CGEN(CGEN)
CGEN()所属R语言包：CGEN
An R package for analysis of case-control studies in genetic epidemiology
遗传流行病学病例对照研究分析的R包
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This package is for logistic regression analyses of SNP data in case-control studies. It is designed to give the users flexibility of using a number of different methods for analysis of SNP-environment or SNP-SNP interactions. It is known that power of interaction analysis in case-control studies can be greatly enhanced if it can be assumed that the factors (e.g. two SNPs) under study are independently distributed in the underlying population. The package implements a number of different methods that can incorporate such independence constraints into analysis of interactions in the setting of both unmatched and matched case-control studies. These methods are more general and flexible than the popular case-only method of analysis of interaction that also assumes gene-gene or/and gene-environment independence for the underlying factors in the underlying population. The package also implements various methods, based on shrinkage estimation and conditional-likelihoods, that can automatically adjust for possible violation of the independence assumption that could arise due to direct causal relationship (e.g. between a gene and a behavior exposure) or indirect correlation (e.g due to population stratification). A number of convenient summary and printing functions are included. The package will continue to be updated with new methods as they are developed. The methods are currently not suitable for analysis of SNPs on sex chromosomes.
这个包是在病例对照研究的SNP数据的logistic回归分析。它的设计，给用户使用一些不同的方法对环境的SNP或单核苷酸多态性，SNP的相互作用分析的灵活性。据了解，如果可以假设所研究的因素（如两个SNPs）是独立于底层的人口分布，病例对照研究中的相互作用分析的权力，可以大大提高。包实现了不同的方法，可以在无与伦比的和匹配的病例对照研究设置的相互作用分析纳入这种独立性约束。这些方法都比较一般较流行的情况下只相互作用分析的方法，还假定基因 - 基因和/或基因与环境的底层人口的基本因素的独立性灵活。该软件包还实现了各种方法，基于收缩估计和有条件的似然性，可能违反独立性假设，可能出现由于直接的因果关系（如之间的基因和行为暴露）或间接相关，可以自动调整（如由于人口分层）。包括一些方便的汇总和打印功能。该软件包将继续更新，因为他们正在开发的新方法。目前的方法是不适合分析性染色体上的SNP。
Details
详情----------Details----------
The main functions for unmatched data are snp.logistic and snp.scan.logistic. Whereas snp.logistic analyzes one SNP with each function call, snp.scan.logistic analyzes a collection of SNPs and writes the summary results to an external file. With snp.logistic, a data frame is input in which the SNP variable must be coded as 0-1-2 (or 0-1). If not, recode.geno can be used for recoding the SNP variable before calling snp.logistic. The functions getSummary, getWaldTest and snp.effects can be called for creating summary tables, computing Wald tests and joint/stratified effects using the returned object from snp.logistic (see Examples in snp.logistic). With snp.scan.logistic, the data is read in from external files defined in snp.list and pheno.list. The collection of p-values computed in snp.scan.logistic, can be plotted using the functions QQ.plot and chromosome.plot. <br> The function for analysis of matched case-control data is snp.matched. Optimal matching can be obtained from the function getMatchedSets. This package contains sample genotype data SNPdata, sample covariate data Xdata, and sample SNP meta data LocusMapData. The current version of the package is only suitable for analysis of SNPs on non-sex chromosomes.
无与伦比的数据的主要职能是snp.logistic和snp.scan.logistic。而snp.logistic分析每个函数调用一个SNP，snp.scan.logistic分析的SNP集合的汇总结果，并写入到外部文件。用snp.logistic，一个数据框中输入变量的SNP必须编码为0-1-2（或0-1）。如果没有，recode.geno可用于重新编码的SNP变量前调用snp.logistic。职能getSummary，getWaldTest和snp.effects可以称为创建汇总表，计算瓦尔德测试和联合/分层影响使用返回的对象从snp.logistic（见<X >Examples）。与snp.logistic，读取数据从外部文件定义snp.scan.logistic和snp.list。 pheno.list计算p值的集合，可以绘制使用职能snp.scan.logistic和QQ.plot。参考配对病例对照数据分析的功能是chromosome.plot。功能snp.matched可以得到最佳匹配。此包包含样品基因型数据getMatchedSets协数据，样品SNPdata，和样品的SNP元数据Xdata。包的当前版本是只适合对非性染色体单核苷酸多态性分析。
作者（S）----------Author(s)----------
Samsiddhi Bhattacharjee, Nilanjan Chatterjee and William Wheeler <wheelerb@imsweb.com>
参考文献----------References----------
exploting gene-environment independence in case-control studies. Biometrika, 2005, 92, 2, pp.399-418.
An empirical Bayes approach to trade-off between bias and efficiency. Biometrics 2008, 64(3):685-94.
type I error, power and designs. Genetic Epidemiology, 2008, 32:615-26.
case-control studies. Journal of the American Statistical Association, 2009, 104: 220-233.
Increased power for detecting associations, interactions and joint-effects. Genetic Epidemiology2005; 28:138-156.
Using Principal Components of Genetic Variation for Robust and Powerful Detection of Gene-Gene Interactions in Case-Control and Case-Only studies. American Journal of Human Genetics, 2010, 86(3):331-342.
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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 2012-2-25 14:27:15
