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

Authors :
Arno van Hilten
Manfred Kayser
Gennady V. Roshchupkin
Wiro J. Niessen
Hieab H.H. Adams
Caroline C W Klaver
Steven A. Kushner
M. Arfan Ikram
Radiology & Nuclear Medicine
Psychiatry
Genetic Identification
Epidemiology
Clinical Genetics
Ophthalmology
Source :
Communications Biology, Vol 4, Iss 1, Pp 1-9 (2021), Communications Biology, 4, Communications Biology, 4(1), Communications Biology, 4, 1, Communications Biology, 4(1):1094. Springer Nature, Communications Biology
Publication Year :
2021
Publisher :
Nature Portfolio, 2021.

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 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.<br />van Hilten and colleagues present GenNet, a deep-learning framework for predicting phenotype from genetic data. This framework generates interpretable neural networks that provide insight into the genetic basis of complex traits and diseases.

Details

Language :
English
ISSN :
23993642
Volume :
4
Issue :
1
Database :
OpenAIRE
Journal :
Communications Biology
Accession number :
edsair.doi.dedup.....6bd94ac64c141e2a1af50cb4161eebc8