tdtGxG(trio)
tdtGxG()所属R语言包：trio
Genotypic TDT for Two-Way Interactions
双向互动的基因型TDT
译者：生物统计家园网 机器人LoveR
描述----------Description----------
tdtGxG and colGxG perform the genotypic TDT for the interaction of two SNPs or of each pair of columns of a genotype matrix, respectively.
tdtGxG和colGxG执行的相互作用的两个单核苷酸多态性的基因型TDT或每对基因型矩阵的列，分别。
fastGxG provides a fast implementation for the genotypic TDT for two-way interactions when considering the simplest conditional logistic regression model only containing one parameter for the interaction effect. It thus leads to the same results as colGxG with test = "screen". In fastGxGrec, an analytic solution to the genotypic TDT based on the simplest model for testing a recessive x recessive model is implemented, which is even faster than fastGxG with model = "recessive". In future versions of this package, fastGxG and fastGxGrec will be joint with colGxG.
fastGxG的双向互动提供了一个快速实现的基因型TDT时考虑的最简单的条件Logistic回归模型只包含一个参数的互动效应。因此，它会导致相同的结果colGxG与test = "screen"。在fastGxGrec，解析解的基因型TDT最简单的模型测试隐性X隐性模型的基础上实现的，这是速度甚至超过了fastGxGmodel = "recessive"。在未来的版本中这个包，fastGxG和fastGxGrec将联合与colGxG。
The genotypic TDT for testing two-way interactions makes use of the 16 possible genotypes that can be obtained from combining the parents' genotypes of the two considered SNPs. Thus, for each family, genotypes for one case (i.e. the affected offspring) and 15 pseudo-controls are used.
的基因型的TDT用于测试双向相互作用使得利用父母的两个单核苷酸多态性的基因型相结合，可以得到从16个可能的基因型。因此，对于每个家庭，基因型为1的情况下（即在受影响的后代）和15伪控制使用。
用法----------Usage----------
tdtGxG(snp1, snp2, test = c("epistatic", "lrt", "full", "screen"),
model = c("additive", "dominant", "recessive"))
colGxG(mat.snp, test = c("epistatic", "lrt", "full", "screen"), genes = NULL,
maf = FALSE, model = c("additive", "dominant", "recessive"))
fastGxG(mat.snp, model = c("additive", "dominant", "recessive"),
genes = NULL, interval = c(-10, 10), tol = 10^-8, maxiter = 1000,
size = 20)
fastGxGrec(mat.snp, genes = NULL, size = 20)
参数----------Arguments----------
参数：snp1, snp2
numeric vectors of length 3 * t representing two SNPs genotyped at t trios. Each of the t blocks (i.e. snp1[1:3], snp1[4:6], ..., and snp2[1:3], snp2[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 vectors 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.
数字向量的长度3 * t两个单核苷酸多态性基因分型t三重奏。 t块（即snp1[1:3]，snp1[4:6]，...，和snp2[1:3]，snp2[4:6]，...）必须包括的基因型中的每一个的父亲，母亲和后代（按照这个顺序）。必须被编码的基因型，0，1，和2。遗漏值是允许的，需要进行编码NA。因此，该向量必须具有相同的结构作为输出trio.check，或基因型示例数据集，例如trio.gen1（参见data(trio.gen1)），并且可以从一个路人文件生成，例如，采用ped2geno。
参数：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.
一个数字矩阵，其中每一列代表一个SNP。的单核苷酸多态性中的每一个必须具有相同的结构snp，并可以，例如，从一个路人文件生成通过采用ped2geno。
参数：test
character string naming the GxG test that should be performed. If test = "epistatic", then a conditional logistic regression version of the test proposed by Cordell (2002) is used to test for epistatistical interactions. If test = "full", a conditional logistic regression model containing one parameter for each SNP and one parameter for the interaction of these two SNPs will be fitted and a Wald test for the interaction term will be performed, where a genetic model specified by model is assumed for both SNPs. If test = "lrt", a likelihood ratio test is performed comparing the fit of this model with the fit of a conditional logistic regression model only containing the two parameters for the main effects of the SNPs. If test = "screen", a conditional logistic regression model only composed of one parameter for the interaction of the two SNPs will be fitted and a Wald test will be performed, where the genetic model specified by model is assumed for both SNPs.
