Discovering genetic interactions bridging pathways in genome-wide association studies
Discovering genetic interactions bridging pathways in genome-wide association studies
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DOI：
10.1038/s41467-019-12131-7
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发表时间：
2019-09-19
影响因子：
16.6
通讯作者：
Myers, Chad L.
中科院分区：
文献类型：
--
作者：
Fang, Gang;Wang, Wen;Myers, Chad L.
据报道，遗传相互作用是各种系统表型的基础，但它们在多大程度上导致人类复杂疾病仍不清楚。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，但现有的从GWAS数据中识别它们的方法往往侧重于测试单个位点对，这破坏了统计能力。重要的是，一个模型真核生物的全球遗传网络映射显示，遗传相互作用往往以高度一致的方式连接基因之间的补偿功能模块。利用这种预期的结构，我们开发了一种名为BridGE的计算方法，该方法可以从GWAS数据中识别由遗传相互作用连接的途径。广泛应用BridGE，我们发现帕金森病，精神分裂症，高血压，前列腺癌，乳腺癌和2型糖尿病中的显着相互作用。我们的新方法为从全基因组基因型数据映射人类疾病的复杂遗传网络提供了一个通用框架。
Genetic interactions have been reported to underlie phenotypes in a variety of systems, but the extent to which they contribute to complex disease in humans remains unclear. In principle, genome-wide association studies (GWAS) provide a platform for detecting genetic interactions, but existing methods for identifying them from GWAS data tend to focus on testing individual locus pairs, which undermines statistical power. Importantly, a global genetic network mapped for a model eukaryotic organism revealed that genetic interactions often connect genes between compensatory functional modules in a highly coherent manner. Taking advantage of this expected structure, we developed a computational approach called BridGE that identifies pathways connected by genetic interactions from GWAS data. Applying BridGE broadly, we discover significant interactions in Parkinson's disease, schizophrenia, hypertension, prostate cancer, breast cancer, and type 2 diabetes. Our novel approach provides a general framework for mapping complex genetic networks underlying human disease from genome-wide genotype data.