Modern genotyping technologies offer new opportunities to explore how genes influence health and disease, but also present the challenge of analyzing huge amounts of genetic and clinical data.
With this in mind, investigators at the Lausanne Branch of the international Ludwig Institute for Cancer Research (LICR), the Swiss Institute of Bioinformatics (SIB) and the University Hospital of Lausanne have developed AssociationViewer, a computational tool that displays genetic differences between individuals on a large scale. Presented in the March 1st (Vol. 25/5) issue of Bioinformatics, the software is a public resource that will help scientists and physicians discover new genetic markers for diseases and other conditions.
Less than 0.5% of the human genome differs between individuals, but this small fraction holds clues that can reveal how each person will respond to a particular disease, therapy or environmental factor. In the past years, genome-wide association (GWA) studies—comprehensive surveys that look for differences in people's DNA code—have uncovered millions of small genetic variations known as single-nucleotide polymorphisms (SNPs). By sharing GWA data, scientists have gained insight into the genetic basis of many complex diseases including cancer and heart disease, and embarked on developing personalized therapeutics.
According to Dr. Nicolas Guex, a senior bioinformatician at the SIB, those who carry out GWA studies need simple ways to integrate datasets from different sources. They also rely on tools that help them visualize SNPs across the entire map of the genome. "AssociationViewer provides an elegant solution to efficiently mine the wealth of data to generate hypotheses," he says. Another user, Dr. Sam Deutsch at the U.S. Department of Energy Joint Genome Institute, USA, also considers the software to be a powerful tool for computational genetics. "Compared to other tools out there, AssociationViewer brings more flexible integration of external data sources and incorporates novel functionalities for comparing results across different studies." He adds that special features allow users to quickly zoom into relevant regions the genome. "Visualizing significant SNPs in their genomic context is very intuitive," he says.
Dr. Ioannis Xenarios, co-author of the article and Director of the Vital-IT group at the SIB, comments that data sharing remains a challenge for GWA studies, since many scientists still are reluctant to upload their unpublished data on public genome browsers. "AssociationViewer is a way to alleviate data transfer problems and allows people to perform their analyses by tapping into essential external resources. We hope that this tool will deliver new ways to mine complex genomic features."
"The software was developed on the principle that a picture is worth a thousand words," says the study's senior author, Dr. Brian Stevenson at the LICR Lausanne Branch. "It stems from a very successful collaboration between the LICR Computational Genomics Group, the SIB Vital-IT group and researchers who generate association data at the University Hospital of Lausanne, and should prove popular among clinicians, biologists and computer scientists."
This study was conducted by investigators from: Ludwig Institute for Cancer Research (LICR) Lausanne Branch, Switzerland; Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland; and the Institute of Microbiology, University Hospital, University of Lausanne, Lausanne, Switzerland. Funding was provided by LICR, the Swiss National Science Foundation, Infectigen, the University of Lausanne and SIB.
Materials provided by Ludwig Institute for Cancer Research. Note: Content may be edited for style and length.
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