Epistasis research takes centre stage

December 20th, 2024

AEHRC researchers are shedding light on the relationship between gene interactions and susceptibility to common diseases.

Bioinformatics research scientists Drs Natalie Twine and Letitia Sng are working to advance epistasis detection so that researchers can identify more gene interactions associated with diseases.

Dr Natalie Twine.

Epistasis explains how genes interact with each other to influence our physical traits, including predisposition to genetic diseases.  

Understanding more about these interactions will help scientists assess people’s susceptibility to diseases like Alzheimer’s and cardiovascular disease more accurately, allowing them to access early treatment and intervention. 

The problem is that detecting epistasis is extremely challenging.  

There is an endless number of possible gene combinations, which has proven a statistical and computational nightmare. 

Fortunately, AI and machine learning now offer advanced computational solutions to help researchers detect epistatic interactions.  

The use of these technologies in epistasis detection is examined in a new article co-led by Natalie and Letitia in Genome Biology. 

The article consolidates learnings from a workshop they co-led in 2023 to identify key challenges in epistasis detection and offer practical solutions. 

Dr Letitia Sng.

The weeklong workshop at the Lorentz Centre in the Netherlands assembled 41 experts with diverse backgrounds and methodologies to address challenges in epistasis detection.  

Six international experts on epistasis, including Natalie and Letitia led the workshop.

It facilitated valuable discussion and collaboration between specialists and evaluated the benefits and limitations of different approaches.  

Epistasis research has received significant interest from the global genetics’ community in the last few years, largely due to its potential to answer the missing heritability problem.  

Missing heritability refers to the discrepancy between heritability estimates based on genetic data from unrelated individuals in genome-wide association studies and data derived from twin studies. In other words, there are still gaps in the understanding of exactly how genes contribute to traits and diseases.  

Nevertheless, our researchers are determined to advance epistasis research and improve our understanding of genetic diseases.