Impact of Genetic Regulation of Molecular Phenotypes and its role on Disease Development

Start Date: From October-2024 (Flexible)

This research project aims to elucidate how genetic and environmental factors influence molecular processes and how these in turn drive disease development. By leveraging state-of-the-art sequencing technologies and comprehensive molecular assays, we seek to identify the network of genetic and molecular drivers of disease. For this, the student will utilize multi-omics datasets, including DNA sequencing, transcriptomics, nanopore long-read RNAseq, proteomics, and metabolomics, from diverse cohorts and studies. These large cohort datasets with millions of data points will be combined using computational data integration methods with more classical statistical genetic methods to infer patterns that describe the relationship between molecular phenotypes. Using large genetic datasets, both hosted in Dundee and elsewhere, we will explore how these molecular effects translate to effects on disease development.

Specific aims include:

– Explore the effect of genetic variants on transcript expression, alternative splicing, and protein production in disease.

– Develop and apply data integration models and machine learning for multi-omics datasets to identify molecular profiles of disease and disease subtypes, aiming to develop predictive models for diagnosis.

– Investigate the causal role of molecular phenotypes in disease development using longitudinal multi-omics datasets and large-scale genetic Biobanks. 

This project presents multiple training opportunities for a candidate interested in acquiring skills in data science and clinical data analysis, including building predictive models of disease risk using machine learning methods and the analysis of high dimensional data. These include short and long read sequencing data, proteomics and other molecular assays. Strong emphasis on developing communications skills, with the student expected to present at large international conferences and at relevant consortia meetings. The PhD candidate will have access to extensive genetic, molecular and clinical datasets from multiple human tissues and will work with biologists, clinicians, statisticians, computational biologists at the University of Dundee and internationally, as well as collaborators from the pharmaceutical industry.

Candidate Requirements:

– A strong academic background in one or more of the following areas: biology, genetics, biomedical sciences, bioinformatics, informatics, statistics, mathematics or a related discipline.

– Experience with genetic association methods (e.g.: GWAS, eQTLs) or analysis of molecular datasets assays (e.g.: RNAseq, DNAseq, mass spectrometry) is desirable but not essential.

– Prior training on data analysis and programming languages and environments (e.g., R, Python, linux, HPC) is advantageous.

– Excellent communication skills and the ability to work collaboratively within a team.

– Motivation, enthusiasm, and a passion for scientific discovery.

This fully funded PhD will cover home fees (UK citizen or permanent resident) and provide a stipend set at UKRI levels.

For further information about the project and informal inquiries, please contact Ana Viñuela, PhD at [email protected].