association(SNPassoc)
association()所属R语言包：SNPassoc
Association analysis between a single SNP and a given phenotype
一个单一的SNP和一个给定的表型之间的关联性分析
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function carries out an association analysis between a single SNP and a dependent variable (phenotype) under five different genetic models (inheritance patterns): codominant, dominant, recessive, overdominant and log-additive. The phenotype may be quantitative or categorical. In the second case (e.g. case-control studies) this variable must be of class 'factor' with two levels.
此功能进行一个单一的SNP和因变量（表型），在5个不同的遗传模型（遗传模式）：显性，显性，隐性，超显性和log添加剂的关联分析。可以是定量的或绝对的表型。在第二种情况下（如病例对照研究），这个变量必须是类的因素“两个级别。
用法----------Usage----------
association(formula, data, model=c("all"), model.interaction=
c("codominant"), subset, name.snp = NULL, quantitative =
is.quantitative(formula,data), genotypingRate= 0,
level = 0.95, ...)
参数----------Arguments----------
参数：formula
a symbolic description of the model to be fited (a formula object). It might have either a continuous variable (quantitative traits) or a factor variable (case-control studies) as the response on the left of the ~ operator and a term corresponding to the SNP on the right. This term must be of class snp (e.g. ~snp(var), where var is a given SNP), and it is required. Terms with additional covariates on the right of the ~ operator may be added to fit an adjusted model (e.g., ~var1+var2+...+varN+SNP). The formula allows to incorporate more than one object of class snp. In that case, the analysis is done for the first SNP which appears in the formula adjusted by the others covariates and other additional SNPs.
一个象征性的模型来描述fited（一个公式对象）。它可能有一个连续变量（数量性状）或一个因素变量（病例对照研究），在左侧的~运营商和术语对应的SNP在正确的响应。该术语必须是类snp（例如~SNP（变种），其中var是一个给定的单核苷酸多态性），并且它是必需的。额外的协变量~操作员在右侧的术语可以被添加到适合调整后的模型（例如，~VAR1 + VAR2 + ... + varN + SNP）。计算公式可以将多个对象的类snp。在这种情况下，分析完成第一SNP的出现在由其他协变量和其他额外的单核苷酸多态性的公式调整。
参数：data
a required dataframe of class 'setupSNP' containing the variables in the model.
类的setupSNP“，包含在模型中的变量所需的数据框。
参数：model
a character string specifying the type of genetic model (mode of inheritance) for the SNP. This indicates how the genotypes should be collapsed. Possible values are "codominant", "dominant", "recessive", "overdominant", "additive" or "all". The default is "all" that fits the 5 possible genetic models. Only the first words are required, e.g "co", "do", etc.
一个字符串指定类型的遗传模型（模式继承）的SNP。这表明如何倒塌的基因型应。可能的值是“显性”，“显性”，“隐性”，“超显性”，“添加剂”或“全部”。默认值是“所有”，适合5种可能的遗传模式。只有第一个字是必需的，例如，“合作”，“做”，等等。
参数：model.interaction
a character string specifying the type of genetic model (mode of inheritance) assumed for the SNP when it is included in a interaction term. Possible values are "codominant", "dominant", "recessive", "overdominant". The default is "codominant".
假设为SNP的字符串指定的类型的遗传模型（继承模式）时，它被包含在一个交互项。可能的值是“显性”，“显性”，“隐性”，“超显性”。默认值是“显性”。
参数：subset
an optional vector specifying a subset of observations to be used in the fitting process
一个可选的向量确定的装配过程中可以使用的观测值的一个子集
参数：name.snp
optional label of the SNP variable to be printed.
可选的单核苷酸多态性变量标签被打印出来。
参数：quantitative
logical value indicating whether the phenotype (that which is in the left of the operator ~ in 'formula' argument) is quantitative. The function 'is.quantitative' returns FALSE when the phenotype is a variable with two categories (i.e. indicating case-control status). Thus, it is not a required argument but it may be modified by the user.
逻辑值，该值指示是否在“公式”的说法~表型（即是在左侧的运营商）是定量的。的功能“is.quantitative的返回FALSE时，表型是一个变量有两大类（即指示的情况下控制状态）。因此，它是不必需的参数，但它可以由用户修改。
参数：genotypingRate
minimum percentage of genotype rate for the SNP to be analyzed. Default is 0% (e.g. all SNPs are analyzed). This parameter should not be changed. It is used in the function 'WGassociation'.
