Gene Map Shortcut
Context: The Human Genome Project provided a parts list of our genes, but that alone cannot connect genetic variants with health; diseases and drug responses must be correlated with genetic markers that vary from person to person. The most common and easily assessed markers are single-nucleotide polymorphisms (SNPs), in which genetic sequences swap one DNA “letter” for another.
But an individual has many more SNPs than researchers can afford to measure, and until now, there have been no reliable tools for selecting the most representative ones. Recently, the biotech company Perlegen Sciences and its collaborators at the International Computer Science Institute and the University of California, San Diego, completed the first map of SNPs that provides these tools. This map will move us closer to an era in which patients’ genetic makeup routinely guides their medical treatment.
Methods and Results: To figure out which SNPs are most informative, researchers at Perlegen needed to know how frequently particular SNPs occur in a population and which ones are most likely to occur together. Led by David Cox, the researchers selected SNPs that represented all 46 chromosomes and whose least-frequent versions still occurred in 1 to 5 percent of individuals. Each occurred at least once in 71 volunteers identified as being of African, European, or Han Chinese ancestry; the researchers kept track of who had what SNPs and which ones were likely to occur together in each population. Overall, Perlegen studied nearly 1.6 million SNPs. The researchers compared their data with that from other studies of much smaller sections of the genome, where nearly all of the so-called common SNPs have been found. That comparison revealed that the SNPs that Perlegen studied could be used to predict the occurrence of most others that were not in its set of 1.6 million. In other words, the researchers found that studying a subset of SNPs captures most of the information about frequently occurring variations. They also found that the subset of common SNPs varied slightly among different ethnic groups.
Why it Matters: The Perlegen study shows that future genetic-variation studies might be easier and cheaper than previously thought. Because so many SNPs are correlated with each other, studies that measure fewer SNPs can potentially get results as powerful as those that measure many more, and the map the researchers created will help epidemiologists select SNPs to study.
A second map of SNPs, which surveys more people, is due out from the International HapMap Project later this year. The two maps will complement each other. For now, Perlegen has shown that these maps can be made and could provide a powerful tool to link genetic variations with disease and medicine.