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.
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Install the RSS-NET software.
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Try RSS-NET on two synthetic datasets.
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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. (xiangzhu[at]psu.edu) and W.H.W (whwong[at]stanford.edu).
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Refer to RSS-NET wiki for tutorials and documentations.
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Create a new GitHub issue to report bugs and/or request features.
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Send an email to
xiangzhu[at]psu.edu.
Xiang Zhu
Wing Hung Wong Lab
Department of Statistics
Stanford University