SNPassoc全基因组关联分析

WGassociation(SNPassoc)

WGassociation()所属R语言包：SNPassoc

                                        Whole genome association analysis

                                         全基因组关联分析

描述----------Description----------

This function carries out a whole genome association analysis between the SNPs 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.

此功能进行全基因组关联分析的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。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----------

   WGassociation(formula, data, model = c("all"), quantitative = is.quantitative(formula, data),

                 genotypingRate = 80, level = 0.95, ...)

参数----------Arguments----------

参数：formula

either a symbolic description of the model to be fited (a formula object) without the SNP or the name of response variable in the case of fitting single models (e.g. unadjusted models). 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 terms with additional covariates on the right of the ~ operator may be  added to fit an adjusted model (e.g., ~var1+var2+...+varN+SNP). See details

无论是象征性的模型来描述fited（公式对象），而不SNP在安装单一车型（如未经调整的机型）的情况下，响应变量的名称。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）上的响应左侧额外的协变量的~运营商和条款的权利的~操作者可能被添加到适合调整后的模型（例如，~VAR1 + VAR2 + ... + varN + SNP）。查看详细资料

参数：data

a required dataframe of class 'setupSNP' containing the variables in the model and the SNPs

所需的数据框的类的setupSNP“，包含在模型中的变量和单核苷酸多态性

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", "log-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”，“log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：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 a given SNP to be included in the analysis. Default is 80%.

对于一个给定的SNP基因型率最低百分比要包含在分析中。默认值是80％。

参数：level

signification level for confidence intervals. Defaul 95%.

置信区间的意义水平。默认情况下将95％。

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 assesses the association between the response variable included in the left side in  the 'formula' and the SNPs included in the 'data' argument adjusted by those variables included  in the right side of the 'formula'. Different genetic models
may be analyzed using 'model' argument.

此函数评估公式和单核苷酸多态性，包括在数据参数调整式的右侧包括在这些变量中的左侧包括在响应变量之间的关联。不同的遗传模型可以使用“模式”的说法进行分析。

值----------Value----------

An object of class 'WGassociation'.

对象的类的WGassociation“。

'summary' returns a summary table by groups defined in info (genes/chromosomes).

“摘要”组中定义的信息（基因/染色体上的）返回一个汇总表。

'WGstats' returns a detailed output, similar to the produced by association.

“WGstats返回一个详细的输出，产生的association。

'pvalues' and 'print' return a table of p-values for each genetic model for each SNP. The first column indicates whether a problem with genotyping is present.

的“pvalues”和“打印”返回表的p-值为每个每个SNP位点的遗传模型。第一列表示是否存在的问题，基因分型。

'plot' produces a plot of p values in the -log scale. See plot.WGassociation for further details.

“图”产生的p值在对数刻度的图。见plot.WGassociation进一步的细节。

'labels' returns the names of the SNPs analyzed.

“标签”返回的SNP分析的名称。

The functions 'codominat', 'dominant', 'recessive', 'overdominant' and 'additive' are used to obtain the p values under these genetic models.

的功能codominat，“显性”，“隐性”，“超显性和添加剂被用于获得这些遗传模型下的p值。

See examples for further illustration about all previous issues.

例子进一步说明所有问题。

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.

参见----------See Also----------

scanWGassociation getSignificantSNPs

scanWGassociationgetSignificantSNPs

实例----------Examples----------

data(SNPs)

datSNP<-setupSNP(SNPs,6:40,sep="")

ansAll<-WGassociation(protein~1,data=datSNP,model="all")

# In that case the formula is not required. You can also write:[在这种情况下，式中不是必需的。你也可以这样写：]

# ansAll<-WGassociation(protein,data=datSNP,model="all")[ansAll <WGassociation（蛋白质，数据datSNP，模型=“所有”）]

#only codominant and log-additive[只有显性和log添加剂]

ansCoAd<-WGassociation(protein~1,data=datSNP,model=c("co","log-add"))

#for printing p values[用于打印的p值]

print(ansAll)

print(ansCoAd)

#for obtaining a matrix with the p palues[用于获得一个矩阵与p palues]

pvalAll<-pvalues(ansAll)

pvalCoAd<-pvalues(ansCoAd)

# when all models are fitted and we are interested in obtaining p values for different genetic models[所有型号都配和我们感兴趣的是获得不同的遗传模型的p值]

# codominant model[显性模型]

pvalCod<-codominant(ansAll)

# recessive model[隐性模型]

pvalRec<-recessive(ansAll)

# and the same for additive, dominant or overdominant[相同的添加剂，显性或超显性]

#summary[总结]

summary(ansAll)

#for a detailed report[一份详细报告]

WGstats(ansAll)

#for plotting the p values[绘制的p值]

plot(ansAll)

#[]

# Whole genome analysis[全基因组分析]

#[]

data(HapMap)

# Next steps may be very time consuming. So they are not executed[下一个步骤可能会花费很长的时间。因此，他们不执行]

#myDat<-setupSNP(HapMap, colSNPs=3:9809, sort = TRUE,[myDat <setupSNP（HapMap计划colSNPs = 3:9809，排序= TRUE，]

#   info=HapMap.SNPs.pos, sep="")[信息= HapMap.SNPs.pos，SEP =“”）]

#resHapMap<-WGassociation(group~1, data=myDat, model="log")[resHapMap <WGassociation（组1，数据= myDat，模型=“log”）]

# However, the results are saved in the object "resHapMap"[然而，结果被保存在对象“resHapMap”]

# to illustrate print, summary and plot functions[说明打印，汇总和绘图功能]

summary(resHapMap)

plot(resHapMap)

print(resHapMap)

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