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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...
Multiple Allele Traits01:49

Multiple Allele Traits

The Concept of Multiple Allelism
Multiple Allele Traits01:49

Multiple Allele Traits

The Concept of Multiple Allelism
Behavioral Genetics and Its Designs01:23

Behavioral Genetics and Its Designs

Behavior genetics explores how genetic inheritance influences human behavior. It focuses on how genes, passed from parents to offspring, contribute to the development of behavioral traits and tendencies. This branch of genetics seeks to understand the complex interplay between inherited genetic factors and environmental influences in shaping our behaviors.
The primary methodologies used in behavior genetics include family studies, twin studies, and adoption studies, each providing unique...
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...
Gene-Environment Interactions01:20

Gene-Environment Interactions

Gene expression is a dynamic process that is significantly influenced by environmental factors. This interaction underlies the complex nature of biological development and the phenotypic differences observed among individuals, even among those with identical genetic makeups. Factors such as radiation, temperature, behavior, nutrition, and stress play pivotal roles in determining how genes are expressed. The concept of the reaction range is central to understanding this interaction. It posits...

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Related Experiment Video

Updated: Jun 12, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Nonparametric Bayesian variable selection with applications to multiple quantitative trait loci mapping with

Fei Zou1, Hanwen Huang, Seunggeun Lee

  • 1Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina 27599, USA. fzou@bios.unc.edu

Genetics
|June 17, 2010
PubMed
Summary

Mapping genes for complex traits is challenging. This study introduces a new nonparametric Bayesian method to identify multiple quantitative trait loci (QTL), including gene interactions and environmental effects, improving genetic analysis.

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Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
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Last Updated: Jun 12, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

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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

Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Complex traits and human diseases arise from the interplay of multiple genes.
  • Identifying genes with epistatic effects and gene-environment interactions is difficult due to small sample sizes and large parameter spaces in traditional quantitative trait locus (QTL) models.
  • Existing parametric QTL analyses often focus on pairwise interactions, limiting the scope of detected genetic influences.

Purpose of the Study:

  • To develop a novel nonparametric Bayesian method for mapping multiple quantitative trait loci (QTL).
  • To account for complex genetic architectures, including epistasis and gene-environment interactions, beyond pairwise effects.
  • To provide a flexible framework that incorporates both genetic and nongenetic factors in trait variation analysis.

Main Methods:

  • A nonparametric Bayesian approach using Gaussian process priors to model an unspecified function of genotypes at all candidate QTL.
  • The method measures the importance of each QTL through hyperparameters, capturing main and interaction effects without explicit term modeling.
  • Inclusion of nongenetic factors and covariates (e.g., age, gender, environment) within the same functional framework.

Main Results:

  • The proposed method effectively measures the importance of each QTL, regardless of whether its effect is primarily from a main effect or an interaction.
  • It can capture complex interactions among multiple genes and between genes and environmental factors.
  • Initial evaluations using simulated and real data demonstrate the method's performance.

Conclusions:

  • The nonparametric Bayesian method offers a powerful and flexible approach for mapping multiple QTL with epistatic and gene-environment interactions.
  • This method overcomes limitations of traditional parametric models by not restricting interactions to pairwise effects.
  • It provides a unified framework for estimating the contribution of genetic and nongenetic factors to complex trait variation.