Related Experiment Video
Updated: May 11, 2026
10:18
A Fast and Reliable Pipeline for Bacterial Transcriptome Analysis Case study: Serine-dependent Gene Regulation in Streptococcus pneumoniae
Published on: April 25, 2015
10.8K
Finding Significant Hits in Networks: a network-based tool for analyzing gene-level P-values to identify significant
Sandeep Acharya1, Vaha Akbary Moghaddam2, Wooseok J Jung3
1Division of Computational and Data Sciences, Washington University, 1 Brookings Dr, St. Louis, MO 63130, United States.
Briefings in Bioinformatics
|March 8, 2026
Summary
Finding Significant Hits in Networks (FISHNET) identifies important genes missed by standard methods by analyzing biological networks and functions. This approach successfully uncovered novel gene-trait links for cardiovascular risk, demonstrating its power in genetic discovery.
Area of Science:
- Genomics
- Bioinformatics
- Systems Biology
Background:
- Genome-wide association studies (GWAS) often miss significant gene-trait associations due to stringent significance thresholds.
- Prior biological knowledge, such as gene networks and functions, can provide valuable context for interpreting genetic findings.
- Replication of genetic associations in independent cohorts is crucial for validating findings.
Purpose of the Study:
- To introduce Finding Significant Hits in Networks (FISHNET), a novel computational method for identifying biologically relevant genes that do not meet genome-wide significance thresholds.
- To leverage gene interaction networks and functional annotations to prioritize suggestive genetic associations.
- To demonstrate FISHNET's utility in discovering novel gene-trait relationships for complex diseases.
Main Methods:
- FISHNET integrates gene-level P-values from various sources (e.g., GWAS, omicsWAS, CRISPR screens) with prior biological knowledge.
- It analyzes gene interaction networks and functional annotations to identify clusters of suggestive genes within biological pathways.
- Permutation-based P-value thresholds are used to identify significant "FISHNET genes" that replicate despite not reaching genome-wide significance.
Main Results:
- Application of FISHNET to 11 cardiovascular risk traits identified 19 gene-trait relationships that replicated in an independent cohort, despite failing genome-wide significance.
- The replication rate of FISHNET-identified genes was comparable to that of genes with lower P-values.
- Novel associations were discovered, including RUNX1 with HDL and LTB with BMI, both supported by experimental evidence.
Conclusions:
- FISHNET effectively identifies biologically meaningful gene-trait associations that are often missed by traditional genome-wide significance thresholds.
- The method enhances genetic discovery by integrating network and functional information, leading to novel insights into complex traits.
- FISHNET provides a valuable tool for researchers seeking to uncover hidden genetic relationships and validate findings through replication.
Related Concept Videos
02:04Protein-protein Interfaces
Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a polypeptide...
02:26Protein Networks
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...