E-GADGETS：用病例-父母三联体检测高阶基因-环境交互的遗传算法
A genetic algorithm for detecting higher-order gene-by-environment interactions using case-parent triads
研究团队开发了遗传算法 E-GADGETS，可在病例-父母三联体数据中搜索多个 SNP 与非遗传暴露因素之间的高阶交互（G×G×E）。模拟显示，在 1 万或 5 万个候选 SNP 中，E-GADGETS 能识别涉及 3 或 4 个 SNP 及分类或连续暴露的 G×G×E 交互，并优于将现有三联体上位性挖掘算法改造后用于二分类暴露和小候选 SNP 集的方案。作者还给出基于置换的推断流程和结果可视化方法。应用于公开病例-父母数据的候选 SNP 时，E-GADGETS 在亚洲人群中检出与母亲环境烟草烟雾暴露相关的交互信号，关联表型为单纯腭裂。
来源：Briefings in Bioinformatics · doi.org
We developed a genetic algorithm, called Exposure-related Genetic Algorithm for Detecting Genetic Epistasis using Triads and Siblings (E-GADGETS), to search for risk-associated interactions among multiple single-nucleotide polymorphisms (SNPs) and non-genetic “exposures” (G×G×E interactions) using case-parent triads. In E-GADGETS, a non-genetic “exposure” may comprise factors ranging from health-related behaviors and environmental agents to disease subcategories. We demonstrate through simulation that, when searching 10 000 or 50 000 candidate SNPs, E-GADGETS identifies G×G×E interactions involving three or four SNPs and categorical or continuous exposures. We also describe how to adapt existing triad-based epistasis-mining algorithms to search for G×G×E effects provided that E is binary and the set of candidate SNPs is small enough, but E-GADGETS outperforms those methods. We further describe permutation-based inferential procedures and a visualization approach for results. When applied to candidate SNPs from publicly available case-parent data, E-GADGETS detected evidence of genetic interactions with maternal exposure to environmental tobacco smoke associated with cleft-palate-only in Asians.