Deep learning is rarely used in population genomics because of the computational burden and challenges in interpreting neural networks. Here, we propose GenNet, a novel open-source deep learning framework for predicting phenotypes from genetic variants. In this framework, interpretable and memory-efficient neural network architectures are constructed by embedding biological knowledge from public databases, resulting in neural networks that contain only biological plausible connections. We applied the framework to seventeen phenotypes from a case-control study, a population-based study and the UK Biobank. Interpreting the networks revealed well-replicated genes such as HERC2 and OCA2 for hair and eye color and novel genes such as ZNF773 and PCNT for schizophrenia. Additionally, the framework obtained an AUC of 0.74 in the held-out test set and identified ubiquitin mediated proteolysis, endocrine system and viral infectious diseases as most predictive biological pathways for schizophrenia. GenNet is a freely available, end-to-end deep learning framework that allows researchers to develop and use interpretable neural networks to obtain novel insights into the genetic architecture of complex traits and diseases.

中文翻译：

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GenNet框架：用于表型预测的可解释神经网络

深度学习很少用于种群基因组学中，这是因为计算负担和解释神经网络所面临的挑战。在这里，我们提出GenNet，这是一种新颖的开源深度学习框架，用于从遗传变异中预测表型。在此框架中，通过嵌入公共数据库中的生物学知识来构建可解释且高效存储的神经网络体系结构，从而形成仅包含生物学上可能的联系的神经网络。我们从病例对照研究，基于人群的研究和UK Biobank将框架应用于17种表型。对网络的解释揭示了复制良好的基因，例如用于头发和眼睛颜色的HERC2和OCA2，以及用于精神分裂症的新基因，例如ZNF773和PCNT。此外，框架获得的AUC为0。74在保留的测试集中，确定了泛素介导的蛋白水解，内分泌系统和病毒感染性疾病是精神分裂症最可预测的生物学途径。GenNet是一个免费的，端到端的深度学习框架，允许研究人员开发和使用可解释的神经网络来获得对复杂性状和疾病遗传结构的新颖见解。