tdt {trio} | R Documentation

## 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)

    ## 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, holger.schwender@udo.edu

### 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)

[Package trio version 3.18.0 Index]