Li Jing
Electrical Engineering and Computer Science Department, Case Western Reserve University, Cleveland, OH 44106, USA.
Int J Bioinform Res Appl. 2008;4(2):150-63. doi: 10.1504/IJBRA.2008.018342.
Large-scale Genome-Wide Association Studies (GWAS) for complex diseases are increasingly common, due to recent advances in genotyping technology. Gene-gene interactions play an important role in the etiology of complex diseases and have to be addressed in GWAS. In this paper, an efficient strategy based on two-stage analysis is proposed. It combines a single-locus approach with a Goodness-Of-Fit (GOF) test in stage one, and selects a promising subset of SNPs to be modelled using a full interaction model in stage two. Extensive simulations using different disease models with different levels of epistasis demonstrate that it achieves higher power than existing approaches.
由于基因分型技术的最新进展，针对复杂疾病的大规模全基因组关联研究（GWAS）越来越普遍。基因-基因相互作用在复杂疾病的病因学中起着重要作用，必须在GWAS中加以考虑。本文提出了一种基于两阶段分析的有效策略。它在第一阶段将单基因座方法与拟合优度（GOF）检验相结合，并在第二阶段选择一组有前景的单核苷酸多态性（SNP）子集，使用全交互模型进行建模。使用具有不同上位性水平的不同疾病模型进行的广泛模拟表明，该策略比现有方法具有更高的检验效能。
