sme: Sparse Marginal Epistasis Test (SME)

In smer: Sparse Marginal Epistasis Test

Sparse Marginal Epistasis Test (SME)

Description

SME fits a linear mixed model in order to test for marginal epistasis. It concentrates the scans for epistasis to regions of the genome that have known functional enrichment for a trait of interest.

Usage

sme(
  plink_file,
  pheno_file,
  mask_file = NULL,
  gxg_indices = NULL,
  chunk_size = NULL,
  n_randvecs = 10,
  n_blocks = 100,
  n_threads = 1,
  gxg_h5_group = "gxg",
  ld_h5_group = "ld",
  rand_seed = -1,
  log_level = "WARNING"
)

Arguments

plink_file	Character. File path to the PLINK dataset (without *.bed extension). The function will append .bim, .bed, and .fam extensions automatically. The genotype data must not have any missing genotypes. Use PLINK to remove variants with missing genotypes or impute them.
pheno_file	Character. File path to a phenotype file in PLINK format. The file should contain exactly one phenotype column.
mask_file	Character or NULL. File path to an HDF5 file specifying per-SNP masks for gene-by-gene interaction tests. This file informs which SNPs are tested for marginal epistasis. Defaults to NULL, indicating no masking. Masking impacts the scaling of memory and time.
gxg_indices	Integer vector or NULL. List of indices corresponding to SNPs to test for marginal epistasis. If NULL, all SNPs in the dataset will be tested. These indices are 1-based.
chunk_size	Integer or NULL. Number of SNPs processed per chunk. This influences memory usage and can be left NULL to automatically determine the chunk size based on gxg_indices and number of threads.
n_randvecs	Integer. Number of random vectors used for stochastic trace estimation. Higher values yield more accurate estimates but increase computational cost. Default is 10.
n_blocks	Integer. Number of blocks into which SNPs are divided for processing. This parameter affects memory requirements. Default is 100.
n_threads	Integer. Number of threads for OpenMP parallel processing. Default is 1.
gxg_h5_group	Character. Name of the HDF5 group within the mask file containing gene-by-gene interaction masks. SNPs in this group will be included in the gene-by-gene interactions. Defaults to "gxg".
ld_h5_group	Character. Name of the HDF5 group within the mask file containing linkage disequilibrium masks. SNPs in this group are excluded from analysis. Defaults to "ld".
rand_seed	Integer. Seed for random vector generation. If -1, no seed is set. Default is -1.
log_level	Character. Logging level for messages. Must be in uppercase (e.g., "DEBUG", "INFO", "WARNING", "ERROR"). Default is "WARNING".

Details

This function integrates PLINK-formatted genotype and phenotype data to perform marginal epistasis tests on a set of SNPs. Using stochastic trace estimation, the method computes variance components for gene-by-gene interaction and genetic relatedness using the MQS estimator. The process is parallelized using OpenMP when n_threads > 1.

The memory requirements and computation time scaling can be optimized through the parameters chunk_size, n_randvecs, and n_blocks.

Mask Format Requirements

The mask file format is an HDF5 file used for storing index data for the masking process. This format supports data retrieval by index. Below are the required groups and datasets within the HDF5 file:

The required group names can be configured as input parameters. The defaults are described below.

• Groups:

  • ld: Stores SNPs in LD with the focal SNP. These SNPs will be excluded.

  • gxg: Stores indices of SNPs that the marginal epistasis test is conditioned on. These SNPs will be included.

• Datasets:

  • ⁠ld/<j>⁠: For each focal SNP ⁠<j>⁠, this dataset contains indices of SNPs in the same LD block as that SNP. These SNPs will be excluded from the gene-by-gene interaction covariance matrix.

  • ⁠gxg/<j>⁠: For each focal SNP ⁠<j>⁠, this dataset contains indices of SNPs to include in the the gene-by-gene interaction covariance matrix for focal SNP ⁠<j>⁠.

Important: All indices in the mask file data are zero-based, matching the zero-based indices of the PLINK .bim file.

Value

A list containing:

• summary: A tibble summarizing results for each tested SNP, including:

  • id: Variant ID.

  • index: Index of the SNP in the dataset.

  • chromosome: Chromosome number.

  • position: Genomic position of the SNP.

  • p: P value for the gene-by-gene interaction test.

  • pve: Proportion of variance explained (PVE) by gene-by-gene interactions.

  • vc: Variance component estimate.

  • se: Standard error of the variance component.

• pve: A long-format tibble of PVE for all variance components.

• vc_estimate: A long-format tibble of variance component estimates.

• vc_se: A long-format tibble of standard errors for variance components.

• average_duration: Average computation time per SNP.

References

Stamp, J., Pattillo Smith, S., Weinreich, D., & Crawford, L. (2025). Sparse modeling of interactions enables fast detection of genome-wide epistasis in biobank-scale studies. bioRxiv, 2025.01.11.632557.

Stamp, J., DenAdel, A., Weinreich, D., & Crawford, L. (2023). Leveraging the genetic correlation between traits improves the detection of epistasis in genome-wide association studies. G3: Genes, Genomes, Genetics, 13(8), jkad118.

Crawford, L., Zeng, P., Mukherjee, S., & Zhou, X. (2017). Detecting epistasis with the marginal epistasis test in genetic mapping studies of quantitative traits. PLoS genetics, 13(7), e1006869.

Examples

plink_file <- gsub("\\.bed", "", system.file("testdata", "test.bed", package="smer"))
pheno_file <- system.file("testdata", "test_h2_0.5.pheno", package="smer")
mask_file <- ""

# Parameter inputs
chunk_size <- 10
n_randvecs <- 10
n_blocks <- 10
n_threads <- 1

# 1-based Indices of SNPs to be analyzed
n_snps <- 100
snp_indices <- 1:n_snps

sme_result <- sme(
  plink_file,
  pheno_file,
  mask_file,
  snp_indices,
  chunk_size,
  n_randvecs,
  n_blocks,
  n_threads
)
head(sme_result$summary)

smer documentation built on April 12, 2025, 9:14 a.m.