Steffens Michael, Becker Tim, Sander Thomas, Fimmers Rolf, Herold Christine, Holler Daniela A, Leu Costin, Herms Stefan, Cichon Sven, Bohn Bastian, Gerstner Thomas, Griebel Michael, Nöthen Markus M, Wienker Thomas F, Baur Max P
Institute for Medical Biometry, Informatics and Epidemiology, University of Bonn, Bonn, Germany.
Hum Hered. 2010;69(4):268-84. doi: 10.1159/000295896. Epub 2010 Mar 31.
The Genome-Wide Association Study (GWAS) is the study design of choice for detecting common genetic risk factors for multifactorial diseases. The performance of full Genome-Wide Interaction Analyses (GWIA) has always been considered computationally challenging. Two-stage strategies to reduce the amount of numerical analysis require the detection of single marker effects or prior pathophysiological hypotheses before the analysis of interaction. This prevents the detection of pure epistatic effects. Our case-control study in idiopathic generalized epilepsy demonstrates that a full GWIA is feasible through use of data compression, specific data representation, interleaved data organization, and parallelization of the analysis on a multi-processor system. Following extensive quality control of the genotypes, our final list of top interaction hits contains only pairs of interacting SNPs with negligible marginal effects. The TOP HIT interaction was between a SNP-pair intragenic to gene DNER (chr 2) and gene CTNNA3 (chr 10). Both of these genes are functionally involved in neuronal migration, synaptogenesis, and the formation of neuronal circuits. Our results therefore indicate a possible interaction between these two genes in epileptogenesis. Results from GWAS are beginning to reveal a 'missing heritability' in complex traits and diseases. Systematic, hypothesis-free analysis of epistatic interaction (GWIA) may help to close this increasingly recognized gap in heritability.
全基因组关联研究(GWAS)是检测多因素疾病常见遗传风险因素的首选研究设计。全基因组相互作用分析(GWIA)的性能一直被认为在计算上具有挑战性。减少数值分析量的两阶段策略需要在相互作用分析之前检测单个标记效应或先前的病理生理假设。这会妨碍对纯上位性效应的检测。我们针对特发性全身性癫痫的病例对照研究表明,通过使用数据压缩、特定的数据表示、交错的数据组织以及在多处理器系统上进行分析并行化,全基因组相互作用分析是可行的。在对基因型进行广泛的质量控制之后,我们最终的顶级相互作用命中列表仅包含边际效应可忽略不计的相互作用单核苷酸多态性(SNP)对。顶级命中相互作用发生在基因DNER(2号染色体)和基因CTNNA3(10号染色体)的基因内SNP对之间。这两个基因在功能上均参与神经元迁移、突触形成以及神经回路的形成。因此,我们的结果表明这两个基因在癫痫发生过程中可能存在相互作用。全基因组关联研究的结果开始揭示复杂性状和疾病中“缺失的遗传力”。对上位性相互作用进行系统的、无假设分析(GWIA)可能有助于缩小这一日益被认识到的遗传力差距。