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Related Concept Videos

Epistasis Analysis01:09

Epistasis 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...
Protein-protein Interfaces02:04

Protein-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...
Protein Networks02:26

Protein 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,...
Epistasis01:39

Epistasis

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...
Interactions Between Signaling Pathways01:19

Interactions Between Signaling Pathways

Signaling cascades usually lack linearity. Multiple pathways interact and regulate one another, allowing cells to integrate and respond to diverse environmental stimuli.
Convergence and divergence, and cross-talk between signaling pathways
Two distinct signaling pathways can converge on a single functional unit, which may either be a single protein or a complex of proteins. The response is either functionally distinct or synergistic between the two pathways but different from the response...
Pedigree Analysis01:35

Pedigree Analysis

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Related Experiment Video

Updated: Jul 6, 2026

Generalized Psychophysiological Interaction (PPI) Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease
09:38

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Published on: November 14, 2017

Polymorphism Interaction Analysis (PIA): a method for investigating complex gene-gene interactions.

Leah E Mechanic1, Brian T Luke, Julie E Goodman

  • 1Laboratory of Human Carcinogenesis, National Cancer Institute, NIH, Bethesda, MD, USA. mechanil@mail.nih.gov

BMC Bioinformatics
|March 8, 2008
PubMed
Summary
This summary is machine-generated.

The Polymorphism Interaction Analysis tool (PIA v. 2.0) effectively identifies complex gene interactions for disease risk. This software aids in pinpointing significant single nucleotide polymorphism (SNP) combinations, reducing false positives in genetic studies.

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Published on: July 18, 2013

Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Complex disease risk arises from gene-environment interactions, including single nucleotide polymorphisms (SNPs).
  • Traditional analysis methods struggle with high-dimensional genetic data, leading to inefficiencies and false positives.
  • High-throughput genotyping generates vast amounts of data, necessitating advanced analytical tools.

Purpose of the Study:

  • To introduce an improved tool, PIA v. 2.0, for analyzing complex genetic interactions.
  • To enhance the detection of gene-gene and gene-environment interactions.
  • To provide a robust method for identifying potential genetic associations in large datasets.

Main Methods:

  • Developed the Polymorphism Interaction Analysis tool (PIA version 2.0).
  • Incorporated novel approaches for ranking and scoring SNP combinations.
  • Enabled stratification on factors and analysis of user-defined pathways for case status association.

Main Results:

  • PIA v. 2.0 identified 2-SNP interactions as the top-ranked model 77% of the time in simulated data.
  • The tool successfully detected interacting SNPs in both balanced and imbalanced case-control datasets.
  • Analysis included simulated genetic models with minor allele frequency of 0.2 and heritability of 0.01.

Conclusions:

  • PIA v. 2.0 is valuable for exploring gene-gene and gene-environment interactions.
  • The tool helps identify a focused set of potential associations for further investigation.
  • Findings support the use of PIA v. 2.0 in genetic association studies and replication efforts.