Faculty scientists have utilised a new way of working to identify a gene linked to neurodegenerative diseases such as Alzheimer’s. The discovery may help to identify which people are most likely to develop the condition.

The team compared genes in mice and humans. Using brain scans from ENIGMA Consortium and genetic information from The Brain scansMouse Brain Library, they were able to identify MGST3, a novel gene which regulates the size of the hippocampus in both mouse and human. This gene was shown to be linked to neurodegenerative diseases. Dr Reinmar Hager, senior author of the study, said:

“What is critical about this research is that we have not only been able to identify this specific gene, but also the networks it uses to influence a disease like Alzheimer’s. We believe this information will be incredibly useful for future studies looking at treatments and preventative measures.”

The team used two of the world’s largest collections of scientific data, The ENIGMA Consortium and The Mouse Brain Library. The ENIGMA Consortium is led by Paul Thompson, based at the University of California. It contains brain images and gene information from almost 25,000 subjects. The Mouse Brain Library, established by Robert Williams from the University of Tennessee Health Science Centre, contains data on over 10,000 brains and numerical data from more than 20,000 mice. David Ashbrook, a researcher in Dr Hager’s team, explained why combining the databases was so useful:

“It is much easier to identify a genetic variant in mice as they live in such controlled environments. By taking the information from mice and comparing it to human gene information, we can identify the same variant much more quickly. We are living in a big data world thanks to the likes of the Human Genome Project and post-genome technologies. A lot of that information is now widely shared. By mining what we already know we can learn so much more, advancing our knowledge of diseases and ultimately improving detection and treatment.”

For more information, please read the full paper which was published in BMC Genomics.

For further enquiries, please contact david.ashbrook@manchester.ac.uk

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