Grid-based stochastic search for hierarchical gene-gene interactions in population-based genetic studies of common human diseases.
Grid-based stochastic search for hierarchical gene-gene interactions in population-based genetic studies of common human diseases.
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DOI：
10.1186/s13040-017-0139-3
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发表时间：
2017
期刊：
影响因子：
4.5
通讯作者：
Armentrout SL
中科院分区：
文献类型：
--
作者：
Moore JH;Andrews PC;Olson RS;Carlson SE;Larock CR;Bulhoes MJ;O'Connor JP;Greytak EM;Armentrout SL
对人类常见疾病的大规模遗传学研究几乎完全集中在单核苷酸多态性（SNP）对疾病易感性的独立主要影响上。这些研究取得了一些成功，但常见疾病的大部分遗传结构仍然无法解释。现在人们的注意力转向检测在其他遗传因素和环境暴露的背景下影响疾病易感性的SNP。这些背景依赖的遗传效应可以表现为非加性相互作用，这对使用参数统计方法建模更具挑战性。由于同时考虑许多SNP而导致的大量基因型组合的维度使得这些方法动力不足。我们以前开发了多因素降维（MDR）方法作为非参数和遗传无模型机器学习的替代方案。MDR等方法可以提高检测基因-基因相互作用的能力，但由于搜索空间的组合爆炸，它们在全基因组关联研究（GWAS）中彻底考虑SNP组合的能力有限。我们在这里介绍了一个随机搜索算法，称为粉碎MDR建模高阶基因间相互作用的全基因组数据的应用。Crush-MDR方法使用专家知识在一个框架内指导概率搜索，该框架利用生物学知识在分析之前过滤基因集。在这里，我们评估了Crush-MDR的能力，以检测层次集的相互作用的SNP使用基于生物学的模拟策略，假设非加性的基因内的相互作用和加性的基因组之间的遗传效应的生化途径。我们表明，Crush-MDR能够在基因或途径水平上识别遗传效应，显著优于具有相同数量模型评估的基线随机搜索。然后，我们将相同的方法应用于阿尔茨海默病的GWAS，并显示Crush-MDR能够识别一组与阿尔茨海默病有生物联系的相互作用基因的基础水平验证。我们讨论了随机搜索和云计算在全基因组数据中检测复杂遗传效应的作用。
Large-scale genetic studies of common human diseases have focused almost exclusively on the independent main effects of single-nucleotide polymorphisms (SNPs) on disease susceptibility. These studies have had some success, but much of the genetic architecture of common disease remains unexplained. Attention is now turning to detecting SNPs that impact disease susceptibility in the context of other genetic factors and environmental exposures. These context-dependent genetic effects can manifest themselves as non-additive interactions, which are more challenging to model using parametric statistical approaches. The dimensionality that results from a multitude of genotype combinations, which results from considering many SNPs simultaneously, renders these approaches underpowered. We previously developed the multifactor dimensionality reduction (MDR) approach as a nonparametric and genetic model-free machine learning alternative. Approaches such as MDR can improve the power to detect gene-gene interactions but are limited in their ability to exhaustively consider SNP combinations in genome-wide association studies (GWAS), due to the combinatorial explosion of the search space. We introduce here a stochastic search algorithm called Crush for the application of MDR to modeling high-order gene-gene interactions in genome-wide data. The Crush-MDR approach uses expert knowledge to guide probabilistic searches within a framework that capitalizes on the use of biological knowledge to filter gene sets prior to analysis. Here we evaluated the ability of Crush-MDR to detect hierarchical sets of interacting SNPs using a biology-based simulation strategy that assumes non-additive interactions within genes and additivity in genetic effects between sets of genes within a biochemical pathway. We show that Crush-MDR is able to identify genetic effects at the gene or pathway level significantly better than a baseline random search with the same number of model evaluations. We then applied the same methodology to a GWAS for Alzheimer’s disease and showed base level validation that Crush-MDR was able to identify a set of interacting genes with biological ties to Alzheimer’s disease. We discuss the role of stochastic search and cloud computing for detecting complex genetic effects in genome-wide data.
影响因子：
1.8
作者：
Sinnott-Armstrong NA;Greene CS;Cancare F;Moore JH
通讯作者：
Moore JH