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

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

1 2 3 4 5 6 7 8 9 10 |

`snp` |
a numeric vector of length |

`mat.snp` |
a numeric matrix in which each column represents a SNP. Each of the SNPs must have the same structure
as |

`model` |
type of model that should be fitted. Abbreviations are allowed. Thus, e.g., |

`size` |
the number of SNPs considered simultaneously when computing the parameter estimates. Ignored if |

`x` |
an object of class |

`digits` |
number of digits that should be printed. |

`top` |
number of interactions that should be printed. If |

`...` |
ignored. |

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, |

`lowerRR` |
the lower bound of the 95% confidence interval for |

`upperRR` |
the upper bound of the 95% confidence interval for |

`usedTrios` |
the number of trios affecting the parameter estimation (only for |

`...` |
further internal parameters |

Holger Schwender, holger.schwender@udo.edu

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.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | ```
# 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)
``` |

```
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
```

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