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Related Concept Videos

Epistasis Analysis01:09

Epistasis Analysis

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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...
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Epistasis01:39

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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...
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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.
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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...
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Signaling cascades usually lack linearity. Multiple pathways interact and regulate one another, allowing cells to integrate and respond to diverse environmental stimuli.
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Gene expression is a dynamic process that is significantly influenced by environmental factors. This interaction underlies the complex nature of biological development and the phenotypic differences observed among individuals, even among those with identical genetic makeups. Factors such as radiation, temperature, behavior, nutrition, and stress play pivotal roles in determining how genes are expressed. The concept of the reaction range is central to understanding this interaction. It posits...
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Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays
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CINOEDV: a co-information based method for detecting and visualizing n-order epistatic interactions.

Junliang Shang1,2, Yingxia Sun3, Jin-Xing Liu3,4

  • 1School of Information Science and Engineering, Qufu Normal University, Rizhao, 276826, China. shangjunliang110@163.com.

BMC Bioinformatics
|May 18, 2016
PubMed
Summary

This study introduces CINOEDV, a novel tool for detecting and visualizing complex genetic interactions beyond simple pairs. It helps uncover hidden clues in the genetic architecture of diseases.

Keywords:
Co-informationEpistatic interactionsHypergraphParticle swarm optimizationSingle nucleotide polymorphisms

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Area of Science:

  • Bioinformatics and computational biology
  • Genetics and genomics
  • Statistical genetics

Background:

  • Detecting nonlinear interaction effects of single nucleotide polymorphisms (SNPs), or epistatic interactions, is crucial for understanding complex diseases and the 'missing heritability' phenomenon.
  • Current research is largely limited to pairwise epistatic interactions due to significant methodological and computational challenges.
  • Advanced methods are needed to explore higher-order SNP interactions.

Purpose of the Study:

  • To develop a computational tool, CINOEDV (Co-Information based N-Order Epistasis Detector and Visualizer), for detecting and visualizing epistatic interactions of order n (n ≥ 2).
  • To enable a deeper understanding of the genetic architecture underlying complex diseases by analyzing higher-order SNP interactions.

Main Methods:

  • CINOEDV utilizes co-information based measures to quantify association effects of n-order SNP combinations with phenotypes.
  • It incorporates two search strategies for identifying n-order epistatic interactions: exhaustive search and particle swarm optimization.
  • A hypergraph is constructed to visualize detected n-order epistatic interactions, representing SNP main effects and interaction effects.

Main Results:

  • CINOEDV successfully detects and visualizes n-order epistatic interactions.
  • The hypergraph visualization aids in revealing potential insights into complex genetic architectures.
  • Experimental results on simulated and real-world data (age-related macular degeneration) demonstrate CINOEDV's effectiveness.

Conclusions:

  • CINOEDV is a promising tool for advancing the study of higher-order epistasis.
  • The method facilitates the detection and visualization of complex genetic interactions.
  • CINOEDV is implemented in R and publicly available, promoting wider research application.