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Activity Number: 85 - SPEED: An Ensemble of Advances in Genomics and Genetics
Type: Contributed
Date/Time: Sunday, July 29, 2018 : 5:05 PM to 5:50 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #332824
Title: Optimal Covariate Weighting Increases Discoveries in High-Throughput Biology
Author(s): Paul Schliekelman* and Mohamad Hasan
Companies: University of Georgia and University of Georgia
Keywords: high throughput; multiple testing; p-value weighting; covariate

The large scale multiple testing inherent to high throughput biological data necessitates very high statistical stringency and thus true effects in data are difficult to detect unless they have high effect size. Weighted p-values provide a rigorous framework for using independent information to prioritize the most promising features of the data and thus increase the power to detect them. However, calculating weights that incorporate the independent information and optimize power is challenging. We introduce Covariate Rank Weighting, a method for calculating approximate optimal weights conditioned on the ranking of tests by an external covariate. This approach uses the probabilistic relationship between covariate ranking and test effect size to calculate more informative weights that are not diluted by null effects as is common with existing methods. This relationship can be calculated theoretically for normally distributed covariates or estimated empirically in other cases. We show via simulations and applications to data that this method outperforms existing methods by a large margin in the rare/low effect size scenario and has at least comparable performance in all scenarios.

Authors who are presenting talks have a * after their name.

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