The method, which does not involve genome sequencing, could save time and resources say scientists
Scientists at the Agency for Science, Technology and Research's Genome Institute of Singapore (GIS) have developed a new technique that simplifies the task of identifying the precise DNA mutations that cause disease, laying the groundwork for the development of new drugs and new ways of diagnosing diseases.
Scientists believe that pinpointing genetic mutations of a disease which then can be correlated with the corresponding mutations in the proteins can lead to new precision drugs. This can be combined with measuring the activity of these proteins for more efficient diagnosis.
The team of scientists, led by Dr Shyam Prabhakar, Associate Director for Integrated Genomics at GIS, developed a new genetic analysis technique, called the Genotype-independent Signal Correlation and Imbalance (G-SCI) test, that senses specific chemical changes within the genome and connects them to nearby genetic mutations. They then showed that mutations associated with the chemical changes were also likely to cause disease. The G-SCI test was validated in a study of 57 individuals and is reported in the scientific journal, Nature Methods.
Dr Jeremie Poschmann from GIS, the other co-lead of the study, highlighted another benefit of the new approach: ‘Instead of using genome sequencing, we can use the histone acetylation sequencing data from our method to detect DNA mutations. This saves us a huge amount of time, effort and resources.’
Prof. Bing Ren of the Ludwig Institute of Cancer Research and the Department of Cellular and Molecular Medicine, University of California, San Diego (UCSD), said: ‘This is an exciting study that sets a new benchmark for genetic analysis of gene regulation. The method greatly enhances our ability to interpret the human genome and will benefit research into the genetic basis of diseases.’
The scientists validated the efficiency of the new test and were able to identify links to certain genetic diseases.
‘We have found a strong association between mutations that perturbed the genome’s chemical state and those that caused autoimmune diseases. That’s when we knew we had hit the bullseye with the G-SCI test,’ said Dr Prabhakar.
‘This work provides important new tools for linking genetic variation to variation in chromatin function, and provides compelling evidence for the central role of this type of genetic variation in human disease,’ said Prof. Jonathan Pritchard from Stanford University, who is also an investigator at the Howard Hughes Medical Institute.
The G-SCI method utilises epigenetic information to select regions of the human genome for disease association analysis, said Prof. Huck-Hui Ng, Executive Director of GIS.
‘As we see bigger and more complex datasets, the community will face the forthcoming challenges of analysing big data. This method has expanded our arsenal of computational analytics capabilities at the Genome Institute of Singapore,’ he said.