OINT: Omnibus-INT

In RNOmni: Rank Normal Transformation Omnibus Test

Omnibus-INT

Description

Association test that synthesizes the DINT and IINT tests. The first approach is most powerful for traits that could have arisen from a rank-preserving transformation of a latent normal trait. The second approach is most powerful for traits that are linear in covariates, yet have skewed or kurtotic residual distributions. During the omnibus test, the direct and indirect tests are separately applied, then the p-values are combined via the Cauchy combination method.

Usage

OINT(
  y,
  G,
  X = NULL,
  k = 0.375,
  ties.method = "average",
  weights = c(1, 1),
  simple = FALSE
)

Arguments

y	Numeric phenotype vector.
G	Genotype matrix with observations as rows, SNPs as columns.
X	Model matrix of covariates and structure adjustments. Should include an intercept. Omit to perform marginal tests of association.
k	Offset applied during rank-normalization. See RankNorm.
ties.method	Method of breaking ties, passed to base::rank.
weights	Respective weights to allocate the DINT and IINT tests.
simple	Return the OINT p-values only?

Value

A numeric matrix of p-values, three for each column of G.

See Also

• Basic association test BAT.

• Direct INT test DINT.

• Indirect INT test IINT.

Examples

set.seed(100)
# Design matrix
X <- cbind(1, rnorm(1e3))
# Genotypes
G <- replicate(1e3, rbinom(n = 1e3, size = 2, prob = 0.25))
storage.mode(G) <- "numeric"
# Phenotype
y <- exp(as.numeric(X %*% c(1, 1)) + rnorm(1e3))
# Omnibus
p <- OINT(y = y, G = G, X = X, simple = TRUE)

RNOmni documentation built on Sept. 11, 2023, 9:07 a.m.