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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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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.
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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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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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Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
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Exon Recombination02:32

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The evolution of new genes is critical for speciation. Exon recombination, also known as exon shuffling or domain shuffling, is an important means of new gene formation. It is observed across vertebrates, invertebrates, and in some plants such as potatoes and sunflowers. During exon recombination, exons from the same or different genes recombine and produce new exon-intron combinations, which might evolve into new genes. 
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Updated: Dec 5, 2025

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays
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Determining dependency and redundancy for identifying gene-gene interaction associated with complex disease.

Xiangdong Zhou1, Keith C C Chan2, Zhihua Huang1

  • 1College of Mathematics and Computer Science, Fuzhou University Fuzhou, Fujian 350108, P. R. China.

Journal of Bioinformatics and Computational Biology
|October 16, 2020
PubMed
Summary

This study introduces a new way to understand gene-gene interactions for complex diseases. The developed method uses a novel inequality to define and measure these interactions, improving disease prediction.

Keywords:
Complex diseasesgene–gene interactioninteraction groupmutual information

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Area of Science:

  • Genetics
  • Computational Biology
  • Bioinformatics

Background:

  • Interactions among genetic variants are crucial for complex disease prediction.
  • Existing computational methods for detecting gene-gene interactions can be improved with a deeper understanding of interaction properties.

Purpose of the Study:

  • To uncover patterns in gene-gene interactions.
  • To develop a novel, more effective method for detecting high-order gene-gene interactions.
  • To establish a new definition and measure for gene-gene interactions.

Main Methods:

  • Uncovered patterns in gene-gene interactions revealing a generalizable inequality.
  • Established a conditional independence and redundancy (CIR)-based definition of gene-gene interaction and interaction groups.
  • Derived a novel measure of gene-gene interaction based on the CIR definition.

Main Results:

  • Demonstrated a generalizable inequality for gene-gene interactions involving multiple genotype variables.
  • Introduced novel concepts of CIR-based gene-gene interaction and interaction groups.
  • Developed a novel algorithm for detecting high-order gene-gene interactions.

Conclusions:

  • The proposed CIR-based approach provides a promising new measure for gene-gene interactions.
  • The novel algorithm effectively detects high-order gene-gene interactions.
  • Experimental results on simulated and real data support the method's potential for complex disease prediction.