Department of Electrical Engineering & Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA.
Bioinformatics. 2011 Jul 1;27(13):i222-9. doi: 10.1093/bioinformatics/btr227.
Most complex diseases involve multiple genes and their interactions. Although genome-wide association studies (GWAS) have shown some success for identifying genetic variants underlying complex diseases, most existing studies are based on limited single-locus approaches, which detect single nucleotide polymorphisms (SNPs) essentially based on their marginal associations with phenotypes.
In this article, we propose an ensemble approach based on boosting to study gene-gene interactions. We extend the basic AdaBoost algorithm by incorporating an intuitive importance score based on Gini impurity to select candidate SNPs. Permutation tests are used to control the statistical significance. We have performed extensive simulation studies using three interaction models to evaluate the efficacy of our approach at realistic GWAS sizes, and have compared it with existing epistatic detection algorithms. Our results indicate that our approach is valid, efficient for GWAS and on disease models with epistasis has more power than existing programs.
大多数复杂疾病涉及多个基因及其相互作用。尽管全基因组关联研究 (GWAS) 已经显示出一些成功，可以识别复杂疾病背后的遗传变异，但大多数现有研究都是基于有限的单基因方法，这些方法主要基于与表型的边缘关联来检测单核苷酸多态性 (SNP)。
在本文中，我们提出了一种基于提升的基因-基因相互作用的集成方法。我们通过纳入基于基尼杂质的直观重要性得分来扩展基本的 AdaBoost 算法，以选择候选 SNP。我们使用置换检验来控制统计显著性。我们使用三种交互模型进行了广泛的模拟研究，以评估我们的方法在现实 GWAS 规模下的效果，并将其与现有的上位性检测算法进行了比较。我们的结果表明，我们的方法是有效的，在 GWAS 中效率高，并且在具有上位性的疾病模型中比现有的程序更有优势。
