gbr_pcs

<p>A dataset containing 5 simulated Principal Components (PCs) for each of 91 subjects
from the 'GBR' population in the 1000 Genomes Projects.  These would normally be
used as covariates in a regression model to control for population stratification.</p>

datasets

Offers the Generalized Berk-Jones (GBJ) test for set-based inference in genetic
association studies. The GBJ is designed as an alternative to tests such as Berk-Jones (BJ),
Higher Criticism (HC), Generalized Higher Criticism (GHC), Minimum p-value (minP), and Sequence
Kernel Association Test (SKAT). All of these other methods (except for SKAT) are also implemented
in this package, and we additionally provide an omnibus test (OMNI) which integrates information from each of the tests.
The GBJ has been shown to outperform other tests in genetic association studies when signals
are correlated and moderately sparse. Please see the vignette for a quickstart guide or Sun and Lin
(2017) <arXiv:1710.02469> for more details.

Ryan Sun

Generalized Berk-Jones Test for Set-Based Inference in Genetic
Association Studies

gbr_pcs function

<p>A matrix with 91 rows (one for each subject) and 5 columns (one for each PC)</p>

gbr_pcs: Simulated Principal Components for 'GBR' population in the 1000 Genomes Project.

Description

Usage

Arguments

Format