In EpiSlim/epiGWAS: Robust Methods for Epistasis Detection

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epiGWAS

This package implements a number of methods for detecting pure epistatic interactions with a predetermined target variant. The common denominator lies in the use of propensity scores to filter out the main effects of the rest of genotype. The methods incorporate propensity scores in two different ways: either in the sample weights (outcome weighted learning) or in the response (modified outcome).

Installation

You can install the released version of epiGWAS from CRAN with:

install.packages("epiGWAS")

The latest development version is directly available from GitHub:

install.packages("devtools")
devtools::install_github("EpiSlim/epiGWAS")

Usage Examples

The example below illustrates how to use our methods on a synthetic dataset:

require("epiGWAS")

# Genotype simulation
set.seed(542)
n_samples <- 300
p <- 450
# Genotypes matrix with {0, 1, 2} SNP values
genotypes <- matrix(
  (runif(n_samples * p, min = 0, max = 1) <
     runif(n_samples * p, min = 0, max = 1)) +
    (runif(n_samples * p, min = 0, max = 1) <
       runif(n_samples * p, min = 0, max = 1)),
  ncol = p, nrow = n_samples, dimnames = list(NULL, paste0("SNP_", seq_len(p)))
)

# Phenotype simulation
target <- "SNP_56"
syner <- paste0("SNP_", sample.int(p, 10))
size_effects <- rnorm(10) 
binarized <- genotypes[, target] > 1
risk <-   (2 * binarized - 1) * (genotypes[, syner] %*% size_effects)
risk <- risk - mean(risk) # Centering to balance cases and controls
phenotype <- runif(n_samples) < 1/(1+exp(-risk)) # Logistic model

The propensity scores can be estimated using the fastPHASE hidden Markov model. Make sure to download the fastPHASE executable before running the fast_HMM function.

hmm <- fast_HMM(genotypes, fp_path = "~/Downloads/fastPHASE3",
  n_state = 4, n_iter = 10)
propensity <- cond_prob(genotypes, target, hmm, binary = FALSE)
propensity <- propensity[cbind(seq(dim(genotypes)[1]), binarized + 1)]

hmm <- fast_HMM(genotypes, fp_path = "/path/to/fastPHASE",
  n_state = 4, n_iter = 10)
propensity <- cond_prob(genotypes, target, hmm, binary = FALSE)
propensity <- propensity[cbind(seq(dim(genotypes)[1]), binarized + 1)]

All the pieces are now in place to apply our epistasis detection methods via the epiGWAS function.

stability_scores <- epiGWAS(binarized, genotypes[, colnames(genotypes) != target], phenotype,
                            propensity, methods = c("OWL", "modified_outcome", "shifted_outcome",
                                        "normalized_outcome", "robust_outcome"), parallel = FALSE)

References

Slim, L., Chatelain, C., Azencott, C.-A., & Vert, J.-P. (2018). Novel Methods for Epistasis Detection in Genome-Wide Association Studies. BioRxiv. Retrieved from http://biorxiv.org/content/early/2018/10/14/442749

EpiSlim/epiGWAS documentation built on Nov. 19, 2019, 7:15 p.m.