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Epistasis

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...

Epistasis Analysis

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...

Background and Environment Affect Phenotype

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...

Genetic Screens

Genetic Screens

Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which...

Incomplete Dominance

Incomplete Dominance

Gregor Mendel's work (1822 - 1884) was primarily focused on pea plants. Through his initial experiments, he determined that every gene in a diploid cell has two variants called alleles inherited from each parent. He suggested that amongst these two alleles, one allele is dominant in character and the other recessive. The combination of alleles determines the phenotype of a gene in an organism.

In-vitro Mutagenesis

In-vitro Mutagenesis

To learn more about the function of a gene, researchers can observe what happens when the gene is inactivated or “knocked out,” by creating genetically engineered knockout animals. Knockout mice have been particularly useful as models for human diseases such as cancer, Parkinson’s disease, and diabetes.

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Related Experiment Video

Updated: Jul 4, 2025

In Vivo Modeling of the Morbid Human Genome using Danio rerio

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

In vivo functional phenotypes from a computational epistatic model of evolution.

Sophia Alvarez¹, Charisse M Nartey¹, Nicholas Mercado¹

¹Department of Biological Sciences, University of Texas at Dallas, Richardson, TX 75080.

Proceedings of the National Academy of Sciences of the United States of America

|January 29, 2024

Summary

This summary is machine-generated.

Computational models can now evolve proteins with enhanced functionality. Our new algorithm, SEEC, uses natural protein family data to create more active variants, advancing evolutionary biology and biomedical applications.

Keywords:

direct coupling analysis epistasis evolutionary dynamics sequence evolution sequence–fitness landscape

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Last Updated: Jul 4, 2025

In Vivo Modeling of the Morbid Human Genome using Danio rerio

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Published on: August 24, 2013

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In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

Published on: August 20, 2019

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

Area of Science:

Evolutionary biology
Computational biology
Biochemistry

Background:

Computational models of evolution are crucial for understanding sequence variation, phylogeny, and evolutionary pathways.
Validating the in vivo functionality of model outputs is essential for accurate evolutionary algorithms.
Epistasis, interactions between mutations, plays a key role in protein evolution.

Purpose of the Study:

To demonstrate the power of epistasis inferred from natural protein families to evolve functional protein variants.
To introduce and validate a novel algorithm, Sequence Evolution with Epistatic Contributions (SEEC).
To assess the in vivo functionality and activity of evolved protein variants.

Main Methods:

Developed the SEEC algorithm incorporating epistasis inferred from natural protein families.
Used the Hamiltonian of joint sequence probabilities as a fitness metric.
Experimentally tested in vivo beta-lactamase activity of evolved Escherichia coli TEM-1 variants.

Main Results:

SEEC evolved protein variants with dozens of mutations while preserving essential catalytic and interaction sites.
Evolved variants retained family-like functionality and exhibited higher activity than wild-type.
Different epistasis inference methods simulated diverse selection strengths, with weaker selection recapitulating neutral evolution.

Conclusions:

SEEC effectively evolves functional protein variants with enhanced activity using natural epistasis.
The algorithm can simulate different evolutionary dynamics, including neutral evolution.
SEEC holds potential for applications in neofunctionalization, viral fitness landscape characterization, and vaccine development.