Cargando…

GxGrare: gene-gene interaction analysis method for rare variants from high-throughput sequencing data

BACKGROUND: With the rapid advancement of array-based genotyping techniques, genome-wide association studies (GWAS) have successfully identified common genetic variants associated with common complex diseases. However, it has been shown that only a small proportion of the genetic etiology of complex...

Descripción completa

Detalles Bibliográficos
Autores principales: Kwon, Minseok, Leem, Sangseob, Yoon, Joon, Park, Taesung
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5861485/
https://www.ncbi.nlm.nih.gov/pubmed/29560826
http://dx.doi.org/10.1186/s12918-018-0543-4
_version_ 1783308105872834560
author Kwon, Minseok
Leem, Sangseob
Yoon, Joon
Park, Taesung
author_facet Kwon, Minseok
Leem, Sangseob
Yoon, Joon
Park, Taesung
author_sort Kwon, Minseok
collection PubMed
description BACKGROUND: With the rapid advancement of array-based genotyping techniques, genome-wide association studies (GWAS) have successfully identified common genetic variants associated with common complex diseases. However, it has been shown that only a small proportion of the genetic etiology of complex diseases could be explained by the genetic factors identified from GWAS. This missing heritability could possibly be explained by gene-gene interaction (epistasis) and rare variants. There has been an exponential growth of gene-gene interaction analysis for common variants in terms of methodological developments and practical applications. Also, the recent advancement of high-throughput sequencing technologies makes it possible to conduct rare variant analysis. However, little progress has been made in gene-gene interaction analysis for rare variants. RESULTS: Here, we propose GxGrare which is a new gene-gene interaction method for the rare variants in the framework of the multifactor dimensionality reduction (MDR) analysis. The proposed method consists of three steps; 1) collapsing the rare variants, 2) MDR analysis for the collapsed rare variants, and 3) detect top candidate interaction pairs. GxGrare can be used for the detection of not only gene-gene interactions, but also interactions within a single gene. The proposed method is illustrated with 1080 whole exome sequencing data of the Korean population in order to identify causal gene-gene interaction for rare variants for type 2 diabetes. CONCLUSION: The proposed GxGrare performs well for gene-gene interaction detection with collapsing of rare variants. GxGrare is available at http://bibs.snu.ac.kr/software/gxgrare which contains simulation data and documentation. Supported operating systems include Linux and OS X. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12918-018-0543-4) contains supplementary material, which is available to authorized users.
format Online
Article
Text
id pubmed-5861485
institution National Center for Biotechnology Information
language English
publishDate 2018
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-58614852018-03-22 GxGrare: gene-gene interaction analysis method for rare variants from high-throughput sequencing data Kwon, Minseok Leem, Sangseob Yoon, Joon Park, Taesung BMC Syst Biol Research BACKGROUND: With the rapid advancement of array-based genotyping techniques, genome-wide association studies (GWAS) have successfully identified common genetic variants associated with common complex diseases. However, it has been shown that only a small proportion of the genetic etiology of complex diseases could be explained by the genetic factors identified from GWAS. This missing heritability could possibly be explained by gene-gene interaction (epistasis) and rare variants. There has been an exponential growth of gene-gene interaction analysis for common variants in terms of methodological developments and practical applications. Also, the recent advancement of high-throughput sequencing technologies makes it possible to conduct rare variant analysis. However, little progress has been made in gene-gene interaction analysis for rare variants. RESULTS: Here, we propose GxGrare which is a new gene-gene interaction method for the rare variants in the framework of the multifactor dimensionality reduction (MDR) analysis. The proposed method consists of three steps; 1) collapsing the rare variants, 2) MDR analysis for the collapsed rare variants, and 3) detect top candidate interaction pairs. GxGrare can be used for the detection of not only gene-gene interactions, but also interactions within a single gene. The proposed method is illustrated with 1080 whole exome sequencing data of the Korean population in order to identify causal gene-gene interaction for rare variants for type 2 diabetes. CONCLUSION: The proposed GxGrare performs well for gene-gene interaction detection with collapsing of rare variants. GxGrare is available at http://bibs.snu.ac.kr/software/gxgrare which contains simulation data and documentation. Supported operating systems include Linux and OS X. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12918-018-0543-4) contains supplementary material, which is available to authorized users. BioMed Central 2018-03-19 /pmc/articles/PMC5861485/ /pubmed/29560826 http://dx.doi.org/10.1186/s12918-018-0543-4 Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Kwon, Minseok
Leem, Sangseob
Yoon, Joon
Park, Taesung
GxGrare: gene-gene interaction analysis method for rare variants from high-throughput sequencing data
title GxGrare: gene-gene interaction analysis method for rare variants from high-throughput sequencing data
title_full GxGrare: gene-gene interaction analysis method for rare variants from high-throughput sequencing data
title_fullStr GxGrare: gene-gene interaction analysis method for rare variants from high-throughput sequencing data
title_full_unstemmed GxGrare: gene-gene interaction analysis method for rare variants from high-throughput sequencing data
title_short GxGrare: gene-gene interaction analysis method for rare variants from high-throughput sequencing data
title_sort gxgrare: gene-gene interaction analysis method for rare variants from high-throughput sequencing data
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5861485/
https://www.ncbi.nlm.nih.gov/pubmed/29560826
http://dx.doi.org/10.1186/s12918-018-0543-4
work_keys_str_mv AT kwonminseok gxgraregenegeneinteractionanalysismethodforrarevariantsfromhighthroughputsequencingdata
AT leemsangseob gxgraregenegeneinteractionanalysismethodforrarevariantsfromhighthroughputsequencingdata
AT yoonjoon gxgraregenegeneinteractionanalysismethodforrarevariantsfromhighthroughputsequencingdata
AT parktaesung gxgraregenegeneinteractionanalysismethodforrarevariantsfromhighthroughputsequencingdata