tdt: Genotypic TDT

Description

Computes the genotypic TDT for a SNP or for each column of a matrix representing a SNP.

Usage

tdt(snp, model = c("additive", "dominant", "recessive"))
colTDT(mat.snp, model = c("additive", "dominant", "recessive"),  size = 50)
"print"(x, digits = 4, ...)
"print"(x, top = 5, digits = 4, ...)

Arguments

snp

a numeric vector of length $3 * t$ representing a SNP genotyped at $t$ trios. Each of the $t$ blocks (i.e. snp[1:3], snp[4:6], ...) must consist of the genotypes of father, mother, and offspring (in this order). The genotypes must be coded by 0, 1, and 2. Missing values are allowed and need to be coded by NA. The vector must thus have the same structure as the output of trio.check, or the genotype example data sets such as trio.gen1 (see data(trio.gen1)), and can be generated from a ped-file by, e.g., employing ped2geno.

mat.snp

a numeric matrix in which each column represents a SNP. Each of the SNPs must have the same structure as snp, and can, e.g., be generated from a ped-file by employing ped2geno.

model

type of model that should be fitted. Abbreviations are allowed. Thus, e.g., model = "dom" will fit a dominant model, and model = "r" an recessive model.

size

the number of SNPs considered simultaneously when computing the parameter estimates. Ignored if fast = FALSE.

an object of class tdt or colTDT, i.e. the output of the function tdt (or tdtGxG) or the function colTDT.

digits

number of digits that should be printed.

top

number of interactions that should be printed. If top is less than or equal to zero, set to NA, or larger than the number of SNPs, then the statistics for all SNPs are printed in the order as they were in the genotype matrix used as input into colTDT. Otherwise, the top interactions with the smallest p-values are printed.

...

ignored.

Value

coef: the estimated parameter,
se: the estimated standard deviation of the parameter estimate,
stat: Wald statistic,
RR: the relative risk, i.e.\ for trio data, exp(coef) (see Schaid, 1996),
lowerRR: the lower bound of the 95% confidence interval for RR,
upperRR: the upper bound of the 95% confidence interval for RR,
usedTrios: the number of trios affecting the parameter estimation (only for colTDT),
...: further internal parameters

References

Schaid, D.J. (1996). General Score Tests for Associations of Genetic Markers with Disease Using Cases and Their Parents. Genetic Epidemiology, 13, 423-449.

Schwender, H., Taub, M.A., Beaty, T.H., Marazita, M.L., and Ruczinski, I. (2011). Rapid Testing of SNPs and Gene-Environment Interactions in Case-Parent Trio Data Based on Exact Analytic Parameter Estimation. Biometrics, 68, 766-773.

Examples

Run this code

# Load the simulated data.
data(trio.data)

# One particular SNP (e.g., the one in the first 
# column of mat.test) can be tested by
tdt.out <- tdt(mat.test[,1])

# All SNPs in mat.test can be tested by
tdt.out2 <- colTDT(mat.test)

# By default, an additive mode of inheritance is considered.
# If another mode, e.g., the dominant mode, should be 
# considered, then this can be done by
tdt.out3 <- colTDT(mat.test, model = "dominant")

# By default, statistics for the top 5 SNPs are displayed.
# If another number of SNPs, say 10, should be displayed,
# then this can be done by
print(tdt.out2, top = 10)

# The statistics for all SNPs (not ordered by their 
# significance) can be obtained by
print(tdt.out2, top = 0)