single.snp.test: Regression analysis with single SNP genotypes as independent...
In HapEstXXR: Multi-Locus Stepwise Regression

Description Usage Arguments Details Value Author(s) References See Also Examples

This function fits a generalized linear model with quantative, dichotomous or survival trait as dependent variable and one or more potential covariates. In case of family data the weighted TDT statistic is used.

single.snp.test(snps, trait, adj.var = NULL, 
           type = c("gaussian", "binomial", "families", "casecohort"), 
           famid, patid, fid, mid, 
           start.time, stop.time, subcohort, stratvar = NA, robust = FALSE, 
           marker.label = NA, 
           prt = TRUE, ties = "efron")

`snps`	Matrix of alleles, such that each locus has one column of alleles (R code: 1 = 1/1, 3 = 1/2, 2 = 2/2, 0 = missing). Rows contains alleles for each subject.
`trait`	Vector of trait values. For case control data use type= "binomial", trait must have values of 1 for event, 0 for no event.
`adj.var`	Matrix of (non-genetic) covariates used to adjust the regression model.
`type`	Character string defining type of trait, with values of gaussian, binomial, families, survival, and casecohort.
`famid`	vector; Identifier for every family; needed by type="families".
`patid`	vector; Identifier for every individual; needed by type="families" and type="casecohort".
`fid`	vector; Identifier for father (0=unkown); needed by type="families".
`mid`	vector; Identifier for mother (0=unkown); needed by type="families".
`start.time`	vector; age at the start of the follow-up.
`stop.time`	vector; age at the end of the follow-up.
`subcohort`	A logical value (TRUE or FALSE). If TRUE, the individual is in the subcohort.
`stratvar`	vector; names the variables that determine the stratification.ss
`robust`	A logical value (TRUE or FALSE). If TRUE, request the robust sandwich estimate.
`marker.label`	Vector of labels for marker.
`prt`	A logical value (TRUE or FALSE). If TRUE, an overview is printed.
`ties`	defines the handling of ties in case-cohort design: "efron" (default),"breslow","exact".

For normal distributed phenotypes from independent individuals we prefer an F test and for case control data we prefer the likelihood ratio test (logistic regression) in comparison of full model with genetic and non-genetic factors to a reduced model, which includes only non-genetic variables. In the case of no specified non-genetic variable only the intercept is used. If one of these tests are significance we assume a genetic effect. In case of family data the weigthed TDT statistic is used.

So far SURVIVAL data is not supported.

Cox proportional hazards regression modified for case cohort designs according to the Prentice method will be used by type="casecohort".

As genetic effect the allele dosis (0, 1, 2) is modelled.

single.snp.test returns an object of class data.frame containing the following components:

`snp`	snp number
`N`	number of individuals
`type`	type of depending variable
`beta`	estimation of beta coefficient out of full regression model
`se(beta)`	estimation of standard error of beta coefficient out of full regression model
`exp(beta)`	Odds ratio=exp(beta.estimate) are calculated, if type = "binomial". In case of type = "casecohort" hazard ratio is calculated.
`lower.95`	lower limit of 95 % confidence intervall for exp(beta).
`upper.95`	upper limit of 95 % confidence intervall for exp(beta).
`aic`	Akaike's An Information Criterion (AIC) of full model

Sven Knueppel and Klaus Rohde

Knueppel S, Esparza-Gordillo J, Marenholz I, Holzhuetter HG,

Bauerfeind A, Ruether A, Weidinger S, Lee Y-A, Rohde K.

Multi-locus stepwise regression: a haplotype-based algorithm

for finding genetic associations applied to atopic dermatitis.

BMC Med Genet 2012;13(1):8.

single.haplotype.test

N <- 2000
nloci <- 14
set.seed(1234)
y <- sample(c(0, 1), N, replace = TRUE)
snp <- matrix(sample(c(1, 2, 3), N * nloci, replace = TRUE), 
              ncol = nloci)
colnames(snp) <- paste("SNP", 1:nloci, sep = "")

adj.var <- matrix(rnorm(N * 3), ncol = 3)
colnames(adj.var) <- paste("A", 1:3, sep = "")

sst <- single.snp.test(snps = snp, trait = y, adj.var = adj.var,
    type = "binomial", prt = TRUE)

Loading required package: survival
Start procedure: single.snp.test
Individuals:          2000(0 excluded)
SNPs:                 14
Trait type:           binomial
Statistic:            Chisq
Number of covariates: 3