Quantifying the “gene for” fallacy

Using game theory, scientists show single gene knockouts miss some gene functions

“Gene for obesity discovered.” “Scientists find gene for bad manners.”

Such “gene for” headlines often oversimplify the complex sets of genes that underlie many biological traits. A new study has used game theory to show just how much of the picture single-gene studies are missing.

The standard way to find the functional role of a gene is to engineer organisms to lack that gene and see whether the organisms behave differently.

Using a well-verified computer model of the inner workings of brewer’s yeast, researchers in Israel and Germany compared this single-gene approach with studies that “knock out” two, three or four genes at a time.

When taken together, single-gene studies missed at least 33 percent of the genes important for the growth of yeast cells when compared with the genes found by multiple-gene knockouts, the scientists report online and in an upcoming BioMed Central Systems Biology.

“You can actually gain a lot of information if you do multiple knockout instead of single knockout,” says coauthor Eytan Ruppin, a computational biologist at TelAvivUniversity in Israel. “This [study] exactly quantifies the extra contribution of these multiple-gene knockouts.”

Combinations of several genes can work together to produce a single trait, so investigating each gene one by one doesn’t always reveal a gene’s function. Also, some pairs of genes perform redundant tasks, so removing either one of the genes will have no effect, and researchers might conclude that the genes don’t play essential roles.

“One has to remember that apparently redundant genes have been maintained in the budding yeast genome for some time, so they cannot be evolutionarily inert,” comments Brenda Andrews, a molecular biologist at the University of Toronto in Canada.

Ruppin and colleagues ran computer simulations of a yeast cell with various combinations of genes missing, noting in each case how the missing genes altered the growth of the virtual cell. Then they used game theory to isolate the contribution of any one gene.

Game theory is a branch of mathematics that studies how players in a game will compete or cooperate in various scenarios. Lloyd Shapley — a famous game theorist and contemporary of Nobel Prize winner John Nash, the mathematician depicted in the movie A Beautiful Mind — devised a way to quantify how much each player contributes to the different outcomes of a series of collaborations.

In this case, each “collaboration” was a multiple-gene knockout experiment, and the outcome of each collaboration was a change in the yeast cell’s behavior. Using Shapley’s theory, the researchers could discern the function of each gene.

Ruppin says that, because gene interaction networks in human cells are much more complex than those in yeast cells, he expects that single-knockout studies will miss an even greater percentage of human genes’ functions than the researchers found for yeast cells.

The team plans to repeat the experiments with computer models of human cells.

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