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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...
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...
Background and Environment Affect Phenotype02:27

Background and Environment Affect Phenotype

Although the genetic makeup of an organism plays a major role in determining the phenotype, there are also several environmental factors, such as temperature, oxygen availability, presence of mutagens, that can alter an organism’s phenotype.
An example of how genetic background affects phenotype can be seen in horses. The Extension gene in horses is responsible for their coat color. A wild-type gene (EE) produces black pigment in the coat, while a mutant gene (ee) produces red pigment. A...
Multiple Allele Traits01:49

Multiple Allele Traits

The Concept of Multiple Allelism
Multiple Allele Traits01:49

Multiple Allele Traits

The Concept of Multiple Allelism
Pharmacogenetic Phenotypes: Alterations in Pharmacokinetics, Drug Targets and Biologic Milieu01:29

Pharmacogenetic 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...

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Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays
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Published on: November 12, 2012

Epistatic interaction maps relative to multiple metabolic phenotypes.

Evan S Snitkin1, Daniel Segrè

  • 1Program in Bioinformatics, Boston University, Boston, Massachusetts, United States of America.

Plos Genetics
|February 25, 2011
PubMed
Summary

Epistasis, where one gene

Area of Science:

  • Systems Biology
  • Metabolic Engineering
  • Genetics

Background:

  • Epistasis describes gene interactions where one gene's phenotypic effect depends on another, crucial for understanding cellular functions and diseases.
  • Research has primarily focused on epistasis affecting growth rate, neglecting its broader impact across diverse phenotypes.
  • The full extent and nature of epistasis across multiple cellular phenotypes remain largely unexplored.

Purpose of the Study:

  • To investigate the extent and properties of epistatic interactions across multiple metabolic phenotypes using genome-scale metabolic network modeling.
  • To analyze how epistasis influences various cellular functions beyond growth rate.
  • To identify genes with significant impacts across multiple phenotypes and their association with diseases.

Main Methods:

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  • Utilized an experimentally refined stoichiometric model of Saccharomyces cerevisiae.
  • Computed a three-dimensional matrix of epistatic interactions for all enzyme gene deletion pairs across metabolic flux phenotypes.
  • Analyzed gene expression, evolutionary rates, and disease associations for genes involved in multi-phenotype epistasis.

Main Results:

  • Epistatic interactions significantly increase with the number of phenotypes considered, showing an 8-fold higher connectivity than for growth rate alone.
  • Gene pairs exhibit incoherent interactions, acting antagonistically for some phenotypes and synergistically for others.
  • Genes with widespread interactions across phenotypes are highly expressed, evolve slowly, and are linked to diseases.

Conclusions:

  • Epistasis is pervasive across multiple metabolic phenotypes, revealing complex nonlinear effects of genetic perturbations.
  • Multi-phenotype epistasis provides a powerful framework for mapping cellular functions and understanding genetic disease mechanisms.
  • The total phenotypic impact of a gene, rather than isolated effects, determines its biological importance and disease relevance.