RBAT: Adaptive variant-set association test based on FE and RHE...

In baolinwu/MetaSAT: Meta-analysis of variant-set association tests

Description

Conduct meta-analysis of variant-set association test of m variants assuming similar effects across variants. RHE BT: ∑_k(η_k^TU_k)^2; FE BT: (∑_kη_k^TU_k)^2; BAT: adaptively combine RHE BT and FE BT.

Usage

RBAT(Us, Vs, eta = NULL, rho = (0:10/10)^2)

Arguments

Us	matrix of variant test statistics (m by K)
Vs	array of covariance matrix for test statistics (m,m by K)
eta	coefficient matrix for variants (m by K). Default to equal weights.
rho	weights assigned to the FE BT

Value

p.value

p-values for BAT, RHE BT, FE BT

pval

vector of p-values for each rho

rho.est

optimal rho value leading to the minimum p-value

References

Wu,B. and Zhao,H. (2018) Efficient and powerful meta-analysis of variant-set association tests using MetaSAT.

Examples

K = 3; m=10
Vs = array(0, dim=c(m,m,K)); Us = matrix(0, m,K)
for(k in 1:K){
  ak = matrix(rnorm(100*m),100,m)*sqrt(0.8)+rnorm(100)*sqrt(0.2)
  Vs[,,k] = cor(ak)
  Rh = chol(Vs[,,k])
  Us[,k] = colSums(Rh*rnorm(m))
}
RBAT(Us,Vs)
U1 = Us + rnorm(m*K,1,1.25)
RBAT(U1,Vs)

baolinwu/MetaSAT documentation built on May 13, 2019, 5:22 p.m.