ReAD: Replicability analysis across two genome-wide association...

In ReAD: Powerful Replicability Analysis of Genome-Wide Association Studies

Replicability analysis across two genome-wide association studies accounting for the linkage disequilibrium structure.

Description

Replicability analysis across two genome-wide association studies accounting for the linkage disequilibrium structure.

Usage

ReAD(pa, pb)

Arguments

pa	A numeric vector of p-values from study 1.
pb	A numeric vector of p-values from study 2.

Value

A list:

rLIS	The estimated rLIS for replicability null.
fdr	The adjusted values based on rLIS for FDR control.
loglik	The log-likelihood value with converged estimates of the unknowns.
pi	An estimate of the stationary probabilities of four states (0,0), (0,1), (1,0), (1,1).
A	An estimate of the 4-by-4 transition matrix.
f1	A non-parametric estimate for the non-null probability density function in study 1.
f2	A non-parametric estimate for the non-null probability density function in study 2.

Examples

# Simulate p-values in two studies locally dependent via a four-state hidden Markov model
data <- SimuData(J = 10000)
p1 = data$pa; p2 = data$pb; theta1 = data$theta1; theta2 = data$theta2
# Run ReAD to identify replicable signals
res.read = ReAD(p1, p2)
sig.idx = which(res.read$fdr <= 0.05)

ReAD documentation built on July 9, 2023, 6:38 p.m.