sigmod: Find connected explanatory SNPs

In hclimente/martini: GWAS Incorporating Networks

Find connected explanatory SNPs

Description

Finds the SNPs maximally associated with a phenotype while being connected in an underlying network.

Usage

sigmod(
  gwas,
  net,
  eta,
  lambda,
  covars = data.frame(),
  score = c("chi2", "glm", "r2"),
  family = c("binomial", "poisson", "gaussian", "gamma"),
  link = c("logit", "log", "identity", "inverse")
)

Arguments

gwas	A SnpMatrix object with the GWAS information.
net	An igraph network that connects the SNPs.
eta	Value of the eta parameter.
lambda	Value of the lambda parameter.
covars	A data frame with the covariates. It must contain a column 'sample' containing the sample IDs, and an additional columns for each covariate.
score	Association score to measure association between genotype and phenotype. Possible values: chi2 (default), glm.
family	A string defining the generalized linear model family. This should match one of "binomial", "poisson", "gaussian" or "gamma". See snp.rhs.tests for details.
link	A string defining the link function for the GLM. This should match one of "logit", "log", "identity" or "inverse". See snp.rhs.tests for details.

Value

A copy of the SnpMatrix$map data.frame, with the following additions:

• c: contains the univariate association score for every single SNP.

• selected: logical vector indicating if the SNP was selected by SConES or not.

• module: integer with the number of the module the SNP belongs to.

References

Liu, Y., Brossard, M., Roqueiro, D., Margaritte-Jeannin, P., Sarnowski, C., Bouzigon, E., Demenais, F. (2017). SigMod: an exact and efficient method to identify a strongly interconnected disease-associated module in a gene network. Bioinformatics, 33(10), 1536–1544. https://doi.org/10.1093/bioinformatics/btx004

Examples

gi <- get_GI_network(minigwas, snpMapping = minisnpMapping, ppi = minippi)
sigmod(minigwas, gi, 10, 1)

hclimente/martini documentation built on Feb. 26, 2024, 6:23 p.m.