DeepGWAS

DeepGWAS to Enhance GWAS Signals for Neuropsychiatric Disorders via Deep Neural Network

In this work, we developed a 14-layer deep neural network, DeepGWAS, to enhance GWAS signals by leveraging GWAS summary statistics (p-value, odds ratio, minor allele frequency, linkage disequilibrium score), as well as brain-related functional genomic and epigenomic information (FIRE, super FIRE, open chromatin, eQTL).

DeepGWAS is maintained by Jia Wen [[email protected]] and Gang Li [[email protected]].

News and Updates

All notable changes to this project will be documented in this file.

Installation

Our DeepGWAS is tested with R 3.6.0 with the keras package. See our session info here.

• R 3.6.0
• tensorflow
• keras

Data Preparation

GWAS summary statistics are needed for enhancement. Functional annotations are also needed.

Enhancement

We provide our pre-trained model for users to enhance GWAS signals. Users can also use their own data to train a DeepGWAS network for prediction.

R CMD BATCH --no-save --no-restore '--args input_data=enhance.Rdata model=DeepGWAS_SCZ.h5 output_file="enhance.txt"' bin/03-DeepGWAS-enhance.R   

Training

R CMD BATCH --no-save --no-restore '--args input_data=train.Rdata output_file="DeepGWAS.model.h5"' bin/02-DeepGWAS-train.R

Citation

1. DeepGWAS ms
2. PMID: 35396580