TY - JOUR T1 - Playing Musical Chairs in Big Data to Reveal Variables’ Associations JF - bioRxiv DO - 10.1101/057190 SP - 057190 AU - Hugues Aschard AU - Bjarni Vilhjalmsson AU - Chirag Patel AU - David Skurnik AU - Jimmy Yu AU - Brian Wolpin AU - Peter Kraft AU - Noah Zaitlen Y1 - 2016/01/01 UR - http://biorxiv.org/content/early/2016/06/05/057190.abstract N2 - Testing for associations in big data faces the problem of multiple comparisons, with true signals buried inside the noise of all associations queried. This is particularly true in genetic association studies where a substantial proportion of the variation of human phenotypes is driven by numerous genetic variants of small effect. The current strategy to improve power to identify these weak associations consists of applying standard marginal statistical approaches and increasing study sample sizes. While successful, this approach does not leverage the environmental and genetic factors shared between the multiple phenotypes collected in contemporary cohorts. Here we develop a method that improves the power of detecting associations when a large number of correlated variables have been measured on the same samples. Our analyses over real and simulated data provide direct support that large sets of correlated variables can be leveraged to achieve dramatic increases in statistical power equivalent to a two or even three folds increase in sample size. ER -