GenNet framework: interpretable deep learning for predicting phenotypes from genetic data.

ABSTRACT: Applying deep learning in population genomics is challenging because of computational issues and lack of interpretable models. 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 biologically knowledge from public databases, resulting in neural networks that contain only biologically plausible connections. We applied the framework to seventeen phenotypes and found 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 identified ubiquitin mediated proteolysis, endocrine system and viral infectious diseases as most p

SUBMITTER: van Hilten A 

PROVIDER: S-EPMC8448759 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature