INTERSNP: genome-wide interaction analysis guided by a priori information
INTERSNP: genome-wide interaction analysis guided by a priori information
复制标题
DOI：
10.1093/bioinformatics/btp596
复制
发表时间：
2009-12-15
期刊：
影响因子：
5.8
通讯作者：
Becker, Tim
中科院分区：
文献类型：
--
作者：
Herold, Christine;Steffens, Michael;Becker, Tim
全基因组关联研究已经确定了数百个与复杂疾病相关的基因组区域。尽管如此，它们的很大一部分遗传性仍然无法解释。基因变异之间的相互作用是“遗传性缺失”的几个假定解释之一，因此，下一步的分析是令人信服的。然而，如果没有大规模的并行化，对来自标准标记面板的所有SNP对的全基因组相互作用分析(GWIA)在计算上是不可行的。此外，所有SNP三元组中的Gwia都是乌托邦的。为了克服这些计算限制，我们提出了一种基于先验信息选择SNPs组合进行交互分析的Gwia方法。信息来源是统计证据(中等水平的单标记关联)、遗传相关性(基因组位置)和生物相关性(SNP功能类别和途径信息)。我们介绍了INTERSNP软件包，它实现了Logistic回归框架以及用于多个SNP联合分析的对数-线性模型。提供了来自KEGG数据库的SNP注释和路径的自动处理。此外，还实现了判断全基因组重要性的蒙特卡罗模拟。我们介绍了可以使用INTERSNP进行的各种有意义的GWIA策略。例如，典型的例子是对所有非同义SNP的分析，或者对位于共同路径中并在前50,000个单标记结果中的三个SNP的所有组合的分析。我们通过对GWAs数据集的应用证明了这些策略和其他GWIA策略的可行性，并讨论了有希望的结果。
Genome-wide association studies (GWAS) have lead to the identification of hundreds of genomic regions associated with complex diseases. Nevertheless, a large fraction of their heritability remains unexplained. Interaction between genetic variants is one of several putative explanations for the 'case of missing heritability' and, therefore, a compelling next analysis step. However, genome-wide interaction analysis (GWIA) of all pairs of SNPs from a standard marker panel is computationally unfeasible without massive parallelization. Furthermore, GWIA of all SNP triples is utopian. In order to overcome these computational constraints, we present a GWIA approach that selects combinations of SNPs for interaction analysis based on a priori information. Sources of information are statistical evidence (single marker association at a moderate level), genetic relevance (genomic location) and biologic relevance (SNP function class and pathway information). We introduce the software package INTERSNP that implements a logistic regression framework as well as log-linear models for joint analysis of multiple SNPs. Automatic handling of SNP annotation and pathways from the KEGG database is provided. In addition, Monte Carlo simulations to judge genome-wide significance are implemented. We introduce various meaningful GWIA strategies that can be conducted using INTERSNP. Typical examples are, for instance, the analysis of all pairs of non-synonymous SNPs, or, the analysis of all combinations of three SNPs that lie in a common pathway and that are among the top 50 000 single-marker results. We demonstrate the feasibility of these and other GWIA strategies by application to a GWAS dataset and discuss promising results.