GxGrare: gene-gene interaction analysis method for rare variants from high-throughput sequencing data.
GxGrare: gene-gene interaction analysis method for rare variants from high-throughput sequencing data.
复制标题
DOI：
10.1186/s12918-018-0543-4
复制
发表时间：
2018-03-19
影响因子：
--
通讯作者：
Park T
中科院分区：
文献类型：
--
作者：
Kwon M;Leem S;Yoon J;Park T
随着基于阵列的基因分型技术的快速发展，全基因组关联研究（GWAS）已经成功地鉴定出与常见复杂疾病相关的常见遗传变异。然而，研究表明，只有一小部分复杂疾病的遗传病因可以用从GWAS中确定的遗传因素来解释。这种缺失的遗传力可能由基因-基因相互作用（上位性）和罕见变异来解释。在方法发展和实际应用方面，常见变异的基因-基因相互作用分析呈指数增长。此外，近年来高通量测序技术的进步使得进行罕见变异分析成为可能。然而，在罕见变异的基因互作分析方面进展甚微。本文在多因素降维分析框架下，提出了一种罕见变异的基因-基因互作方法GxGrare。该方法分为三个步骤；1)坍缩稀有变异，2)对坍缩稀有变异进行MDR分析，3)检测顶级候选相互作用对。GxGrare不仅可以检测基因间的相互作用，还可以检测单个基因内的相互作用。该方法用韩国人口的1080个全外显子组测序数据进行了说明，以确定2型糖尿病罕见变异的因果基因-基因相互作用。本文提出的GxGrare在检测罕见变异的基因-基因相互作用方面表现良好。GxGrare可在http://bibs.snu.ac.kr/software/gxgrare上获得，其中包含模拟数据和文档。支持的操作系统包括Linux和OS x。本文的在线版本（10.1186/s12918-018- 0534 -4）包含补充资料，授权用户可以使用。
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. 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. 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. The online version of this article (10.1186/s12918-018-0543-4) contains supplementary material, which is available to authorized users.
登录
查看更多内容
影响因子：
4.5
作者：
Liu DJ;Leal SM
通讯作者：
Leal SM
影响因子：
30.8
作者：
通讯作者：
--
影响因子：
1.2
作者：
Cingolani, Pablo;Platts, Adrian;Ruden, Douglas M.
通讯作者：
Ruden, Douglas M.
影响因子：
9.8
作者：
Li, Bingshan;Leal, Suzanne M.
通讯作者：
Leal, Suzanne M.
IGENT：用于全基因组基因 - 基因相互作用分析的有效基于熵的算法。
影响因子：
2.7
作者：
Kwon MS;Park M;Park T
通讯作者：
Park T