Research Fellow in Genetic Epidemiology

The Faculty of Medicine at the University of Southampton invites applications for a full-time, fixed-term Research Fellow to join Professor Hanieh Yaghootkar's interdisciplinary precision-diabetes research programme investigating the heterogeneity of obesity and its impact on type 2 diabetes and related complications. Based at University Hospital Southampton, you will investigate why individuals with similar levels of adiposity differ substantially in their risk of developing type 2 diabetes and its complications. Working alongside genetic epidemiologists, clinicians and data scientists, you will apply statistical genetics approaches, including genome-wide association studies (GWAS), Mendelian randomisation and post-GWAS analyses, to identify genetic determinants, investigate causal relationships and uncover biological pathways linking obesity to metabolic disease.

As a Research Fellow, you will:

- Investigate the genetic and biological heterogeneity of obesity and its relationships with type 2 diabetes and related complications.
- Conduct genome-wide association studies (GWAS) of obesity-related traits, adiposity phenotypes and metabolic outcomes using large-scale genetic and epidemiological datasets.
- Apply Mendelian randomisation and complementary genetic epidemiological approaches to investigate potential causal relationships between different aspects of adiposity, type 2 diabetes and its complications.
- Perform post-GWAS analyses, including fine-mapping, functional annotation, gene prioritisation and pathway enrichment, to identify genes and biological mechanisms underlying obesity-related metabolic risk.
- Investigate the genetic determinants of different obesity-related phenotypes and their associations with metabolic and cardiometabolic outcomes.
- Contribute to high-quality scientific publications, conference presentations and the dissemination of research findings.
- Collaborate with multidisciplinary researchers across genetic epidemiology, statistical genetics, genomics, clinical medicine and data science.

You will have opportunities to develop your expertise in statistical genetics and genetic epidemiology, contribute to innovative analytical approaches and build your publication record within an internationally collaborative research environment.

You will have:

- A PhD in genetic epidemiology, statistical genetics, bioinformatics, biostatistics, computational biology or a closely related discipline.
- Experience analysing large-scale genetic, genomic or epidemiological datasets, ideally including UK Biobank.
- Strong computational skills, including proficiency in R and/or Python and experience working in Linux-based computing environments.
- Knowledge of GWAS methodology, including genetic data quality control, association testing and interpretation of results.
- Knowledge of Mendelian randomisation methods, including instrumental variable assumptions, sensitivity analyses and interpretation of findings.
- Knowledge of post-GWAS approaches, such as fine-mapping, functional annotation, gene prioritisation, colocalisation or pathway analyses.
- An understanding of genetic epidemiology and statistical methods used to investigate the genetic architecture and biological mechanisms of complex diseases.
- Evidence of high-quality research outputs, including peer-reviewed publications.
- Excellent analytical, organisational and communication skills, with the ability to work independently and collaboratively within multidisciplinary research teams.
- Experience in obesity, type 2 diabetes, cardiometabolic disease research or the genetic analysis of adiposity-related traits would be advantageous.

We offer:

- Access to large-scale genetic and biomedical datasets, high-performance computing infrastructure and secure analytical environments.
- Opportunities to investigate the genetic and biological mechanisms underlying obesity heterogeneity, type 2 diabetes and its complications.
- Collaboration with leading researchers across human genetics, genomics, epidemiology, clinical medicine and computational science.
- Opportunities for high-quality publications, scientific conferences, professional development and career progression.
- A competitive University benefits package, including pension provision and generous annual leave.