We are currently missing the causes of many diseases simply because we don’t yet have a tool to say which synonymous mutations cause disease.
Synonymous mutations are genetic mutations that do not affect the encoding of proteins. Historically, because of their ‘silent’ effect, they were thought to have no influence on an organism’s fitness and hence be neutrally evolving (i.e., evolving randomly through genetic drift rather than selection).
However, more recent studies have identified that synonymous mutations have phenotypic consequences (an impact on observable characteristics or traits) as they perturb multiple mechanisms throughout gene expression. Importantly, they have been shown to commonly be under selection, and that this selection can be strong enough to cause genetic diseases.
My research aims to apply the latest Artificial Intelligence techniques to address a key question: can we predict which synonymous mutations will cause disease?
Bridging the gap between evolution and medicine
My research builds on our understanding of the features of synonymous mutations of known effect. I am using what has been learnt from population genetics, recent developments in Artificial Intelligence, and the immense power of publicly available data resources to develop a machine learning model that can predict the likely ability to produce disease of mutations at any synonymous site in the human protein-coding genome.
This project uses data science to bridge the gap between evolution and medicine, thereby providing an elegant and practical example demonstrating why understanding evolution matters. To bring the best of these two fields together, I work closely with the Milner Centre for Evolution at the University of Bath and the Milner Therapeutics Institute at the University of Cambridge.
Potential to improve healthcare
Our genes have an important role to play in defining biological fitness. I am fascinated by the Theory of Evolution and how selection pressures can shape changes to the genetic makeup of a population over time. I hope that my research will help advance state-of-the-art in the clinical application of evolutionary genetics and improve healthcare.
Following my PhD, I plan to apply my scientific curiosity to the pharmaceutical field, identifying therapies for inherited diseases through genomics studies.
The EET scholarship is helping me achieve my ambitions by granting me the chance to pursue my interest in evolutionary genetics and its application to the field of diagnostics and therapeutics.
A greater understanding of evolution can provide us with insights into the mechanisms through which inherited diseases arise and persist.
More about Sofia’s work:
Sofia is part of the organising committee for the Cambridge AI Club for Biomedicine