Researchers have developed a new computer tool that uses SNPs to identify disease pathways. Known as VarSAn, the tool can spot DNA mutations in a patient’s sample and identify pathways to certain diseases, including breast and prostate cancer and a rare form of heart disease, known as Hypoplastic Left Heart Syndrome. The algorithm further demonstrates the power of SNPs at identifying life-limiting conditions, including cancer.
Single nucleotide polymorphisms (SNPs) are genetic mutations that appear in the DNA. Scientists, including the RGCC and Biocentaur teams, have established links between genetic mutations and pathways to disease, using SNPs to provide an “early warning” system. VarSAn works by automating this process, using a computer algorithm to identify SNPs known to play a role in disease pathways, say scientists in a paper published in Nucleic Acids Research.
“We’re trying to approach the problem from a computational point of view,” said Saurabh Sinha, Director of Computational Genomics at the Carl R. Woese Institute for genomic biology, University of Illinois at Urbana-Champaign and co-author of the paper. “Does the whole gamut of SNPs identified in a genetic study point us to specific pathways that may not be known in the literature?”
The study was a part of the Mayo Grand Challenge, a programme that aimed to improve understanding of a rare form of heart disease that affects children, known as Hypoplastic Left Heart Syndrome. “Our collaborators at the Mayo Clinic had sequenced the DNA of the children and their parents, and our colleagues at UIUC had identified mutations that were present in the children but not the parents. After that, we developed a tool to analyze the data to understand the disease pathways better,” said Sinha.
The tool uses two methods to analyse each SNP. Including using information from published scientific literature and another way to assess the consistency of the algorithm’s findings. Doing so provides both subjective and objective analysis, making results more reliable.
The powerful algorithm has clear clinical potential, providing an accurate, evidence-based analysis to identify SNPs and disease pathways. The plan is to make the algorithm free for users through an online portal, says co-author Xiaoman Xie, a graduate student in the Sinha lab. “Currently, if a user wants to use this tool, they have to download the repository and run the code themselves, which can be inconvenient. We’re working on making it easier.”
The new algorithm is an exciting illustration of the potential of SNPs. At Biocentaur, we use SNPs to help clinicians understand how patients will respond to specific drugs and treatments, including anti-inflammatories, antibiotics and anticoagulants.
Our SNPs test uses a sample of DNA that we use to run a series of tests to identify genetic mutations that could impact how effective a treatment (or combination of treatments) would be. This can help clinicians make better treatment choices, and patients experience the best outcomes.
You can read the full paper, VarSAn: associating pathways with a set of genomic variants using network analysis, here.