Overview

The lit package implements a kernel-based multivariate testing procedure, called Latent Interaction Testing (LIT), to test for latent genetic interactions in genome-wide association studies. See our manuscript for additional details:

Bass AJ, Bian S, Wingo AP, Wingo TS, Culter DJ, Epstein MP. Identifying latent genetic interactions in genome-wide association studies using multiple traits. Genome Medicine; 2024.

Installation

This software is implemented in the R statistical programming language. To install the release version, type the following in the R terminal:

# release version
install.packages("lit")

The development version of lit can be installed using the following code:

# install devtools
install.packages("devtools")
devtools::install_github("ajbass/lit")

The vignette can be viewed by typing:

browseVignettes(package = "lit")

If you run into issues with gfortran on Mac, see the answer here for additional details.

Quick start

We provide two ways to use the lit package. When the genotypes can be loaded in R (small GWAS datasets), the lit() function can be used:

library(lit)
# set seed
set.seed(123)
# generate 10 SNPs for 10 individuals
X <- matrix(rbinom(10 * 10, size = 2, prob = 0.25), ncol = 10)
# generate 4 phenotypes for 10 individuals
Y <- matrix(rnorm(10 * 4), ncol = 4)
# test for latent genetic interactions
out <- lit(Y, X)
head(out)
#>        wlit      ulit      alit
#> 1 0.2681410 0.3504852 0.3056363
#> 2 0.7773637 0.3504852 0.6044655
#> 3 0.4034423 0.3504852 0.3760632
#> 4 0.7874949 0.3504852 0.6157108
#> 5 0.8701189 0.3504852 0.7337565
#> 6 0.2352616 0.3504852 0.2847600

The output is a data frame of p-values where the rows are SNPs and the columns are different implementations of LIT to test for latent genetic interactions:

wlit uses a linear kernel to measure pairwise similarity for the genotype and trait matrices
ulit uses a projection kernel to measure pairwise similarity for the genotype and trait matrices
alit combines the p-values of wlit and ulit using a Cauchy combination test to maximize the number of discoveries

For large GWAS datasets (e.g., biobank-sized), the lit() function is not computationally feasible because the genotypes cannot be loaded in R. Instead, the lit_plink() function can be applied directly to plink files. To demonstrate how to use the function, we use the example plink files from the genio package:

# load genio package
library(genio)
# path to plink files
file <- system.file("extdata", 'sample.bed', package = "genio", mustWork = TRUE)
# generate trait expression
Y <- matrix(rnorm(10 * 4), ncol = 4)
# apply lit to plink file
out <- lit_plink(Y, file = file, verbose = FALSE)
head(out)
#>   chr         id     pos alt ref       maf      wlit      ulit      alit
#> 1   1  rs3094315  752566   G   A 0.3888889 0.7908763 0.3422960 0.6150572
#> 2   1  rs7419119  842013   T   G 0.3888889 0.1552580 0.3422960 0.2194972
#> 3   1 rs13302957  891021   G   A 0.2500000 0.4088937 0.3325939 0.3687589
#> 4   1  rs6696609  903426   C   T 0.3125000 0.5857829 0.3325939 0.4519475
#> 5   1     rs8997  949654   A   G 0.4375000 0.6628300 0.3325939 0.4969663
#> 6   1  rs9442372 1018704   A   G 0.2500000 0.3192430 0.3325939 0.3258332

See ?lit and ?lit_plink for additional details and input arguments.

Note that a marginal testing procedure for latent genetic interactions based on the squared residuals and cross products (Marginal (SQ/CP)) can also be implemented using the marginal and marginal_plink functions:

# apply Marginal (SQ/CP) to loaded genotypes
out <- marginal(Y, X)
# apply Marginal (SQ/CP) to plink file
out <- marginal_plink(Y, file = file, verbose = FALSE)