tdt: Genotypic TDT

In trio: Testing of SNPs and SNP Interactions in Case-Parent Trio Studies

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)

## S3 method for class 'tdt'
print(x, digits = 4, ...)

## S3 method for class 'colTDT'
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.
x	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

An object of class tdt or colTDT consisting of the following numeric values or vectors, respectively:

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

Author(s)

Holger Schwender, [e-mail]

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.

See Also

tdt2way, ped2geno

Examples

# 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)

Example output

        Genotypic TDT Based on 3 Pseudo Controls 

Model Type: Additive

         Coef     RR  Lower Upper     SE Statistic p-Value Trios
SNP1 -0.04256 0.9583 0.6396 1.436 0.2063   0.04255 0.83658    72
SNP2 -0.19671 0.8214 0.5561 1.213 0.1990   0.97724 0.32288    73
SNP3 -0.22884 0.7955 0.5103 1.240 0.2265   1.02085 0.31232    66
SNP4 -0.13353 0.8750 0.5783 1.324 0.2113   0.39941 0.52740    71
SNP5  0.09764 1.1026 0.7148 1.701 0.2211   0.19497 0.65881    64
SNP6  0.44895 1.5667 0.9910 2.477 0.2337   3.69084 0.05471    63
        Genotypic TDT Based on 3 Pseudo Controls 

Model Type: Additive

         Coef     RR  Lower Upper     SE Statistic p-Value Trios
SNP1 -0.04256 0.9583 0.6396 1.436 0.2063   0.04255 0.83658    72
SNP2 -0.19671 0.8214 0.5561 1.213 0.1990   0.97724 0.32288    73
SNP3 -0.22884 0.7955 0.5103 1.240 0.2265   1.02085 0.31232    66
SNP4 -0.13353 0.8750 0.5783 1.324 0.2113   0.39941 0.52740    71
SNP5  0.09764 1.1026 0.7148 1.701 0.2211   0.19497 0.65881    64
SNP6  0.44895 1.5667 0.9910 2.477 0.2337   3.69084 0.05471    63

trio documentation built on Nov. 8, 2020, 7:41 p.m.