Researchers from Brown University have developed a new method for sifting through genomic data in search of genetic variants that have helped populations adapt to their environments. The technique, dubbed SWIF(r), could be helpful in piecing together the evolutionary history of people around the world, and in shedding light on the evolutionary roots of certain diseases and medical conditions.
SWIF(r) brings several different statistical tests together into a single machine-learning framework. That framework can then be used to scan genomic data from multiple individuals and compute the probabilities that individual mutations or regions of a genome are adaptive.
"These individual statistical techniques are useful, but none of them is particularly powerful on its own," said Lauren Alpert Sugden, a postdoctoral researcher at Brown who led the technique's development. "The method we've developed combines those techniques in a way that's careful and that produces an output that's easy to interpret."
Alpert Sugden works in the lab of Sohini Ramachandran, an associate professor and director of Brown's Center for Computational Molecular Biology. The researchers describe their work in the journal Nature Communications.