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gtWAS:将基因组和转录组数据与表型数据联合关联分析的Rpackage

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发表于 2018-10-11 22:13:37 | 显示全部楼层 |阅读模式
本帖最后由 2xia 于 2018-10-11 23:00 编辑

gtWAS--Genome and Transcriptome Wide Association Study

工作之余,结合之前研究生的课题,自己瞎琢磨出一个将基因组和转录组数据与表型数据联合关联分析的R程序包;
该程序包已于2018年1月份上传到CRAN;
gtWAS主要考虑了QTL定位、GWAS分析和eQTL分析,将自变量由之前的SNP等转换为基因表达水平数据和SNP相互嵌套的数据(1.SNP数据 2.表达数据 3.不同表达水平下SNP数据 4.不同SNP下表达数据),通过(1)BLUE/BLUP模型计算表型数据,(2)逐步回归分析计算最优线性模型,(3)Full model和Reduced model的F检验计算每个自变量的p值或LOD值(4)通过Bonferroni Correction确定阈值。
欢迎大家使用。


❗️ This is a read-only mirror of the CRAN R package repository.
1. Introduction
  • What is the Genome and Transcriptome Wide Association Study?
Quantitative trait loci(QTL) mapping and genome wide association analysis(GWAS) are used to find candidate molecular marker or region associated with phenotype based on linkage analysis and linkage disequilibrium. Gene expression quantitative trait loci mapping(eQTL) is used to find candidate molecular marker or region associated with gene expression. In this package, we applied the method in Liu W. (2011) doi:10.1007/s00122-011-1631-7 and Gusev A. (2016) doi:10.1038/ng.3506 to genome and transcriptome wide association study, which is aimed at revealing the association relationship between phenotype and molecular markers, expression levels, molecular markers nested within different related expression effect and expression effect nested within different related molecular marker effect.
  • What is the main features?
Genome marker effects(or Transcriptome effect) are nested within Transcriptome effects(or Genome marker effects) are perfomed as independent variable besides general methods of GWAS and TWAS.
2. Statistical details in this package
  • The best linear model can be obtained by stepwise regression analysis.
  • F test based on full and reduced model are performed to obtain p value or likelihood ratio statistic(LOD).
  • Thresholds can be obtained by permutation test(for LOD) and Bonferroni Correction.
3. Usage and Examples
[AppleScript] 纯文本查看 复制代码
#install.package("gtWAS")
library(gtWAS)
data(Tdata)
data(alldata)
independent <- "E(B)"
gtWAS(Tdata,alldata,independent,selection='stepwise',select="SBC",Choose="SBC",vecThr=c(0.05,0.05,0.05),correct = "Bonferroni")
##---#---##
##Maybe it is not a good idea to use the same varible for the gtWAS function and gtWAS package name

We welcome commits from researchers who wish to improve our software, and good luck to you.

github:  
CRAN:  




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