multipleSNP: Association analysis with multiple SNPs
In zhanghfd/CCMO: Case-Control Mother-Offspring Pair Genetic Association Studies

Description Usage Arguments Value See Also Examples

View source: R/CCMO.R

Multiple SNPs can be analyzed utilizing multiple CPU cores (requires the R package doMC), which calls singleSNP. Estimation results include the parameter estimates and their estimated standard errors, the p-values for significance tests (need to call OmnibusTest), some model information, and omnibus test if required (the argument test should be specified).

multipleSNP(
  Y,
  Gc,
  Gm,
  Xo = NULL,
  Xc = NULL,
  Xm = NULL,
  X.Gm = NULL,
  G.main = c("Gc", "Gm"),
  G.int = FALSE,
  mode = "add",
  prev = 0.01,
  ind = FALSE,
  HWE = TRUE,
  normalized.genotype = FALSE,
  test = NULL,
  cores = NULL
)

`Y`	a `n`-vector of disease statuses for n offspring (1 for case and 0 for control).
`Gc`	a `n` x `m` matrix of genotypes for offspring (n: number of offspring; m: number of SNPs). The possible values should be 0, 1, 2, or 1/2 (see `normalized.genotype` for details).
`Gm`	a `n` x `m` matrix of genotypes for mothers (n: number of mothers; m: number of SNPs). The possible values should be 0, 1, 2, or 1/2 (see `normalized.genotype` for details).
`Xo`	a `n` x `n_o` matrix of maternal covariates for main effects (n: number of mothers; `n_o`: the numbers of genotypes; default value: NULL)
`Xc`	a `n` x `n_c` matrix of maternal covariates for intercation effects with offspring genotypes (`n`: number of mothers; `n_c`: the numbers of genotypes; default value: NULL)
`Xm`	a `n` x `n_m` matrix of maternal covariates for intercation effects with maternal genotypes (`n`: number of mothers; `n_m`: the numbers of genotypes; default value: NULL)
`X.Gm`	a `n` x `n_g` matrix of maternal covariates potentially associated with maternal genotypes (`n`: number of mothers; `n_g`: the numbers of genotypes; default value: NULL)
`G.main`	a vector containing the main SNP effects taking values 'Gc' and/or 'Gm' (default value: c('Gc','Gm'))
`G.int`	an indicator for the presence of Gc x Gm interaction (TRUE for yes and FALSE for no; default value: FALSE)
`mode`	mode of inheritance ('rec' for recessive, 'add' for additive, 'dom' for dominant; default value: 'add')
`prev`	specified disease prevalence (default value: 0.01)
`ind`	a logical variable indicating whether Gm and X.Gm are independent (TRUE for independence and FALSE for dependence, default value: FALSE)
`HWE`	a logical variable indicating whether the HWE assumption is incorporated (TRUE for incorprated and FALSE otherwise; default value: TRUE)
`normalized.genotype`	a logical variable indicating whether the genotypes are normalized (TRUE for normalized and FALSE otherwise, default value: FALSE). If FALSE, Gc and Gm should take values 0 (genotype `AA`), 1 (genotype `AB`), 2 (genotype `BB`); otherwise, their values depend on the mode of inheritance ('add': 0 for genotype `AA`, 0.5 for genotype `AB`, 1 for genotype `BB`; 'rec': 0 for genotype `AA` or AB, 1 for genotype BB; 'dom': 0 for genotype `AA`, 1 for genotype AB or BB).
`test`	a vector of predictor indices in Omnibus test (default value: NULL; if not NULL, call OmnibusTest)
`cores`	the number of CPU cores used for parallele execution. If not specified, the system will determine its value.

a list of length m, each element is a list with the following elements

`new`	estimation and significance test results for the new method
`log`	estimation and significance test results for the standard logistic regression method
`cov.new`	covariance matrix of the estimated parameters by the new method
`cov.log`	covariance matrix of the estimated parameters by the standard logistic regression method)
`penetrance`	logistic regression model for the penetrance function
`daLOG`	daLOG model relating maternal genotype and maternal covariates
`Omnibus`	Omnibus test results (test statistic, degrees of freedom, p-value) if `test` is not NULL

singleSNP OmnibusTest

## Not run: 
library(foreach)
data(SampleData)
Y = SampleData[,1]
M = 10 # the number of SNPs
Gc = SampleData[,1+1:M]
Gm = SampleData[,1+M+(1:M)]
X = SampleData[,-(1:(1+2*M))]
res = multipleSNP(Y,Gc,Gm,Xo=X,Xc=X,Xm=X,X.Gm=X)

## End(Not run)