WGHA work spawns new Golden Helix app
The result is a whole genome homozygosity association (WGHA)—and new Golden Helix software.
BOZEMAN, Mont.—When Todd Lencz, PhD, associate director of research in Psychiatry Research at the Zucker Hillside Hospital campus of the Feinstein Institute for Medical Research in Glen Oaks, N.Y., wanted to develop new ways to look at data from whole genome analysis on schizophrenia, he contacted his software company, Golden Helix, Inc. The request resulted in a new methodology—whole genome homozygosity association (WGHA)—and new Golden Helix software.
The project began when Lencz inspected raw data, looking at individual single nucleotide polymorphisms (SNPs) and noticed patterns that were runs of homozygosity, stretches of DNA inherited identically from both parents. Lencz says his call to Golden Helix was to "find a way to quantify this and to really move from just a simple observation to a rigorous statistical algorithm." Christophe Lambert, Golden Helix's president and CEO, obliged, says Lencz, resulting in a collaboration where Lencz provided biological and statistical input and Lambert wrote code.
"This was really complicated," says Lencz of his piece of the project. "This was the hardest thing I've ever done in my life professionally." Although perfecting the software took months, says Lencz, it developed enough during six weeks of fall 2006 that he informally presented results to colleagues.
Lambert says in a prepared statement that "Golden Helix developed a prototype with HelixTree's python scripting interface, and Todd used the prototype to perform his study. When successful publication became imminent, and it was clear there was interest from other customers, we converted the prototype into a fast C++ implementation within our SNP & Variation Suite."
The product is now available in Golden Helix's whole genome module for HelixTree.
Interpreting results to find patterns in whole genome data and identify recessive effects was not easy because the analysis was so new. Lencz's team, though, found nine genetic risk factors for schizophrenia. Significantly, four of the regions with runs of homozygosity contain genes that previous research had already associated with schizophrenia, providing corroboration for the method. The other five regions have been shown to affect neurons.
That validation, says Josh Forsythe, director of marketing at Golden Helix, "kind of confirms that, Wow, we have some kind of valid method here." Forsythe says WGHA extends analysis of existing SNP data, "but it's a new method for doing analysis on the same data.You don't have to get new data to do it."
Lencz sees potential for broad application of WGHA among microarray users. He's already looking at WGHA for researching other illnesses and collaborating on a nonpsychiatric indication. Still, though Forsythe considers WGHA another powerful tool in the arsenal for finding association, he says "this is what we consider a bleeding edge method. It's got one publication."
That publication, an article in Proceedings of the National Academy of Sciences, details Lencz's team's findings among 178 schizophrenia cases and 144 healthy controls. More research needs to confirm the results, but Lencz says some of the risk regions are "almost exclusive to patients with schizophrenia," meaning they could someday have value in diagnostics or, ultimately, identifying potential drug targets. Zucker Hillside Hospital, he says, could license study results for either purpose.