lit
Latent Interaction Testing for Genome-Wide Studies
Identifying latent genetic interactions in genome-wide association studies using the Latent Interaction Testing (LIT) framework. LIT is a flexible kernel-based approach that leverages information across multiple traits to detect latent genetic interactions without specifying or observing the interacting variable (e.g., environment). LIT accepts standard PLINK files as inputs to analyze large genome-wide association studies.
lit
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:
wlituses a linear kernel to measure pairwise similarity for the genotype and trait matricesulituses a projection kernel to measure pairwise similarity for the genotype and trait matricesalitcombines the p-values ofwlitandulitusing 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)