AsianScientist (Mar. 30, 2015) – Scientists have developed a new technique that simplifies the task of identifying the precise DNA mutations that cause disease. Their work, published in Nature Methods, could aid drug development and find new ways of diagnosing diseases.
Connecting genetic mutations to specific diseases is currently either done by genotyping or sequencing. In genotyping, only regions of DNA with known variations are compared. While this makes the analysis simpler, genotyping does not allow for the detection of unknown variations. Conversely, genome-wide association studies (GWAS) tend to find mutations that are outside coding regions, with few that are eventually linked to the disease.
Instead, a team of scientists led by Dr. Shyam Prabhakar, Associate Director for Integrated Genomics at the Genome Institute of Singapore (GIS), has developed a new genetic analysis technique that does not require either genotyping or whole-genome sequencing.
Called the Genotype-independent Signal Correlation and Imbalance (G-SCI) test, the new method senses specific chemical modifications within the genome and connects them to nearby genetic mutations. When combined with a chemical profiling strategy known as ChIP-Seq, the G-SCI test was ten times more sensitive than existing methods at identifying detrimental gene mutations.
The study’s co-lead, Dr. Ricardo del Rosario from GIS, said, “The G-SCI test is transformative—instead of examining gene expression correlations in 500 individuals, we can get away with histone acetylation analysis of a mere 50 to 60. This reduces the number of test subjects needed to conclude each study and gives us the ability to look into multiple diseases.”
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.”
Applying the G-SCI test to a over half a million single-nucleotide polymorphisms (SNPs) within gene regulatory regions identified by histone acetylation ChIP-Seq, the researchers were able to identify 8,764 quantitative trait loci (QTL). These so called histone acetylation QTLs (haQTLs) were highly predictive of autoimmune disease, demonstrating that the G-SCI method can be used to simplify the identification of mutations which cause disease.
“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 bulls-eye with the G-SCI test,” said Prabhakar.
The article can be found at: del Rosario et al. (2015) Sensitive Detection of Chromatin-Altering Polymorphisms Reveals Autoimmune Disease Mechanisms.
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Source: A*STAR; Photo: Shutterstock.
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