Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Epistasis Analysis01:09

Epistasis Analysis

6.3K
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...
6.3K
Epistasis01:39

Epistasis

51.7K
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...
51.7K
Multiple Allele Traits01:49

Multiple Allele Traits

39.3K
The Concept of Multiple Allelism
39.3K
Multiple Allele Traits01:49

Multiple Allele Traits

15.2K
15.2K
Genetic Lingo01:11

Genetic Lingo

119.3K
Overview
119.3K
Pleiotropy01:33

Pleiotropy

44.4K
Pleiotropy is the phenomenon in which a single gene impacts multiple, seemingly unrelated phenotypic traits. For example, defects in the SOX10 gene cause Waardenburg Syndrome Type 4, or WS4, which can cause defects in pigmentation, hearing impairments, and an absence of intestinal contractions necessary for elimination. This diversity of phenotypes results from the expression pattern of SOX10 in early embryonic and fetal development. SOX10 is found in neural crest cells that form melanocytes,...
44.4K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Genetic parameters of growth and adaptive traits in aspen (Populus tremuloides): Implications for tree breeding in a warming world.

PloS one·2020
Same author

Potential of the C Genome of the Different Variants of <i>Brassica oleracea</i> for Heterosis in Spring <i>B. napus</i> Canola.

Frontiers in plant science·2020
Same author

Evaluation of <i>Brassica oleracea</i> accessions for resistance to <i>Plasmodiophora brassicae</i> and identification of genomic regions associated with resistance.

Genome·2019
Same author

Inferring defense-related gene families in Arabidopsis and wheat.

BMC genomics·2017
Same author

Field data analysis of active chlorine-containing stormwater samples.

Journal of environmental management·2017
Same author

Prediction and analysis of three gene families related to leaf rust (Puccinia triticina) resistance in wheat (Triticum aestivum L.).

BMC plant biology·2017

Related Experiment Video

Updated: Apr 20, 2026

In Vivo Modeling of the Morbid Human Genome using Danio rerio
12:31

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

21.6K

Direct approach to modeling epistasis.

Rong-Cai Yang1

  • 1Research and Innovation Division, Alberta Agriculture and Rural Development, Edmonton, AB, Canada, T6H 5T6, rong-cai.yang@ualberta.ca.

Methods in Molecular Biology (Clifton, N.J.)
|November 19, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for defining genetic effects, reversing the traditional approach by defining genetic effects as linear functions of genotypic values. This offers a more direct way to analyze quantitative trait loci (QTLs) in genetic studies.

More Related Videos

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients
07:34

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients

Published on: August 22, 2018

8.7K
Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays
14:06

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays

Published on: November 12, 2012

47.2K

Related Experiment Videos

Last Updated: Apr 20, 2026

In Vivo Modeling of the Morbid Human Genome using Danio rerio
12:31

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

21.6K
Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients
07:34

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients

Published on: August 22, 2018

8.7K
Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays
14:06

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays

Published on: November 12, 2012

47.2K

Area of Science:

  • Genetics
  • Quantitative Genetics
  • Bioinformatics

Background:

  • Genome-wide association studies (GWAS) identify quantitative trait loci (QTLs) for desired traits in various species.
  • Genetic effects, including epistasis, are traditionally defined indirectly using genotypic values.
  • Existing methods express genotypic values as linear functions of additive, dominance, and epistatic effects.

Purpose of the Study:

  • To propose a novel approach for defining genetic effects directly from genotypic values.
  • To offer a reversed perspective on the relationship between genetic effects and genotypic values.
  • To facilitate a more direct analysis of genetic architecture and gene action.

Main Methods:

  • Defining genetic effects as linear functions of genotypic values.
  • Directly defining functional genetic effects using established gene action models (e.g., unweighted, F2, F∞).
  • Directly defining statistical genetic effects based on Fisher's concept of average excess.

Main Results:

  • The proposed method allows for the direct definition of functional and statistical genetic effects.
  • The definitions are straightforward for common gene action models.
  • The approach can be extended to multiple independent loci.

Conclusions:

  • The direct definition of genetic effects offers a more intuitive and potentially powerful analytical framework.
  • This reversed approach simplifies the analysis of genetic architecture and QTL.
  • The method provides a foundation for more precise genetic selection strategies.