M. Emily, Nicolas Sounac, F. Kroell, Magalie Houée-Bigot
{"title":"Gene-Based Methods to Detect Gene-Gene Interaction in R: The GeneGeneInteR Package","authors":"M. Emily, Nicolas Sounac, F. Kroell, Magalie Houée-Bigot","doi":"10.18637/jss.v095.i12","DOIUrl":null,"url":null,"abstract":"GeneGeneInteR is an R package dedicated to the detection of an association between a case-control phenotype and the interaction between two sets of biallelic markers (single nucleotide polymorphisms or SNPs) in case-control genome-wide associations studies. The development of statistical procedures for searching gene-gene interaction at the SNP-set level has indeed recently grown in popularity as these methods confer advantage in both statistical power and biological interpretation. However, all these methods have been implemented in home made softwares that are for most of them available only on request to the authors and at best have a web interface. Since the implementation of these methods is not straightforward, there is a need for a user-friendly tool to perform gene-based genegene interaction. The purpose of GeneGeneInteR is to propose a collection of tools for all the steps involved in gene-based gene-gene interaction testing in case-control association studies. Illustrated by an example of a dataset related to rheumatoid arthritis, this paper details the implementation of the functions available in GeneGeneInteR to perform an analysis of a collection of SNP sets. Such an analysis aims at addressing the complete statistical pipeline going from data importation to the visualization of the results through data manipulation and statistical analysis.","PeriodicalId":17237,"journal":{"name":"Journal of Statistical Software","volume":null,"pages":null},"PeriodicalIF":5.4000,"publicationDate":"2020-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Statistical Software","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.18637/jss.v095.i12","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 3
Abstract
GeneGeneInteR is an R package dedicated to the detection of an association between a case-control phenotype and the interaction between two sets of biallelic markers (single nucleotide polymorphisms or SNPs) in case-control genome-wide associations studies. The development of statistical procedures for searching gene-gene interaction at the SNP-set level has indeed recently grown in popularity as these methods confer advantage in both statistical power and biological interpretation. However, all these methods have been implemented in home made softwares that are for most of them available only on request to the authors and at best have a web interface. Since the implementation of these methods is not straightforward, there is a need for a user-friendly tool to perform gene-based genegene interaction. The purpose of GeneGeneInteR is to propose a collection of tools for all the steps involved in gene-based gene-gene interaction testing in case-control association studies. Illustrated by an example of a dataset related to rheumatoid arthritis, this paper details the implementation of the functions available in GeneGeneInteR to perform an analysis of a collection of SNP sets. Such an analysis aims at addressing the complete statistical pipeline going from data importation to the visualization of the results through data manipulation and statistical analysis.
期刊介绍:
The Journal of Statistical Software (JSS) publishes open-source software and corresponding reproducible articles discussing all aspects of the design, implementation, documentation, application, evaluation, comparison, maintainance and distribution of software dedicated to improvement of state-of-the-art in statistical computing in all areas of empirical research. Open-source code and articles are jointly reviewed and published in this journal and should be accessible to a broad community of practitioners, teachers, and researchers in the field of statistics.