RT Journal Article
SR Electronic
T1 Playing Musical Chairs in Big Data to Reveal Variables’ Associations
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 057190
DO 10.1101/057190
A1 Hugues Aschard
A1 Bjarni Vilhjalmsson
A1 Chirag Patel
A1 David Skurnik
A1 Jimmy Yu
A1 Brian Wolpin
A1 Peter Kraft
A1 Noah Zaitlen
YR 2016
UL http://biorxiv.org/content/early/2016/06/05/057190.abstract
AB 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.