Professor Sohini Ramachandran of Brown University's Center for Computational Molecular Biology (she also serves as its Director) and Department of Computer Science has just had one of her recent projects, SWIF(r), mentioned in Nature, the multidisciplinary science journal. Originally published ("Localization of adaptive variants in human genomes using averaged one-dependence estimation") in Nature Communications, the research involves creating a machine learning algorithm to examine the genomes of a group of hunter-gatherers from southern Africa, then flag variations near genes associated with metabolism. Sohini and her collaborators speculate that the genetic changes they observed may allow their holders to store body fat at times when sufficient food isn't available.
"Using statistical methods for identifying adaptive mutations from population genetic data is challenging," Sohini says, "There are several obstacles: assessing the significance of genomic outliers, integrating correlated measures of selection into one analytic framework, and distinguishing adaptive variants from hitchhiking neutral variants. SWIF(r) is a probabilistic method that detects selective sweeps by learning the distributions of multiple selection statistics under different evolutionary scenarios and calculating the posterior probability of a sweep at each genomic site. It explicitly models the joint distributions of selection statistics, thereby increasing its power to both identify regions undergoing sweeps and localize adaptive mutations. And because it provides a transparent probabilistic framework for localizing beneficial mutations, it's also extensible to a variety of evolutionary scenarios."
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