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Related Concept Videos

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...
Polygenic Traits01:18

Polygenic Traits

When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...
Polygenic Traits01:18

Polygenic Traits

When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...
X-linked Traits01:19

X-linked Traits

In most mammalian species, females have two X sex chromosomes and males have an X and Y. As a result, mutations on the X chromosome in females may be masked by the presence of a normal allele on the second X. In contrast, a mutation on the X chromosome in males more often causes observable biological defects, as there is no normal X to compensate. Trait variations arising from mutations on the X chromosome are called “X-linked”.
Multiple Allele Traits01:49

Multiple Allele Traits

The Concept of Multiple Allelism
Multiple Allele Traits01:49

Multiple Allele Traits

The Concept of Multiple Allelism

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Related Experiment Video

Updated: Jun 9, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Mapping environment-specific quantitative trait loci.

Xin Chen1, Fuping Zhao, Shizhong Xu

  • 1Department of Statistics, University of California, Riverside, California 92521, USA.

Genetics
|September 1, 2010
PubMed
Summary

This study introduces a Bayesian method using Markov chain Monte Carlo (MCMC) to map quantitative trait loci (QTL) by environment (Q × E) interactions. The new approach effectively identifies main and interaction effects for complex traits like barley yield.

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Area of Science:

  • Genetics
  • Quantitative Genetics
  • Statistical Genomics

Background:

  • Quantitative trait loci (QTL) by environment (Q × E) interactions occur when QTL effects vary across different environments.
  • Traditional QTL mapping methods struggle to accurately analyze Q × E interactions.
  • Existing mixture model maximum-likelihood methods are suboptimal for handling QTL interacting with environments.

Purpose of the Study:

  • To develop and validate a novel statistical method for mapping Q × E interactions.
  • To partition QTL effects into distinct main and interaction components.
  • To improve the accuracy of QTL analysis in diverse environmental conditions.

Main Methods:

  • Developed a Bayesian method utilizing Markov chain Monte Carlo (MCMC) for QTL analysis.
  • Partitioned QTL effects into main effects (means across environments) and interaction effects (variances across environments).
  • Modeled the residual error covariance matrix using a robust factor analytic covariance structure.

Main Results:

  • A simulation study confirmed the robustness and flexibility of the factor analytic structure.
  • Applied the method to barley yield data, identifying 8 markers with significant main effects.
  • Detected 18 markers with significant Q × E interactions across all barley chromosomes, with most markers showing either main or interaction effects, but not both.

Conclusions:

  • The proposed Bayesian MCMC method effectively estimates both main and Q × E interaction effects.
  • The factor analytic covariance structure provides a robust framework for modeling complex genetic architectures.
  • This approach enhances the understanding of genotype-environment interactions in crop improvement.