By now we’re all familiar with the SNP, but what about the CNP, or copy number polymorphism? A group of researchers led by Michael Wigler at Cold Spring Harbor Laboratory have recently shown that CNPs, or variations in individuals’ number of gene duplications or deletions, may play a surprisingly important role in accounting for phenotypic differences between individuals.
Scientists have long been familiar with the concept of the CNP, but haven’t had a way to accurately measure the extent to which they’re present in the population. The solution, according to Jonathan Sebat, a postdoc in Wigler’s lab and the lead author of a paper that appeared in Science in July, was to employ a technique called representational oligonucleotide microarray analysis, or ROMA. In previous studies, Sebat and his colleagues had attempted to use ROMA to analyze the differences between normal and tumor tissue, and soon realized they needed to establish a baseline for the amount of CNP variation among normal tissue samples.
When Sebat and his fellow researchers compared the genomes of 20 normal individuals using ROMA, they found 76 unique large-scale CNPs (involving at least 100 kilobases) affecting 70 different genes. These genes have been associated with Cohen syndrome, neurological development, regulation of metabolism, leukemia, and drug resistant forms of breast cancer, among other conditions.
Sebat says using CNPs as clues to the cause of genetic disease represents a departure from the well-popularized linkage analysis approach to studying the genetic components of health. Rather than seek out the most common variants to use as markers for a particular disease as in linkage analysis, Sebat and his colleagues are betting that CNPs — despite their relative rarity — may more frequently turn out to be the cause of complex diseases. “It’s coming at the genetic disease problem from almost the exact opposite direction,” he says.
“In terms of what genetic variants most contribute to disease, I suppose the field is probably split as to what’s more important: lots of common ones or relatively few rare ones,” Sebat adds. “If the latter turns out to be the most frequent cause of these complex diseases, then we have a very good tool for finding those on a genome-wide scale.”
— John S. MacNeil