tdt.snp(snpStats)
tdt.snp()所属R语言包：snpStats
1-df and 2-df tests for genetic associations with SNPs (or
1-DF和2-DF测试与单核苷酸多态性（或遗传协会
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Given large-scale SNP data for families comprising both parents and one or more affected offspring, this function computes 1 df tests (the TDT test) and a 2 df test based on observed and expected transmissions of genotypes. Tests based on imputation rules can also be carried out.
鉴于双方父母和一个或多个受影响的后代组成的家庭大规模SNP数据，这个函数计算1 DF测试（测试，TDT）和2 DF测试基于基因型的观察和预期的传输。归责原则为基础的测试也可以进行。
用法----------Usage----------
tdt.snp(ped, id, father, mother, affected, data = sys.parent(), snp.data,
rules = NULL, snp.subset, check.inheritance = TRUE, robust = FALSE,
uncertain = FALSE, score = FALSE)
参数----------Arguments----------
参数：ped
Pedigree identifiers
谱系标识符
参数：id
Subject identifiers
主题标识符
参数：father
Identifiers for subjects' fathers
标识符为主体的父亲
参数：mother
Identifiers for subjects' mothers
标识符为主体的母亲
参数：affected
Disease status (TRUE if affected, FALSE otherwise)
疾病状态（TRUE，如果受影响，否则返回FALSE）
参数：data
A data frame in which to evaluate the previous five arguments
一个数据框，在评估前五个参数
参数：snp.data
An object of class "SnpMatrix" containing the SNP genotypes to be tested
一个类的对象"SnpMatrix"包含的SNP基因型进行测试
参数：rules
An object of class "ImputationRules". If supplied, the rules coded in this object are used, together with snp.data, to calculate tests for imputed SNPs
对象类"ImputationRules"。如果提供，使用，在此对象的编码规则与snp.data，一起估算单核苷酸多态性，计算测试
参数：snp.subset
A vector describing the subset of SNPs to be considered. Default action is to test all SNPs in snp.data or, in imputation mode, as specified by rules
一个向量描述的SNPs的子集加以考虑。默认动作是测试snp.data或归集模式，所有的SNPs，rules指定
参数：check.inheritance
If TRUE, each affected offspring/parent trio is tested for Mendelian inheritance and excluded if the test fails. If FALSE, misinheriting trios are used but the "robust" variance option is forced
如果是TRUE，每个受影响的后代/父母三人孟德尔遗传测试和排除如果测试失败。如果假，misinheriting的三重奏使用，但“稳健”的变异选项被迫
参数：robust
If TRUE, forces the robust (Huber-White) variance option (with ped determining independent "clusters")
如果是TRUE，迫使强劲（胡伯白）方差选项（ped确定独立的“聚类”）
参数：uncertain
If TRUE, uncertain genotypes are handed by replacing score contributions by their posterior expectations. Otherwise these are treated as missing. Setting this option authomatically invokes use of robust variance estimates
如果是TRUE，不确定的基因型交给更换后期望的得分贡献。否则被视为失踪。设置此选项authomatically调用robust方差估计的使用
参数：score
If TRUE, the output object will contain, for each SNP, the score vector and its variance-covariance matrix
如果TRUE，输出对象将包含，每个SNP，得分向量和协方差矩阵
Details
详情----------Details----------
Formally, the test statistics are score tests for the "conditioning on parental genotype" (CPG) likelihood. Parametrization of associations is the same as for the population-based tests calculated by single.snp.tests so that results from family-based and population-based studies can be combined using pool.
正式测试统计得分为“父母的基因型空调”（CPG）的可能性测试。协会的参数化计算的人口为基础的测试是相同的single.snp.tests这样的家庭为基础的，基于人口的研究结果可以结合使用pool。
When the function is used to calculate tests for imputed SNPs, the test is still an approximate score test. The current version does not use the family relationships in the imputation. With this option, the robust variance estimate is forced.
当函数被用来计算估算单核苷酸多态性测试，测试仍然是一个近似的得分测试。当前版本不使用在归集的家庭关系。使用此选项，强大的方差估计是被迫的。
The first five arguments are usually derived from a "pedfile". If a data frame is supplied for the data argument, the first five arguments will be evaluated in this frame. Otherwise they will be evaluated in the calling environment. If the arguments are missing, they will be assumed to be in their usual positions in the pedfile data frame i.e. in columns one to four for the identifiers and column six for disease status (with affected coded 2). If the pedfile data are obtained from a dataframe, the row names of the data and snp.data files will be used to align the pedfile and SNP data. Otherwise, these vectors will be assumed to be in the same order as the rows of snp.data.
通常来自“pedfile”中的前五个参数。如果data参数提供一个数据框，前五个参数将在这个框架评估。否则，他们将在调用的环境评估。如果参数丢失，他们将被认为是他们一贯的立场在pedfile列中的数据框，即疾病状态的标识符和列6四（影响编码2）。如果的pedfile数据从dataframe的获得，该行名称data和将被用于snp.data文件，对齐pedfile和SNP数据。否则，这些向量将被认为是在作为snp.data的行的顺序相同。
The snp.subset argument can be a logical, integer, or character vector.
snp.subset参数可以是一个逻辑，整数或字符的向量。
If imputed rather than observed SNPs are tested, or if check.inheritance is set to FALSE, the "robust" variance estimate is used regardless of the value supplied for the robust argument.
如果打杀，而不是观察到的单核苷酸多态性进行测试，或check.inheritance如果设置为FALSE，“稳健”的方差估计是不管用的robust参数提供的值。
值----------Value----------
An object of class "SingleSnpTests". If score=TRUE, the output object will be of the extended class "SingleSnpTestsScore" containing additional slots holding the score statistics and their variances (and covariances). This allows meta-analysis using the pool function.
对象类"SingleSnpTests"。如果score=TRUE，输出对象将扩展类"SingleSnpTestsScore"持有的得分统计和方差和协的附加插槽。这允许使用pool函数的荟萃分析。
注意----------Note----------
When the snps are on the X chromosome (i.e. when the snp.data argument is of class "XSnpMatrix"), the tests are constructed in the same way as was described by Clayton (2008) for population-based association tests i.e. assuming that genotype relative risks for males mirror thos of homozygous females
当SNPs是在X染色体上（即snp.data参数是类的"XSnpMatrix"），构建以同样的方式被克莱顿（2008年）人口为基础的关联测试测试即假设基因型相对危险性为纯合子女性的男性镜thos
作者（S）----------Author(s)----------
David Clayton <a href="mailto:david.clayton@cimr.cam.ac.uk">david.clayton@cimr.cam.ac.uk</a>
参考文献----------References----------
Biostatistics, 9:593-600.)
参见----------See Also----------
single.snp.tests, impute.snps, pool, ImputationRules-class, SingleSnpTests-class, SingleSnpTestsScore-class
single.snp.tests，impute.snps，pool，ImputationRules-class，SingleSnpTests-class，SingleSnpTestsScore-class
举例----------Examples----------
data(families)
tdt.snp(data=pedData, snp.data=genotypes)
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。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
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发表于 2012-2-26 14:49:04
