estimate.vc

Matrix of covariates (first column contains the intercept, last column contains the E factor for studying the GxE effect)

Xtilde

Object containing QR decomposition of Xtilde

qrXtilde

Coefficient vector for covariate matrix Xtilde

beta

Initial sigma input (Default is 0.5)

init.sigma

Initial tau input (Default is 0.5)

init.tau

Tolerance for convergence (Default is 1e-3)

Maximum number of iterations (Default is 1000)

maxiters

REML EM algorithm for estimating variance components

The explosion of biobank data offers immediate opportunities for
gene-environment (GxE) interaction studies of complex diseases because of the
large sample sizes and rich collection in genetic and non-genetic information.
However, the extremely large sample size also introduces new computational
challenges in GxE assessment, especially for set-based GxE variance component (VC)
tests, a widely used strategy to boost overall GxE signals and to evaluate the
joint GxE effect of multiple variants from a biologically meaningful unit
(e.g., gene).
We present 'SEAGLE', a Scalable Exact AlGorithm for Large-scale Set-based
GxE tests, to permit GxE VC test scalable to biobank data. 'SEAGLE' employs modern
matrix computations to achieve the same <e2><80><9c>exact<e2><80><9d> results as the original GxE VC
tests, and does not impose additional assumptions nor relies on approximations.
'SEAGLE' can easily accommodate sample sizes in the order of 10^5, is implementable
on standard laptops, and does not require specialized equipment.
The accompanying manuscript for this package can be found at Chi, Ipsen, Hsiao,
Lin, Wang, Lee, Lu, and Tzeng. (2021+) <arXiv:2105.03228>.

estimate.vc: REML EM Algorithm

Description

Usage

Arguments

Value