Between-Block Haplotype Allele Epistasis Scan

Tests interactions between haplotype allele dosage columns from different LD blocks. For each significant allele (from test_block_haplotypes), tests its interaction with every haplotype allele at all other blocks:

$$y = \mu + \alpha_i x_i + \alpha_j x_j + \gamma_{ij}(x_i x_j) + \varepsilon$$

Restricting to significant alleles x all blocks gives O(n_sig x n_total_alleles) tests – tractable at WGS scale. For your panel: ~25 significant alleles x 17,943 alleles = ~450,000 tests.

This is a trans-haplotype epistasis scan: it tests whether the effect of a resistance haplotype at one block depends on the haplotype background at another block – a form of genetic background dependence that single-SNP and even single-block analyses cannot detect.

Usage

scan_block_by_block_epistasis(
  assoc,
  haplotypes,
  blues,
  blocks,
  trait = NULL,
  sig_alleles = NULL,
  min_freq = 0.05,
  top_n = NULL,
  sig_threshold = 0.05,
  sig_metric = c("p_simplem_sidak", "p_simplem", "p_bonf", "p_fdr"),
  meff_percent_cut = 0.995,
  id_col = "id",
  blue_col = "blue",
  verbose = TRUE
)

Arguments

assoc: Output of test_block_haplotypes.
haplotypes: Named list from extract_haplotypes.
blues: Pre-adjusted phenotype means.
blocks: LD block table.
trait: Character. Trait to use. Default NULL uses first trait.
sig_alleles: Data frame with columns block_id and allele identifying the query alleles. NULL (default) uses all significant = TRUE rows from assoc$allele_tests.
min_freq: Numeric. Minimum allele frequency. Default 0.05.
top_n: Integer or NULL. Alleles per block in feature matrix.
sig_threshold: Numeric. Significance threshold applied to the p-value chosen by sig_metric. Default 0.05.
sig_metric: Character. Which p-value drives the significant flag. One of "p_simplem_sidak" (default, recommended), "p_simplem", "p_bonf", or "p_fdr". All four p-value columns are always present in the output regardless of this choice.
meff_percent_cut: Numeric in (0, 1). Variance threshold for simpleM eigendecomposition when estimating $M_{\mathrm{eff}}$. Default 0.995 (99.5%), following Gao et al. (2008).
id_col: Character. ID column when blues is a data frame.
blue_col: Character. Phenotype column when blues is a data frame.
verbose: Logical. Default TRUE.

Value

A named list of class LDxBlocks_block_epistasis:

results: Data frame sorted by p_wald. Columns: block_i, allele_i, block_j, allele_j, CHR_i, CHR_j, same_chr, aa_effect, SE, t_stat, p_wald, p_bonf, significant.
n_tests: Integer. Total interaction tests performed.
n_sig_alleles: Integer. Number of query alleles.

References

Mackay TFC (2014). Epistasis and quantitative traits: using model organisms to study gene-gene interactions. Nature Reviews Genetics 15:22-33.

Usage

Arguments

Value

References

See also