David Ulrich and Marc Lenburg


screen shot from GMED

Genetics and Genomics Department researchers have created a custom-built genetic data repository called GMED, which helped them quickly discover a common variation associated with obesity.  The study, which took only nine months, combined large amounts of data in multiple forms from over a thousand study participants to show that the variation, upstream of the gene INSIG2, is highly associated with increases in the obesity marker body-mass index, or BMI.

The study, published in the April 14, 2006 issue of Science, required researchers to compare and contrast many types of data, including microarray scans, genotype calls, chromosomal locations, phenotypes and pedigrees. Lacking a suitable, user-friendly system to sort all this information into biologically meaningful results, Marc Lenburg working with David Ulrich combined new and existing technologies to create a repository that their entire team could use, regardless of technical experience.

"Doing an analysis can be a fairly sophisticated undertaking," said Marc Lenburg, co-director of the Boston University Microarray Resource, and an author on the study. "Since we had a large collaborative team of people who work on this project, one of the things we wanted to do was put the analytic results into a simpler format so that people of varying degrees of technical sophistication could browse the results as they were being generated."

As the technology to perform large-scale disease-gene searches has developed, researchers and bioinformatics specialists have been challenged with creating the computational infrastructure to gather, sort and analyze their data.  Enlisting help from Affymetrix, Stratagene and IBM, the Genetics and Genomics team was able to simultaneously conduct their experiments while creating their database to rapidly analyze their genetic variation and determine its relationship to obesity.

Read the Affymetrix Microarray Bulletin interview with Marc Lenburg to learn more about the IT challenges of a whole-genome association scan and the system the BU scientists put together for meeting them.