The present repository contains source codes and documentations of RSS-NET, a novel Bayesian framework for simultaneous enrichment and prioritization analysis of complex trait GWAS and gene regulatory networks.

1. Install the RSS-NET software.

2. Try RSS-NET on two synthetic datasets.

3. Try RSS-NET on a real-world dataset.

If you find any part of this repository useful for your work, please kindly cite the following research article:

Zhu, X., Duren, Z. & Wong, W.H. Modeling regulatory network topology improves genome-wide analyses of complex human traits. Nat Commun 12, 2851 (2021). https://doi.org/10.1038/s41467-021-22588-0

We originally developed RSS-NET to integrate GWAS with gene regulatory networks, as implemented in rss_net.m. We recently extended RSS-NET to integrate GWAS with other genomic annotations such as sequence-conserved enhancers, and this extension is available as rss_gset.m. If you find this extension useful for your work, please kindly cite the following research article, in addition to the original RSS-NET publication.

Zhu, X., Ma, S. & Wong, W.H. Genetic effects of sequence-conserved enhancer-like elements on human complex traits. Genome Biol 25, 1 (2024). https://doi.org/10.1186/s13059-023-03142-1

Correspondence should be addressed to X.Z. and W.H.W.

1. Refer to RSS-NET wiki for tutorials and documentations.

2. Create a new GitHub issue to report bugs and/or request features.

Xiang Zhu, Ph.D.
Wing Hung Wong Lab
Department of Statistics
Stanford University