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GenNet framework: interpretable deep learning for predicting phenotypes from genetic data
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DOI：10.1038/s42003-021-02622-z.png)
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摘要
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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. 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.
Keyword:
GENOME-WIDE ASSOCIATION
SCHIZOPHRENIA
ENCYCLOPEDIA
ENRICHMENT
COLOR
HERC2
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期刊
IF:
5.1
论文数:
1.0W
被引数:
3.2W
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引用论文
ANNOVAR: functional annotation of genetic variants from high-throughput sequencing dataANNOVAR: 来自高通量测序数据的遗传变异的功能注释.png)
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OAAI分享收藏HERC2 rs12913832 modulates human pigmentation by attenuating chromatin-loop formation between a long-range enhancer and the OCA2 promoterGENOME RESEARCHOAAI
GENOME RESEARCHIF5.5
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