A team of Harvard researchers have recently developed a novel way to pinpoint, with greater accuracy than ever before, genetic mutations that drive evolution—and the new method of examining natural selection’s footprint may have tremendous implications for biomedicine and studies of human evolutionary history.
“This is a step towards identifying specific adaptations that have risen as humans expanded and faced new environments,” said Shari R. Grossman ’08, the lead author of the paper.
For years, scientists have sought to differentiate mutations that actually lead to selective change in a population from mutations that simply arise by chance. But current methods can only identify large stretches of genome as potential selection markers, according to Grossman.
Published online Thursday in the journal Science Express, the paper describes a new algorithm that can identify mutations with a much higher resolution. The method, called "Composite of Multiple Signals", assigns a score to every mutation in a region and then ranks the probability of each mutation influencing the selection of particular traits.
Researchers hope that such knowledge could eventually lead to new drug therapies and ways to fight infectious diseases.
CMS has shown potential for many applications before it saw full development, according to the paper's senior author Pardis Sabeti, a Harvard assistant professor of organismic and evolutionary biology and associate member of the Broad Institute of Harvard and MIT.
Previously, using an approach similar to CMS, Sabeti’s lab had localized a mutation in a gene called EDAR that may be responsible for Asians having less body hair. Sabeti also cited genes influencing lactose intolerance and malaria resistance as areas her team has already researched and hopes to further shed light on with CMS.
"If we keep pursuing these signals, we think we can identify a lot of mutations that can confer resistance to infectious disease,” Sabeti said.
Currently, analysis with CMS has been limited to human genes, but judging from its potential usefulness, Harvard Biology Professor Daniel L. Hartl said he predicts the new method may soon be used to analyze other organisms’ genomes as well.
“This represents significant progress on a very difficult problem,” said Hartl, whose research also focuses on how gene evolution affects organisms at the molecular level.
“[CMS] doesn’t solve all the problems, since there’s so much variation in the human genome," he said. "But it allows you to narrow down the field, and that’s a big improvement."
Data used in the study was obtained from The International HapMap project, a collaborative project of scientists worldwide to develop a complete haplotype map of the human genome.
The study was funded by the Packard Foundation, the Burroughs Wellcome Foundation, and Harvard University.
—Staff writer Helen X. Yang can be reached at email@example.com.