GsSKAT: Rapid gene set analysis and multiple testing correction for rare-variant association studies using weighted linear kernels.

ABSTRACT: Next-generation sequencing technologies have afforded unprecedented characterization of low-frequency and rare genetic variation. Due to low power for single-variant testing, aggregative methods are commonly used to combine observed rare variation within a single gene. Causal variation may also aggregate across multiple genes within relevant biomolecular pathways. Kernel-machine regression and adaptive testing methods for aggregative rare-variant association testing have been demonstrated to be powerful approaches for pathway-level analysis, although these methods tend to be computationally intensive at high-variant dimensionality and require access to complete data. An additional analytical issue in scans of large pathway definition sets is multiple testing correction. Gene set definition

SUBMITTER: Larson NB 

PROVIDER: S-EPMC5397327 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature