A sophisticated computational algorithm, applied to a large set of gene markers, has achieved greater accuracy than conventional methods in assessing individual risk for type 1 diabetes. The researchers suggest that their technique, applied to appropriate complex multigenic diseases, improves the prospects for personalizing medicine to an individual’s genetic profile.