New genetic analysis reveals principles of phenotypic expression

In the journal Chaos, which is published by the American Institute of Physics (AIP), scientists at the University of Alabama at Birmingham report powerful new techniques for studying the phenotypes related to genetic differences in the budding yeast, Saccharomyces cerevisiae. The researchers took yeast cultures from an extensive library of approximately 5,000 mutated strains and subjected them to hydroxyurea -- an anti-cancer drug with known effects on the cell cycle.

Using a method called quantitative high-throughput cellular phenotyping (Q-HTCP), the researchers analyzed growth curves for tens of thousands of individual cultures, "focused on finding all of the genes that modulate the cellular effects of the drug," says study co-author John Hartman, an assistant professor of genetics. The researchers then selected the 300 "most 'hydroxyurea-interactive' genes" and further classified the genes by testing their influence on cell growth after treatment with drugs acting by different mechanisms. To integrate the results from such experiments, the researchers developed a new data mining approach called Recursive Expectation-Maximization Clustering (REMc). The approach, Hartman says, "has advantages over prior methods with respect to defining cluster number and quantifying cluster quality," which augments biological discovery.

The technique, Hartman adds, "offers a new way for trying to understand how genetic variation -- such as that related to human disease -- is alternatively buffered or expressed." Understanding phenotypic expression at a systems level, he says, would help create a new field of medicine, dubbed "phenomics."