Whereas all humans share an identical sequence of base pairs (AT or GC) in most parts of their genome, once every 1,200 base pairs or so the sequence will differ—for example, the letters might be transposed. Those points of variation are known as single nucleotide polymorphisms, or SNPs (pronounced “snips”), and where there’s a SNP on the long strands of DNA known as chromosomes, there may be a gene implicated in a disease-causing mutation. So if people with diabetes, for example, share the same aberration of chemical base pairs at a particular SNP, researchers will look at that piece of the chromosome for one of the many genes thought to contribute to the disease.

Even before publication of the human genome—and the subsequent International HapMap Project, completed in 2005, which identified haplotypes, chromosomal “neighborhoods” in which certain genetic variants cluster—Framingham was trying to mine its family data to identify genetic variations. Today Framingham’s geneticists can be much more precise, scanning the entire genome using gene chips that test subjects’ DNA along thousands of SNPs. “Ten years ago, we could look at only 350 genetic markers at a time,” says L. Adrienne Cupples, professor of biostatistics at Boston University School of Public Health. “Now we have a 1-million-SNP chip.”

Last September the Framingham study released an analysis of its first genomewide association study, which tested DNA from 1,345 Framingham participants against 100,000 SNPs. Researchers looked for associations between the SNPs and what they knew about the people being tested. They considered 987 individual characteristics known as phenotypes—lipid levels, blood pressure, bone mass, presence or absence of cancer or diabetes, and so on—to determine which genetic variations may cause or contribute to those traits. So if, for example, hypertensive Framingham participants have a SNP that reads ATCG, but the SNP of those with normal blood pressure reads attg, that’s evidence the SNP is associated with hypertension. The next step is to hone in on that area to find SNPs that the genome scan didn’t detect because there are often multiple mutations clustered together. Once they know the full scope of the variation, researchers can try to find the real functional mutation—the gene.

The 100,000-SNP scan was conducted in early 2006; Framingham followed up in 2007 with a genome-wide association scan covering 550,000 SNPs—80% to 90% of the locales on the genome at which geneticists think they’re likely to discover disease-causing genes. Called the SHARe (SNP Health Association Resource) Project, the scan tested the DNA of more than 9,000 Framingham participants from three generations. (The analysis won’t be complete for another six to 12 months.)

As the acronym implies, the results from SHARe will be available to qualified researchers around the world via an online database developed by the National Library of Medicine’s National Center for Biotechnology Information. “Many more discoveries will come from this research if we collaborate with researchers conducting other genome studies, which is absolutely critical to moving the field forward,” O’Donnell says.

Because Framingham may well have collected more data on its subjects’ physical traits than any other study in the world, it is poised to find genes causing dozens of diseases and could also help solve the mystery of how multiple genes interact—enabling scientists to understand, say, the effect that genes regulating insulin have on those controlling cholesterol. “How fast we can come up with a therapy to alter the expression of genes for a particular disease will depend on how many SNPs affect the risk of heart disease, for instance,” Cupples says. Researchers already know there are many genes involved, but if some of those genes lie on the same pathway, blocking that path with a drug might lower a person’s risk for heart disease.