Big data reveals genetic clues in humans

After analysing big data about a key protein, computer scientists at Washington University in St. Louis have discovered its connection in human history as well as clues about its role in complex neurological diseases.

Through a novel method, Sharlee Climer and Weixiong Zhang discovered a region encompassing the gephyrin protein - master regulator of receptors in the brain that transmit messages - on chromosome 14 that underwent rapid evolution after splitting in two completely opposite directions thousands of years ago.

Those opposite directions, known as yin and yang, are still strongly evident across different populations of people around the world today.

Malfunction of the gephyrin protein has been associated with epilepsy, Alzheimer's disease, schizophrenia and other neurological diseases.

The research team used big data from the "International HapMap Project", a public resource of genetic data from populations worldwide designed to help researchers find genes associated with human disease, as well as from the "1000 Genomes" project, another public data source of sequenced human genomes.

In total, they looked at the genetic data from 3,438 individuals, said the study that appeared in the journal Nature Communications.

When they analysed the data, they made an interesting discovery in a sequence of markers called a haplotype, enveloping the gephyrin gene.

Using the data from the HapMap Project, they looked at the gephyrin region in several populations of people, including European, East and South Asian and African heritage, and found variations in the haplotype frequencies of each of these populations.

Those from African origin generally have more yang haplotypes, while those of European origin have more yin haplotypes.

Those of Asian descent have nearly equal numbers of yin and yang haplotypes.

Ultimately, the team expects this method will shed light on the genetic roots of disease.

Most complex diseases arise due to a group of genetic variations interacting together.

"Different groups of people who get a disease may be affected by different groups of variations. We are taking a combinatorial approach - looking at combinations of markers together - and we are able to see the patterns," Climer concluded.