最低百分比为SNP基因型率来进行分析。默认值是0％（例如，所有的SNP分析）。该参数不应该被改变。这是用在的函数WGassociation。
参数：level
signification level for confidence intervals.
置信区间的意义水平。
参数：...
Other arguments to be passed through glm function
其他参数通过GLM功能
Details
详细信息----------Details----------
This function should be called by the user when we are interested in analyzing an unique SNP. It is recommended to use WGassociation function when more than one SNP is studied. <br>
当我们在分析一个独特的SNP有兴趣的，可以由用户调用这个函数应该。建议使用WGassociation函数当一个以上的SNP进行了研究。参考
值----------Value----------
For each genetic model (codominant, dominant, recessive, overdominant, and log-additive) the function gives a matrix with sample size and percentages for each genotype, the Odds Ratio and its 95% confidence interval (taking the most frequent homozygous genotype as the reference), the p-value corresponding to the likelihood ratio test obtained from a comparison with the null model, and the Akaike Information Criterion (AIC) of each genetic model. In the case of analyzing a quantitative trait, the function returns a matrix with sample size, mean and standard errors for each genotype, mean difference and its 95% confidence interval with respect to the most frequent homozygous genotype, the p-value obtained from an overall gene effect and the Akaike Information Criterion (AIC) of each genetic model.
对于每一个遗传模型（显性的，显性的，隐性的，超显性和log添加剂）功能使每个基因型矩阵与样本的大小和比例，比值比和95％可信区间（最常见的纯合子基因型的参考），对应于从比较，与空模型，和每种遗传模型的Akaike信息准则（AIC）得到的似然比检验的p-值。的数量性状分析的情况下，该函数返回一个矩阵与样本量，平均每个基因型的标准误差，意味着最常见的基因型差异，其95％的置信区间，p值从整体基因效应和Akaike信息准则（AIC）每种遗传模型。
When an interaction term (a categorical covariate with an SNP) is included in the model, three different tables are given. The first one correponds to the full interaction matrix where the ORs (or mean differences if a quantitative trait is analyzed) are expressed with respect to the non variant genotype and the first category of the covariate. The other two tables show the ORs and their 95% confidence intervals for both marginal models. P values for interaction and trend are also showed in the output.
当相互作用项（具有SNP的类别的协变量）是包含在模型中，三个不同的表中给出。充分的相互作用的OR（或平均值的差异，如果分析的数量性状）相对于非基因型和第一类的协变量表示的矩阵，其中，第一个correponds。其他两个表显示的OR值和95％的置信区间为边际模型。 P值的交互和趋势还表明在输出中。
参考文献----------References----------
JR Gonzalez, L Armengol, X Sole, E Guino, JM Mercader, X Estivill, V Moreno. SNPassoc: an R package to perform whole genome association studies. Bioinformatics, 2007;23(5):654-5.
Iniesta R, Guino E, Moreno V. Statistical analysis of genetic polymorphisms in epidemiological studies. Gac Sanit. 2005;19(4):333-41.
Elston RC. Introduction and overview. Statistical methods in genetic epidemiology. Stat Methods Med Res. 2000;9:527-41.
参见----------See Also----------
WGassociation
WGassociation
实例----------Examples----------
data(SNPs)
# first, we create an object of class 'setupSNP'[首先，我们创建对象类的setupSNP“]
datSNP<-setupSNP(SNPs,6:40,sep="")
# case-control study, crude analysis[病例对照研究，粗略分析]
association(casco~snp10001, data=datSNP)
# case-control study, adjusted by sex and arterial blood pressure[调整的病例对照研究，按性别和动脉血压]
association(casco~sex+snp10001+blood.pre, data=datSNP)
# quantitative trait, crude analysis[数量性状，原油分析]
association(log(protein)~snp10001,data=datSNP)
# quantitative trait, adjusted by sex[调整性的数量性状，]
association(log(protein)~snp10001+sex,data=datSNP)
#[]
# Interaction analysis[相互作用分析]
#[]
# Interaction SNP and factor[互动SNP和因素]
association(log(protein)~snp10001*sex+blood.pre, data=datSNP,
model="codominant")
# Interaction SNP and SNP (codominant and codominant)[互动SNP和SNP（显性和共显性）]
association(log(protein)~snp10001*factor(snp10002)+blood.pre,
data=datSNP, model="codominant")
# Interaction SNP and SNP (dominant and recessive)[互动SNP和SNP（显性和隐性）]
association(log(protein)~snp10001*factor(recessive(snp100019))+blood.pre,
data=datSNP, model="dominant")
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。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
|
发表于 2012-9-30 11:07:17
