test.trioGxE(object, data = NULL, nreps, level = 0.05, early.stop = FALSE,
fix.sp = FALSE, output = NULL, return.data = FALSE,
return.object = FALSE, ...)trioGxE function.
When NULL, a data set of case-parent trios must be provided
(through `data' argument).trioGxE when `object' is not provided.TRUE, the approximated null distribution of the test statistic is obtained by
computing the test statistic by fitting each simulated data set under
fixed values of the smoothing parameters.
When FALSE (Default), the null TRUE, sampling is terminated early when the number of test statistics
that are more extreme than or as extreme as the observed test statistic equals
nreps*level.
$\code{nreps} \times \code{level}$ values larger
than the NULL (Default), no written output file is produced.TRUE, the original data set is returned.TRUE, the fitting object for the original data set is returned.object$penmod = "codominant"),
a 3-column matrix is returned, where the first column holds the values for $T$
for the original and the generated data sets,
and the second and third columns hold the values of
$T_1$ and $T_2$, respectively for the same data sets.
When the actual data was fitted under a non-co-dominant penetrance mode (e.g., dominant),
GxE.test is retuned as a matrix with a sigle column holding $T$.object$penmod = "codominant",
it is returned as a vector holding three values,
where the first value indicates the overall p-value obtained from the distribution of $T$,
and the other two values indicate the individual p-values obtained from the
distributions of $T_1$ and $T_2$.
Under object$penmod is dominant, additive or recessive,
it is returned as a single p-value calculated based on $T$.The function test.trioGxE calculates test statistic $T$,$$T = t(\hat{\bm{c}}){\rm V}^{-1}(\bm{c})\hat{\bm{c}},$$
where $\bm{c}=(\bm{c}_1^{\prime},\bm{c}_2^{\prime})^{\prime}$
and $V_c$ is a square matrix of size $(k_1+k_2-2)$,
formed by extracting the rows and columns, corresponding to the spline
coefficients from the Bayesian posterior variance-covariance matrix
calculated in trioGxE.
If the actual data were fitted under the co-dominant penetrance mode
(i.e., object$penmod="codominant"),
the test statistic $T$ represents an overall test of GxE, where
$${\rm H}_0: \bm{c}=0.$$
Depending on the context, an investigator may also want to perform individual tests:
${\rm H}_{01}: \bm{c}_1 = \bm{0}$ and ${\rm H}_{02}: \bm{c}_2 = \bm{0}$.
For example, when the null hypothesis is rejected, the user may want to know which of
the two interaction function is not zero (i.e., which curve is not flat).
For the individual tests, test.trioGxE calculates the
permutation p-values based on the Monte-Carlo distributions of the individual
test statistics $T_1$ and $T_2$, where
$$T_h = t(\hat{\bm{c}}_h){\rm V}^{-1}(\bm{c}_h)\hat{\bm{c}}_h, h=1,2.$$
Under the dominant, log-additive (multiplicative) or recessive penetrance model, $T$ can be viewed as an individual test since $\bm{c}_2=\bm{0}$, $\bm{c}_1=\bm{c}_2$ and $\bm{c}_1=\bm{0}$, respectively, under the dominant, log-additive and recessive models. For example, under the dominant penetrance model, $T\equiv{T_1}$ because $\bm{c}_2=\bm{0}$, and $T_2=0$.
As the analysis is conditional on parental genotypes, the distribution of the test statistic under ${\rm H}_0$ is calculated by shuffling the column that holds the values of the non-genetic covariate within mating types. This can be justifiable based on the fact that under no interaction, the SNP and the non-genetic covariate are independent of each other within a random affected trio when they are independent within a trio from the general population (Umbach and Weinberg, 2000).
The distribution of the test statistics can be obtained in two ways:
either under fixed smoothing parameters (fixed.sp=TRUE)
or under varying smoothing parameters (fixed.sp=FALSE).
Under the fixed smoothing parameters, the penalized iteratively re-weighted least squares
procedure is performed for each simulated data set under the same smoothing parameter values.
Under varying smoothing parameters, smoothing parameters are estimated for each
simulated data set.
Therefore, the test under fixed.sp=FALSE accounts for the extra uncertainty introduced by
the smoothing parameter estimation.
To save computation time, the user can use `early-termination' option (Besag and Clifford, 1991).
Under this option, sampling is terminated when the number of the simulated data sets reaches
nreps*{level} $<$ level).
For example, if the user specifies nreps=1000 and level=0.05, the test terminates when the number of
data sets that have test statistic values that are more extreme than or as extreme as the observed test statistic value reaches 50.
trioGxE, plot.trioGxE, trioSimdata(hypoTrioDat)
example.fit <- trioGxE(hypoTrioDat, pgenos = c("parent1","parent2"), cgeno = "child",
cenv = "attr",penmod="codominant", k=c(5,5))
# A toy example with 'few' permutation replicates
example.test <- test.trioGxE(example.fit, nreps=10, early.stop = FALSE,
output=NULL)
## More proper examples of permutation tests with 5000 replicates
## Example1: does not generate an output file containing test statistic values
example.test1 <- test.trioGxE(example.fit, nreps=5000, early.stop = TRUE,
output=NULL)
## Example 2: generates an output file 'myoutput.out' containing test statistic values
example.test2 <- test.trioGxE(example.fit, nreps=5000, early.stop = TRUE,
output="myoutput.out")Run the code above in your browser using DataLab