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

Arno van Hilten*, Steven A. Kushner, Manfred Kayser, M. Arfan Ikram, Hieab H.H. Adams, Caroline C.W. Klaver, Wiro J. Niessen, Gennady V. Roshchupkin

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

17 Citations (Scopus)
134 Downloads (Pure)

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.

Original languageEnglish
Article number1094
Number of pages9
JournalCommunications Biology
Volume4
Issue number1
DOIs
Publication statusPublished - 17 Sept 2021

Bibliographical note

Funding Information:
We would like to thank Marloes Arts (University of Copenhagen) for her advice. This work was funded by the Dutch Technology Foundation (STW) through the 2005 Simon Steven Meester grant 2015 to W.J. Niessen. This research has been conducted using the UK Biobank Resource under Application Number 23509. This work was carried out on the Dutch national e-infrastructure with the support of SURF Cooperative (application number 17610). The Rotterdam study is supported by the Netherlands Organization for Scientific Research (NWO, 91203014, 175.010.2005.011, 91103012).

Publisher Copyright:
© 2021, The Author(s).

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