title: Small sample properties of rare variant analysis methods.
abstract: We are now well into the sequencing era of genetic analysis, and methods to investigate rare variants associated with disease remain in high demand. Currently, the more common rare variant analysis methods are burden tests and variance component tests. This report introduces a burden test known as the modified replication based sum statistic and evaluates its performance, and the performance of other common burden and variance component tests under the setting of a small sample size (103 total cases and controls) using the Genetic Analysis Workshop 18 simulated data with complete knowledge of the simulation model. Specifically we look at the variable threshold sum statistic, replication-based sum statistics, the C-alpha, and sequence kernel association test. Using minor allele frequency thresholds of less than 0.05, we find that the modified replication based sum statistic is competitive with all methods and that using 103 individuals leads to all methods being vastly underpowered. Much larger sample sizes are needed to confidently find truly associated genes.
authors.affiliation: Division of Biostatistics, University of Texas School of Public Health, Houston, TX 77025, USA ; Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
authors.forename: Michael D
authors.initials: MD
authors.lastname: Swartz
authors.affiliation: Division of Biostatistics, University of Texas School of Public Health, Houston, TX 77025, USA.
authors.forename: Taebeom
authors.initials: T
authors.lastname: Kim
authors.affiliation: Department of Breast Medical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
authors.forename: Jiangong
authors.initials: J
authors.lastname: Niu
authors.affiliation: Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
authors.forename: Robert K
authors.initials: RK
authors.lastname: Yu
authors.affiliation: Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
authors.forename: Sanjay
authors.initials: S
authors.lastname: Shete
authors.affiliation: Department of Biostatistics, Columbia University, Mailman School of Public Health, New York, NY 10032, USA.
authors.forename: Iuliana
authors.initials: I
authors.lastname: Ionita-Laza