Example 2: Using SEAGLE with Simulated Data

In SEAGLE: Scalable Exact Algorithm for Large-Scale Set-Based Gene-Environment Interaction Tests

This tutorial demonstrates how to use the SEAGLE package when the user inputs a matrix ${\bf G}$. We'll begin by loading the SEAGLE package.

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(SEAGLE)

As an example, we'll generate some synthetic data for usage in this tutorial. Let's consider a dataset with $n=5000$ individuals and $L=100$ loci, where the first $40$ are causal.

The makeSimData function generates a covariate matrix $\widetilde{\bf X} \in \mathbb{R}^{n \times 3}$, where the first column is the all ones vector for the intercept and the second and third columns are ${\bf X} \sim \text{N}(0,1)$ and ${\bf E} \sim \text{N}(0,1)$, respectively. The last two columns are scaled to have $0$ mean and unit variance.

The makeSimData function additionally generates the genetic marker matrix ${\bf G}$ with synthetic haplotype data from the COSI software. Detailed procedures for generating ${\bf G}$ can be found in the accompanying journal manuscript. Finally, the makeSimData function also generates a continuous phenotype ${\bf y}$ according to the following fixed effects model $$ {\bf y} = \tilde{\bf X} \boldsymbol{\gamma}{\widetilde{\bf X}} + {\bf G}\boldsymbol{\gamma}{G} + \text{diag}(E){\bf G}\boldsymbol{\gamma}{GE} + {\bf e}. $$ Here, $\boldsymbol{\gamma}{\tilde{\bf X}}$ is the all ones vector of length $P=3$, $\boldsymbol{\gamma}{G} \in \mathbb{R}^{L}$, $\boldsymbol{\gamma}{GE}\in \mathbb{R}^{L}$, and ${\bf e} \sim \text{N}({\bf 0}, \sigma\, {\bf I}{n})$. The entries of $\boldsymbol{\gamma}{G}$ and $\boldsymbol{\gamma}{GE}$ pertaining to causal loci are set to be $\gamma{G}$ = gammaG and $\gamma_{GE}$ = gammaGE, respectively. The remaining entries of $\boldsymbol{\gamma}{G}$ and $\boldsymbol{\gamma}{GE}$ pertaining to non-causal loci are set to $0$.

dat <- makeSimData(H=cosihap, n=5000, L=100, gammaG=1, gammaGE=0, causal=40, seed=1)

Now that we have our data, we can prepare it for use in the SEAGLE algorithm. We will input our ${\bf y}$, ${\bf X}$, ${\bf E}$, and ${\bf G}$ into the prep.SEAGLE function. The intercept = 1 parameter indicates that the first column of ${\bf X}$ is the all ones vector for the intercept.

This preparation procedure formats the input data for the SEAGLE function by checking the dimensions of the input data. It also pre-computes a QR decomposition for $\widetilde{\bf X} = \begin{pmatrix} {\bf 1}{n} & {\bf X} & {\bf E} \end{pmatrix}$, where ${\bf 1}{n}$ denotes the all ones vector of length $n$.

objSEAGLE <- prep.SEAGLE(y=dat$y, X=dat$X, intercept=1, E=dat$E, G=dat$G)

Finally, we'll input the prepared data into the SEAGLE function to compute the score-like test statistic $T$ and its corresponding p-value. The init.tau and init.sigma parameters are the initial values for $\tau$ and $\sigma$ employed in the REML EM algorithm.

res <- SEAGLE(objSEAGLE, init.tau=0.5, init.sigma=0.5)
res$T
res$pv

The score-like test statistic $T$ for the G$\times$E effect and its corresponding p-value can be found in res$T and res$pv, respectively.

SEAGLE documentation built on Nov. 6, 2021, 1:06 a.m.