New AI model helps discover genetic risk factors for motor neurone disease

London:  Scientists have developed a new machine learning model for the discovery of genetic risk factors for ailments such as Motor Neurone Disease.

Designed by researchers from the University of Sheffield in the UK and the Stanford University School of Medicine in the US, the machine learning tool, RefMap, has been used by the team to trace 690 risk genes for motor neurone disease (MND), many of which are new discoveries.

One of the genes highlighted as a new MND gene, called KANK1, has been shown to produce neurotoxicity very similar to that observed in the brains of patients.

Although at an early stage, this is potentially a new target for the design of new drugs, Sheffield University said in a release on Thursday.

Dr Johnathan Cooper-Knock, from the University of Sheffield’s Neuroscience Institute, said, ”This new tool will help us to understand and profile the genetic basis of MND. Using this model we have already seen a dramatic increase in the number of risk genes for MND, from approximately 15 to 690.” ”Each new risk gene discovered is a potential target for the development of new treatments for MND and could also pave the way for genetic testing for families to work out their risk of disease.” The 690 new genes identified by RefMap lead to a five-fold increase in discovered heritability, a measure that describes how much of the disease is due to a variation in genetic factors.

”RefMap identifies risk genes by integrating genetic and epigenetic data. It is a generic tool and we are applying it to more diseases in the lab,” said Sai Zhang, PhD, instructor of genetics at the Stanford University School of Medicine.

Michael Snyder, PhD, professor and chair of the department of genetics at the Stanford School of Medicine and also the corresponding author of this work said, ”By doing machine learning for genome analysis, we are discovering more hidden genes for human complex diseases such as MND, which will eventually power personalised treatment and intervention.”