Algorithm Mines Big Data, Finds Gene Linked to Psychiatric Disease

We have a wealth of human genome data overlaid with gene ‘hotspots’ linked to the development of particular diseases. The problem? These massive datasets are much too complex and time-consuming for scientists to comprehend.

On the hunt for genetic influences of psychiatric conditions such as schizophrenia, autism spectrum disorder, and epilepsy, a team of researchers from the University of Copenhagen have turned to advanced computing platforms for a helping hand. The researchers, led by Konstantin Khodosevich, found a needle in the haystack: a gene called Klf13. 

“We first identified the gene as a plausible link in big data for brain development, and then we went on to test whether this gene did drive the development of the disease using in two mouse models,” explained Khodosevich, adding that the mice showed behavioral changes that mirrored those often experienced by individuals with common psychiatric conditions. The study was published in the journal Biological Psychiatry.

Interestingly, while trawling through vast datasets, the team observed patterns indicating that only Klf13 appeared active during normal fetal brain development. However, a reduction in the expression of the Klf13 was closely tied to the future onset of psychiatric illness. 

Khodosevic hypothesizes that Klf13 may be altering or inhibiting the proliferation and maturation of neural stem cells, which may ultimately result in the onset of psychiatric disorders.