New AI model helps discover causes of motor neurone disease

Date:
January 18, 2022
Source:
University of Sheffield
Summary:
Scientists have developed a new machine learning model for the
discovery of genetic risk factors for diseases such as Motor
Neurone Disease (MND).



FULL STORY ========================================================================== Scientists have developed a new machine learning model for the discovery
of genetic risk factors for diseases such as Motor Neurone Disease (MND).


========================================================================== Designed by researchers from the University of Sheffield and the Stanford University School of Medicine in the US, the machine learning tool,
named RefMap, has already been utilised by the team to discover 690 risk
genes for motor neurone disease, many of which are new discoveries.

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.

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 which 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," Sai Zhang, PhD, instructor of genetics at the Stanford University
School of Medicine said.

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 personalised treatment and intervention." ========================================================================== Story Source: Materials provided by University_of_Sheffield. Note:
Content may be edited for style and length.


========================================================================== Journal Reference:
1. Sai Zhang, Johnathan Cooper-Knock, Annika K. Weimer, Minyi Shi,
Tobias
Moll, Jack N.G. Marshall, Calum Harvey, Helia Ghahremani Nezhad,
John Franklin, Cleide dos Santos Souza, Ke Ning, Cheng Wang,
Jingjing Li, Allison A. Dilliott, Sali Farhan, Eran Elhaik,
Iris Pasniceanu, Matthew R. Livesey, Chen Eitan, Eran Hornstein,
Kevin P. Kenna, Jan H. Veldink, Laura Ferraiuolo, Pamela J. Shaw,
Michael P. Snyder, Ian Blair, Naomi R.

Wray, Matthew Kiernan, Miguel Mitne Neto, Adriano Chio, Ruben
Cauchi, Wim Robberecht, Philip van Damme, Philippe Corcia,
Philippe Couratier, Orla Hardiman, Russell McLaughin, Marc Gotkine,
Vivian Drory, Nicola Ticozzi, Vincenzo Silani, Jan H. Veldink,
Leonard H. van den Berg, Mamede de Carvalho, Jesus S. Mora Pardina,
Monica Povedano, Peter Andersen, Markus Weber, Nazli A. Başak,
Ammar Al-Chalabi, Chris Shaw, Pamela J. Shaw, Karen E. Morrison,
John E. Landers, Jonathan D. Glass. Genome-wide identification of
the genetic basis of amyotrophic lateral sclerosis.

Neuron, 2022; DOI: 10.1016/j.neuron.2021.12.019 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/01/220118125148.htm

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