Unlocking Big Genetic Data Sets

The same algorithms that personalize movie recommendations and extract topics from oceans of text could bring doctors closer to diagnosing, treating and preventing disease on the basis of an individual's unique genetic profile. In a study to be published Monday, Nov. 7 in Nature Genetics, researchers at Columbia and Princeton universities describe a new machine-learning algorithm for scanning massive genetic data sets to infer an individual's ancestral makeup, which is key to identifying disease-carrying genetic mutations. On simulated data sets of 10,000 individuals, TeraStructure could estimate population structure more accurately and twice as fast as current state-of-the art algorithms, the study said. TeraStructure alone was capable of analyzing 1 million individuals, orders of magnitude beyond modern software capabilities, researchers said. The algorithm could potentially characterize the structure of world-scale human populations.