ALS patient at his room
Color image of a real life young physically impaired ALS patient looking computer at his computer screen with the help of his electronic wheelchair.

A study by researchers from the University of Sheffield and Stanford University School of Medicine demonstrates how a new machine learning model can help uncover genetic risk factors for diseases such as MND.

Motor neurone disease (MND) is a rare condition that progressively damages parts of the nervous system, which leads to muscle weakness, often with visible wasting. MND, also known as amyotrophic lateral sclerosis (ALS), occurs when nerve cells in the brain and spinal cord called motor neurons stop working properly. This is known as neurodegeneration.

“ALS is a complex disease that leads to motor neuron death. Despite heritability estimates of 52%, genome-wide association studies (GWASs) have discovered relatively few loci,” the researchers write in the journal Neuron. “We developed a machine learning approach called RefMap, which integrates functional genomics with GWAS summary statistics for gene discovery.”

One of the genes highlighted as a new MND gene, called KANK1, has been shown by the team to produce neurotoxicity in human neurons 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.

“This new tool will help us to understand and profile the genetic basis of MND,” explained Johnathan Cooper-Knock, PhD, an NIHR clinical lecturer at the University of Sheffield’s Neuroscience Institute. “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 have led to a five-fold increase in discovered heritability.

“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 added: “By doing machine learning for genome analysis, we are discovering more hidden genes for human complex diseases such as MND, which will eventually power personalized treatment and intervention.”

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