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FISHNET: A Network-based Tool for Analyzing Gene-level P-values to Identify Significant Genes Missed by Standard Methods
Abstract FISHNET uses prior biological knowledge, represented as gene interaction networks and gene function annotations, to identify genes that do not meet the genome-wide significance threshold but replicate nonetheless. Its input is gene-level P-values from any source, including omicsWAS, aggregation of GWAS P-values, CRISPR screens, or differential expression analysis.
Construction of Multi-Modal Transcriptome-Small Molecule Interaction Networks from High-Throughput Measurements to Study Human Complex Traits
Abstract Small molecules (SMs) are integral to biological processes, influencing metabolism, homeostasis, and regulatory networks. Despite their importance, a significant knowledge gap exists regarding their downstream effects on biological pathways and gene expression, largely due to differences in scale, variability, and noise between untargeted metabolomics and sequencing-based technologies.
Novel Loci (EIF4A2, ADIPOQ, TPRG1) for Triglyceride / High-density Lipoprotein Cholesterol Ratio Longitudinal Change (ΔTHR) among Subjects without Type 2 Diabetes: Evidence from the Long Life Family Study (LLFS) and the Framingham Heart Study (FHS) Offspr
The authors have declared no competing interest. This research was supported by the National Institute on Aging of the National Institutes of Health (NIA/NIH) under U19AG063893. I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Washington University in Saint Louis IRBS approved this study.
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