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This is my online notebook to document and share the full results of
whole-genome integrated analyses of 38 gene regulatory networks and 18
human complex traits described in 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
If you find the analysis results, the pre-processed networks, the
statistical methods, and/or the open-source software useful for your
work, please kindly cite the research article listed above (Zhu et al,
2021).
If you have any question about the notebook and/or the article,
please feel free to contact me xiangzhu[at]psu.edu.
Main results
For each phenotype below, please click
to view network enrichment results, click
to view gene prioritization results, and click
to view gene cross-reference results. Please note that loading gene
prioritization results may take a while because of the large number of
genes displayed.
The network enrichment results
()
of each phenotype consist of RSS-NET enrichment Bayes factors of 38 inferred
gene regulatory networks and 1 near-gene control network for the
given trait, and enrichment \(P\)-values of LDSC and Pascal based on the
same networks and GWAS data.
The gene prioritization results
()
of each phenotype consist of RSS-NET posterior probabilities of
association (\(P_1\)) for all network
genes under the baseline (\(M_0\)) and
enrichment (\(M_1\)) models for the
given trait.
The gene cross-reference results
()
of each phenotype consist of external information from Therapeutic
Target Database (TTD), Mouse Genome Informatics (MGI) and Online
Mendelian Inheritance in Man (OMIM).