Logistic regression analysis of pooled data for each SNP site in study

Fits a generalized linear model to gentic data for each SNP in the data sets considered, using user specified outcome and phenotypic variabes as covariates Outputs a matrix containing a beta value, standard error and p-value for each SNP

ds.glmSNP(
  snps.fit = NULL,
  model,
  genoData,
  datasources = NULL,
  type.p.adj = "fdr",
  mc.cores = 1,
  family = "binomial",
  strata = NULL
)

Arguments

snps.fit

an optional parameter input as a character vector of SNPs (rs numbers) that should be analysed. If missing all SNPs are analysed

model

list of phenotypic variables to use as covariates in the regression analysis in the form: "outcome ~ covar1 + covar2 + ... + covarN"

genoData

name of the DataSHIELD object to which the genotype (snpMatrix) and phenotypic data (data.frame) has been assigned

datasources

Opal object or list of opal objects denoting the opal server(s) information

mc.cores

optional parameter that allows the user to specify the number of CPU cores to use

strata

character Categorical variable to perform a stratified glm

Examples