lit

Identifying latent genetic interactions in genome-wide association studies using multiple traits

https://github.com/ajbass/lit

Repository

Identifying latent genetic interactions in genome-wide association studies using multiple traits

README.Rmd

---
output: github_document
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  message = FALSE,
  warning = FALSE,
  out.width = "100%"
)
```

# lit



[![R-CMD-check](https://github.com/ajbass/lit/actions/workflows/R-CMD-check.yml/badge.svg)](https://github.com/ajbass/lit/actions/workflows/R-CMD-check.yml) [![CRAN_Status_Badge](https://www.r-pkg.org/badges/version/lit)](https://cran.r-project.org/package=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:

```{r, eval = FALSE}
# release version
install.packages("lit")
```

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

```{r, eval = FALSE}
# install devtools
install.packages("devtools")
devtools::install_github("ajbass/lit")
```

The vignette can be viewed by typing:

``` r
browseVignettes(package = "lit")
```

If you run into issues with `gfortran` on Mac, see the answer [here](https://stackoverflow.com/questions/69639782/installing-gfortran-on-macbook-with-apple-m1-chip-for-use-in-r) 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:

```{r}
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)
```

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:

```{r}
# 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)
```

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:

```{r, eval = FALSE}
# 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)
```


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