipate finding novel genetic factors associated with metabolic and diabetes-related diseases. Our results will be published in high-impact journals under an open-access agreement.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Collaborators:

  • Harry Green - Mid-career Tenured Researcher, University of Exeter
  • Timothy Frayling - Late Career Tenured Researcher, Universite de Geneve
  • Liza Darrous - Research Fellow, Universite de Geneve
  • Lauric Ferrat - Early Career Tenure-track Researcher, Universite de Geneve
  • Aurelie Kamoun - Senior Researcher, Universite de Geneve

Height

What genetic associations can we identify using a variety of genetic data sources within AllofUs? What is the best approach to analysing genetic data at scalene in AllofUS?

Scientific Questions Being Studied

What genetic associations can we identify using a variety of genetic data sources within AllofUs?
What is the best approach to analysing genetic data at scalene in AllofUS?

Project Purpose(s)

  • Disease Focused Research (Height)
  • Methods Development
  • Ancestry

Scientific Approaches

Genome-wide association studies through the use of genotyping arrays and sequencing data. A number of statistical tests will be performed related to single-variant and aggregate-based association testing.

Anticipated Findings

Novel genetic associations with height - many rarer than previously possible to identify. The results from these analyses may be meta-analysed with findings from other studies as well to boost statistical power to detect genetic associations.

Demographic Categories of Interest

  • Race / Ethnicity

Data Set Used

Controlled Tier

Research Team

Owner:

  • Andrew Wood - Mid-career Tenured Researcher, University of Exeter
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