Bao Feng, Deng Yue, Du Mulong, Ren Zhiquan, Wan Sen, Liang Kenny Ye, Liu Shaohua, Wang Bo, Xin Junyi, Chen Feng, Christiani David C, Wang Meilin, Dai Qionghai
Department of Automation, Tsinghua University, Beijing 100084, China.
Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing 100084, China.
Patterns (N Y). 2020 Jul 1;1(6):100057. doi: 10.1016/j.patter.2020.100057. eCollection 2020 Sep 11.
The genetic effect explains the causality from genetic mutations to the development of complex diseases. Existing genome-wide association study (GWAS) approaches are always built under a linear assumption, restricting their generalization in dissecting complicated causality such as the recessive genetic effect. Therefore, a sophisticated and general GWAS model that can work with different types of genetic effects is highly desired. Here, we introduce a deep association kernel learning (DAK) model to enable automatic causal genotype encoding for GWAS at pathway level. DAK can detect both common and rare variants with complicated genetic effects where existing approaches fail. When applied to four real-world GWAS datasets including cancers and schizophrenia, our DAK discovered potential casual pathways, including the association between dilated cardiomyopathy pathway and schizophrenia.
基因效应解释了从基因突变到复杂疾病发生发展的因果关系。现有的全基因组关联研究（GWAS）方法总是在线性假设下构建，限制了它们在剖析诸如隐性基因效应等复杂因果关系方面的通用性。因此，非常需要一种能够处理不同类型基因效应的复杂通用GWAS模型。在此，我们引入了一种深度关联核学习（DAK）模型，以在通路水平上实现GWAS的自动因果基因型编码。DAK能够检测具有复杂基因效应的常见和罕见变异，而现有方法在这些方面则无能为力。当应用于包括癌症和精神分裂症在内的四个真实世界GWAS数据集时，我们的DAK发现了潜在的因果通路，包括扩张型心肌病通路与精神分裂症之间的关联。
