T WO YEARS AGO, WHEN THE HUMAN GENOME PROJECT COMPLETED the first directory of all human genes, the stage was set for two great advances in medicine. First, scientists would more quickly identify the genes tha t foster common, chronic diseases such as Alzheimer’s or high blood pressure, and people would start having their genomes analyzed early in life to reveal their risks. Then, armed with that information, we would all adopt lifestyles—maybe even take medicines—tailored to our awn needs. Alas, ‘personalized genamics” is less simple than it sounds. Linking a gene to a disease can take years by con ventional techniques, and decoding one person’s entire gename would still be a multi-million-dollar endeavor. But the barriers are falling fast. Two brand new technologies—a recently published database known as the HapMap, and an analytical technique called massively parallel DNA sequencing—could soon make the dream a reality.

The human gename consists of 3 billion chemical bases, or letters, strung in a sequence over 23 pairs of chromosomes. Our individual genomes are largely identical, but there are 10 million points in the sequence where our individual codes can vary. These tiny discrepancies, known as polymorphisms, can be important markers of disease risk. The first challenge, a daunting one, is to determine whether people who share a particular health problem have polymorphisms in common. Imagine searching for a familiar house, knowing only that it is located somewhere in the United States. You can search every street in the country, start-ing in San Francisco and moving east but if the house is in Atlanta, you won’t find it for a longtime . The task becomes much easier if you can first narrow your search to a particular city or neighborhood.

That’s where the HapMap comes in. By analyzing DNA samples from people around the world, scientists recently identified a set of polymorphisms that efficiently identify all the “neighborhoods” of the human genome. The HapMap, basically a directory of those neighborhoods, makes the search for disease links faster and cheaper, because only a few hundred thousand polymorphisms need to be examined, instead of 10 million. Finding the likely neighborhood of a disease- related gene is just the first step . Locating the gene itself, and determining its role in the body, requires a meticulous search of the neighborhood. That means sequen-cing all the letters surrounding the polymorphism—tens of millions of them. Sequencing a neighborhood is far easier than sequencing an entire genome, but it still takes a lot of time and money. Fortunately, sequencing is experiencing a revo-lution of its own.

This fall, researchers unveiled a pair of new technologies that could boost the speed of gene sequencing tenfold, while greatly reducing the cost. The new techniques— one developed by 454 Life Sciences Corp. of Branford, Conn., and the other developed at Washington University and Harvard Medical School—involve shattering long strands of DNA into millions of pieces and sequencing the letters simultaneously. Once this ‘massively parallel” sequencing is finished, computers knit the fragmented data into a single sequence. These new techniques still lack the accuracy of conventional sequencing, and they can’t yet handle extremely long DNA sequences. But like silicon-chip microprocessors, they’re likely to become exponentially faster and cheaper as they evolve.

Together, the HapMap and the new sequencing technologies could transform science and medicine over the next 20 years. Researchers will identify genes that make us vulnerable to virtually all the major diseases. Innovators will develop greatly improved diagnostic tests and treatments, even cures. Doctors will scan our genes to determine which treatments are most likely to help us and least likely to cause side effects . Most important, many people born genetically vulnerable to serious disease will remain healthy—because they’ll know which bullets to dodge.

KOMAROFF is editor in chief of the Harvard Health Letter (

LABAER directs the Institute o f Proteomics at Harvard Medical School.

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