Statistical Methods and Applications in Next-Generation Sequencing Data

• Activity Number: 476
• Type: Topic Contributed
• Date/Time: Wednesday, August 7, 2013 : 8:30 AM to 10:20 AM
• Sponsor: Biometrics Section
• Author(s): Yun Li*+ and Song Yan
• Companies: The University of North Carolina and The University of North Carolina
• Keywords: next generation sequencing ; rare variant association testing ; unbalanced design ; case control study

Abstract

A Powerful Method to Control Size of Unbalanced Sequencing Designs in Rare Variant Association Testing

Although next generation sequencing technologies have been transforming complex disease genetics, deep whole genome sequencing of large samples remains cost prohibitive. When studying binary outcome and under the reasonable assumption that the rare causal alleles are enriched among affected individuals, it is cost efficient to adopt unbalanced designs where more, if not solely, affected cases are sequenced than unaffected controls. However, such unbalanced designs lead to inflated type I error in aggregated rare variant association testing if not adjusted properly. A few methods have been proposed to correct the bias but tend to be overly conservative and underpowered. Here we propose a new adjustment method that controls type I error properly and is more powerful than existing approaches.