OINT: Omnibus-INT In RNOmni: Rank Normal Transformation Omnibus Test

 OINT R Documentation

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`.

• 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.