字符串命名GXG应该执行的测试。如果test = "epistatic"，然后一个版本的测试科德尔（2002）提出的条件Logistic回归是用来测试epistatistical相互作用。 test = "full"如果，条件Logistic回归模型，其中包含每个SNP和一个参数一个参数，这两个单核苷酸多态性的相互作用将安装一个Wald检验的交互项进行，其中的遗传模型指定的 model假定两个单核苷酸多态性。如果test = "lrt"，似然比检验，比较适合这种模式适合的条件Logistic回归模型只包含两个参数的单核苷酸多态性的主要影响。如果test = "screen"，条件Logistic回归模型仅由一个参数的两个单核苷酸多态性的相互作用将安装一个Wald检验进行，其中的遗传模型指定的model假定为单核苷酸多态性。
参数：genes
a character vector containing the names of the genes to which the SNPs belong. If specified, only the two-way interactions between SNPs from different genes are tested. If NULL, all two-way interactions between all possible pairs of SNPs are tested.
含有基因的SNPs属于名称的字符矢量。如果指定了测试，只有从不同基因的单核苷酸多态性之间的双向互动。如果NULL，对所有可能的SNP位点之间的双向互动测试。
参数：maf
logical indicating whether the minor allele frequency (computed by considering the genotypes of only the parents) should be added to the output.
逻辑指示是否应该被添加到输出的次要等位基因频率（计算考虑只有父母的基因型）。
参数：model
type of model that should be considered. Abbreviations are allowed. Thus, e.g., model = "dom" will consider a dominant model for each of the respective two SNPs, and model = "r" an recessive model. Ignored if epistatic = TRUE.
应考虑的模型，该模型的类型。缩写是允许的。因此，例如，model = "dom"会考虑相应的两个单核苷酸多态性的主导模式，并model = "r"隐性模型。如果忽略epistatic = TRUE。
参数：interval
the end-points of the interval to be searched for the root. For details, see uniroot.
结束点的时间间隔要搜索的根。有关详细信息，请参阅uniroot。
参数：tol
the desired accuracy/convergence tolerance. For details, see uniroot.
所需的精度/收敛性。有关详细信息，请参阅uniroot。
参数：maxiter
the maximum number of iterations. For details, see uniroot.
最大迭代次数。有关详细信息，请参阅uniroot。
参数：size
the number of interactions considered simultaneously when computing the parameter estimates.
相互作用的数目计算参数估计值时，同时考虑。
值----------Value----------
Depending on test, the output contains statistics and p-values either of a likelihood ratio test (test = "epistatic" or test = "lrt") or the Wald statistics and the corresponding p-values for the interaction term in the conditional logistic regression model (test = "full" or test = "screen"). If maf = TRUE, a vector maf containing the minor allele frequencies of each SNP and a matrix mat.maf with two columns containing the SNP-wise minor allele frequencies for each tested pair of SNPs are added to the output of colGxG.
根据test，输出中包含的统计和p-值的似然比检验（test = "epistatic"或test = "lrt"）或Wald统计量和相应的p值的交互项条件Logistic回归模型（test = "full"或test = "screen"）的。如果maf = TRUE，一个向量maf次要等位基因频率的每个SNP矩阵mat.maf次要等位基因频率为每个测试对单核苷酸多态性SNP明智的两列添加到输出colGxG。
（作者）----------Author(s)----------
Holger Schwender, <a href="mailto:holger.schwender@udo.edu">holger.schwender@udo.edu</a>
参考文献----------References----------
Human Molecular Genetics, 11, 2463-2468.
Rapid Testing of SNPs and Gene-Environment Interactions in Case-Parent Trio Data Based on Exact Analytic Parameter Estimation. Biometrics. DOI: 10.1111/j.1541-0420.2011.01713.x.
参见----------See Also----------
tdt, ped2geno
tdt，ped2geno
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
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发表于 2012-10-1 12:07:08
