Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, USA.
Graduate Program in Bioinformatics and Computational Biology (BICB), University of Minnesota, Minneapolis, MN, USA.
Nat Protoc. 2024 May;19(5):1400-1435. doi: 10.1038/s41596-024-00954-8. Epub 2024 Mar 21.
Genetic interactions have the potential to modulate phenotypes, including human disease. In principle, genome-wide association studies (GWAS) provide a platform for detecting genetic interactions; however, traditional methods for identifying them, which tend to focus on testing individual variant pairs, lack statistical power. In this protocol, we describe a novel computational approach, called Bridging Gene sets with Epistasis (BridGE), for discovering genetic interactions between biological pathways from GWAS data. We present a Python-based implementation of BridGE along with instructions for its application to a typical human GWAS cohort. The major stages include initial data processing and quality control, construction of a variant-level genetic interaction network, measurement of pathway-level genetic interactions, evaluation of statistical significance using sample permutations and generation of results in a standardized output format. The BridGE software pipeline includes options for running the analysis on multiple cores and multiple nodes for users who have access to computing clusters or a cloud computing environment. In a cluster computing environment with 10 nodes and 100 GB of memory per node, the method can be run in less than 24 h for typical human GWAS cohorts. Using BridGE requires knowledge of running Python programs and basic shell script programming experience.
遗传相互作用有可能调节表型，包括人类疾病。原则上，全基因组关联研究（GWAS）为检测遗传相互作用提供了一个平台；然而，传统的识别方法往往侧重于测试单个变异对，缺乏统计能力。在本方案中，我们描述了一种新的计算方法，称为基于上位性的基因集桥接（BridGE），用于从 GWAS 数据中发现生物途径之间的遗传相互作用。我们展示了一种基于 Python 的 BridGE 实现，并提供了将其应用于典型人类 GWAS 队列的说明。主要阶段包括初始数据处理和质量控制、构建变异水平遗传相互作用网络、使用样本置换测量途径水平遗传相互作用、使用样本置换评估统计显著性以及以标准化输出格式生成结果。BridGE 软件管道包括为有访问计算集群或云计算环境权限的用户提供在多个核和多个节点上运行分析的选项。在具有 10 个节点和每个节点 100GB 内存的集群计算环境中，对于典型的人类 GWAS 队列，该方法可以在不到 24 小时内运行。使用 BridGE 需要具备运行 Python 程序的知识和基本的 shell 脚本编程经验。
