knockoffGenotypes {SNPknock}

Group-knockoffs of unphased genotypes

Description

This function efficiently constructs group-knockoffs of 0,1,2 variables distributed according to the Li and Stephens model for unphased genotypes.

Usage

knockoffGenotypes(X, r, alpha, theta, groups = NULL, seed = 123,
  cluster = NULL, display_progress = FALSE)

Arguments

X

a 0,1,2 matrix of size n-by-p containing the original variables.

r

a vector of length p containing the "r" parameters estimated by fastPHASE.

alpha

a matrix of size p-by-K containing the "alpha" parameters estimated by fastPHASE.

theta

a matrix of size p-by-K containing the "theta" parameters estimated by fastPHASE.

groups

a vector of length p containing group memberships for each variable. Indices are assumed to be monotone increasing, starting from 1 (default: NULL).

seed

an integer random seed (default: 123).

cluster

a computing cluster object created by makeCluster (default: NULL).

display_progress

whether to show progress bar (default: FALSE).

Details

Generate group-knockoffs of unphased genotypes according to the Li and Stephens HMM. The required model parameters can be obtained through fastPHASE and loaded with loadHMM. This function is more efficient than knockoffHMM for haplotype data.

Value

A 0,1,2 matrix of size n-by-p containing the knockoff variables.

References

Sesia M, Katsevich E, Bates S, Candès E, Sabatti C (2019). “Multi-resolution localization of causal variants across the genome.” bioRxiv. doi: 10.1101/631390.

See Also

Other knockoffs: knockoffDMC, knockoffHMM, knockoffHaplotypes

Examples

# Problem size
p = 10
n = 100
# Load HMM to generate data
r_file = system.file("extdata", "haplotypes_rhat.txt", package = "SNPknock")
alpha_file = system.file("extdata", "haplotypes_alphahat.txt", package = "SNPknock")
theta_file = system.file("extdata", "haplotypes_thetahat.txt", package = "SNPknock")
char_file = system.file("extdata", "haplotypes_origchars", package = "SNPknock")
hmm.data = loadHMM(r_file, alpha_file, theta_file, char_file, compact=FALSE, phased=FALSE)
hmm.data$Q = hmm.data$Q[1:(p-1),,]
hmm.data$pEmit = hmm.data$pEmit[1:p,,]
# Sample X from this HMM
X = sampleHMM(hmm.data$pInit, hmm.data$Q, hmm.data$pEmit, n=n)
# Load HMM to generate knockoffs
hmm = loadHMM(r_file, alpha_file, theta_file, char_file)
hmm$r = hmm$r[1:p]
hmm$alpha = hmm$alpha[1:p,]
hmm$theta = hmm$theta[1:p,]
# Generate knockoffs
Xk = knockoffGenotypes(X, hmm$r, hmm$alpha, hmm$theta)
# Generate group-knockoffs for groups of size 3
groups = rep(seq(p), each=3, length.out=p)
Xk = knockoffGenotypes(X, hmm$r, hmm$alpha, hmm$theta, groups=groups)