Related Experiment Video
Updated: May 29, 2026
14:06
Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays
Published on: November 12, 2012
Characterizing genetic interactions in human disease association studies using statistical epistasis networks
Ting Hu1, Nicholas A Sinnott-Armstrong, Jeff W Kiralis
1Department of Genetics, Dartmouth Medical School, Dartmouth College, Lebanon, NH, USA.
BMC Bioinformatics
|September 14, 2011
Summary
Network science reveals widespread gene interactions in bladder cancer. This approach identifies complex genetic architectures beyond single gene effects, offering new insights into disease susceptibility.
Area of Science:
- Genetics
- Network Science
- Cancer Research
Background:
- Epistasis, or gene interactions, is crucial for complex traits like disease susceptibility.
- Previous studies examined limited genetic variants, overlooking broader interaction landscapes.
- Network science offers a novel approach to explore extensive gene-gene interactions.
Purpose of the Study:
- To apply network science to map global pairwise interactions among Single Nucleotide Polymorphisms (SNPs).
- To investigate the extent of non-additive genetic effects beyond small variant subsets.
- To characterize the genetic architecture of bladder cancer using a population-based dataset.
Main Methods:
- Inferred statistical epistasis networks to identify significant pairwise SNP interactions.
- Linked SNPs based on interaction strength exceeding a defined threshold.
- Analyzed network topology, including hub SNPs and connected components, comparing real data to permuted null models.
Main Results:
- The real-data epistasis network exhibited distinct topology compared to null models.
- Identified significantly more hub SNPs, not necessarily those with high main effects.
- Discovered a large connected component of 39 SNPs, absent in permuted networks, suggesting scale-free properties.
Conclusions:
- The network approach provides a global view of gene-gene interactions in bladder cancer.
- Distinct network topology and large connected components highlight joint effects across numerous SNPs.
- This statistical epistasis network reveals previously undescribed features of bladder cancer's genetic architecture.
Related Concept Videos
01:09Epistasis Analysis
Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
01:39Epistasis
In addition to multiple alleles at the same locus influencing traits, numerous genes or alleles at different locations may interact and influence phenotypes in a phenomenon called epistasis. For example, rabbit fur can be black or brown depending on whether the animal is homozygous dominant or heterozygous at a TYRP1 locus. However, if the rabbit is also homozygous recessive at a locus on the tyrosinase gene (TYR), it will have an unshaded coat that appears white, regardless of its TYRP1...
01:11Genome-wide Association Studies-GWAS
Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
GWAS does not require the identification of the target gene involved in...
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,...
01:29Pharmacogenomics: Identification of New Drug Targets
Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
01:29Pharmacogenetic Phenotypes: Alterations in Pharmacokinetics, Drug Targets and Biologic Milieu
Genetic variations significantly influence drug response through pharmacokinetics, receptor interactions, and biologic milieu modifications. Pharmacokinetic alterations impact drug metabolism and clearance, affecting efficacy and toxicity. Variants in drug-metabolizing enzymes, such as CYP2C9 and CYP2C19, alter drug activation and elimination. For example, CYP2C9 loss-of-function variants require lower warfarin doses to prevent excessive bleeding, while CYP2C19 variants reduce clopidogrel...