association.test.logisticR Documentation

Mixed logistic regression for GWAS

Description

Mixed logistic regression for GWAS

Usage

association.test.logistic(
  x,
  Y = x@ped$pheno,
  X = matrix(1, nrow(x)),
  K,
  beg = 1,
  end = ncol(x),
  algorithm = c("offset", "amle"),
  eigenK,
  p = 0,
  ...
)

Arguments

x

a bedmatrix

Y

phenotype vector. Default is column pheno of x@ped

X

A matrix of covariates (defaults to a column of ones for the intercept)

K

A genetic relationship matrix (or a list of such matrices)

beg

Index of the first SNP tested for association

end

Index of the last SNP tested for association

algorithm

Algorithm to use

eigenK

eigen decomposition of K (only if p > 0)

p

Number of principal components to include in the model

...

Additional parameter for gaston::logistic.mm.aireml

Details

Tests the association between the phenotype and requested SNPs in x. The phenotype Y is a binary trait. A Wald test is performed using an approximate method defined by the parameter algorithm. All other arguments are as in gaston::association.test.

Value

A data frame giving for each SNP the association statistics.

See Also

association.test

Examples

data(TTN)
x <- as.bed.matrix(TTN.gen, TTN.fam, TTN.bim)
## Simulation data ##
set.seed(1)
# some covariables
X <- cbind(1, runif(nrow(x)))
# A random GRM
ran <- random.pm( nrow(x))
# random effects (tau = 1)
omega <- lmm.simu(1, 0, eigenK=ran$eigen)$omega
# linear term of the model
lin <- X %*% c(0.1,-0.2) + omega
# vector of probabilitues
pi <- 1/(1+exp( -lin ))
# vector of binary phenotypes
y <- rbinom(nrow(x), 1, pi)
# testing association with 1) the score test, 2) the offset algorithm, 3) the 'amle' algorithm
a1 <- association.test(x, y, X, K = ran$K, method = "lmm", response = "bin")
a2 <- association.test.logistic(x, y, X, K = ran$K, algorithm = "offset")
a3 <- association.test.logistic(x, y, X, K = ran$K, algorithm = "amle")