Association Analysis Using Sequence Reads Without Calling Genotypes

• Activity Number: 451 - Current Trends in Statistical Genomics: Finding Needle in a Haystack?
• Type: Topic Contributed
• Date/Time: Wednesday, August 2, 2017 : 8:30 AM to 10:20 AM
• Sponsor: ENAR
• Author(s): Yijuan Hu* and Peizhou Liao and Henry Johnston and Andrew Allen and Glen Satten
• Companies: Emory University and Department of Biostatistics and Bioinformatics, Emory and Department of Biostatistics and Bioinformatics, Emory and Department of Biostatistics and Bioinformatics, Duke and Centers for Disease Control and Prevention
• Keywords: bootstrap ; differential genotyping error ; external controls ; next-generation sequencing ; read depth ; case-control studies

Abstract

Next-generation sequencing provides an unprecedented opportunity to discover rare genetic variants associated with complex diseases and traits. However, the common practice of first calling underlying genotypes and then treating the called values as known is prone to false positive findings, especially when cases and controls are sequenced at different depths. In this article, we provide a likelihood-based approach to testing rare variant associations that directly models sequencing reads without calling genotypes. We consider the burden test statistic. Because variant locations are unknown, we develop a simple, computationally efficient screening algorithm to estimate the loci that are variants. Because our burden statistic may not have mean zero after screening, we develop a novel bootstrap procedure for assessing the significance of the burden statistic. We demonstrate through extensive simulation studies that the proposed tests are robust to a wide range of differential sequencing qualities between cases and controls, and are at least as powerful as the standard genotype calling approach when the latter controls type I error. We also provide an application to the UK10K data.