Kari Stefansson is in a hurry. The president and CEO of deCODE genetics must get across town to see a former colleague at Beth Israel Deaconess hospital before dashing to the airport-and he still needs to squeeze in a promised interview with a journalist. In the cab, he alternates between discussing travel plans in Icelandic with a coworker and expounding on the intersection of disease and human evolution in English with TR associate editor Erika Jonietz.Changing how genetics is done has kept Stefansson on this hectic pace since 1996, when he left Harvard Medical School and Beth Israel to found deCODE genetics, based in his native Iceland. In 1997 the company proposed construction of the first “phenotype database,” a collection of the health records of all 280,000 Icelanders. DeCODE would use information from this centralized health-care database and Iceland’s extensive public genealogy records to find disease-causing genes, aided by the relatively homogenous genetics of Iceland’s population. This plan helped launch a revolution in population genomics (see “Your Genetic Destiny for Sale”), with a variety of other companies quickly following suit.
Although it’s widely imitated, deCODE remains controversial. Editorials in publications from the New York Times to the New England Journal of Medicine attacked and defended the ethics and scientific merits of the proposal. After extensive public debate, Iceland’s parliament approved the creation of the Icelandic Health Sector Database in December 1998 and granted deCODE a 12-year license to create and operate the database last January. A vocal minority, including the physician-led group Mannvernd, is still trying to stop the database in the courts, but deCODE has moved ahead. Last July, the company completed a successful initial public offering that raised $244 million.
TR: You were a professor at a leading medical school. Why leave and switch directions so radically to found deCODE?
STEFANSSON: Basically, the methods of genetics are the methods that you use no matter the disease you are studying. Around the time I founded deCODE, the technology had evolved to the extent that it had stopped being the limiting resource; the limiting resource was becoming the access to populations to do this work. So I moved to a place where there were the least limitations on that resource. I think the Icelandic population is not unique, but it’s very well suited to do exactly what we want to do, which is to apply this new technology to a well-defined population.
TR: What has it been like for you to be a CEO instead of an academic scientist?
STEFANSSON: The difference between the two is vastly overrated. What mostly drives you is the desire to win, to perform, to control your own fate, whether you do it through money, or the admiration of people who follow your work or whatever. It’s a larger scale: I was running a lab of 10 or 15 people; I’m now running a company of about 450 people. But it is basically the same thing. You put together certain ideas, you gather around you people to execute them. The fact that you can create value out of the results of your research doesn’t alter in any way the weight or the importance of the fundamental question you are asking.
Also, don’t forget that I come from Iceland. My family has lived in Iceland for 1100 years. There is a certain adaptation that has taken place. We fit this sort of wet, barren, dark corner of the North Atlantic. That does not necessarily mean I like every aspect of it, and I miss America a lot. It was a great place for me; this was a community that was extremely generous to me. I learnt an awful lot here. I’m running a company in Iceland that is fundamentally run on American philosophy. It fits pretty well there.
TR: What specifically motivated you to create a phenotype database?
STEFANSSON: When you look at common diseases, they are probably common because they are complex, and they’re complex for all kinds of reasons. Not only because they probably require the confluence of many genes to cause the disease, but they may also require interactions between genes and the environment. And when you are studying complex common diseases, you have to work on the assumption that you know very little. In spite of 75 to 100 years of intense research into these diseases and an enormous expenditure of money, we have come a short distance in developing a useful understanding of these diseases, simply because the fundamental approach just doesn’t reach deep enough. You don’t see enough of a pattern to put together a reasonable hypothesis. You basically have to begin to use systematic data mining, to be able to bring together large amounts of information.