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Related Concept Videos

Multiple Allele Traits01:49

Multiple Allele Traits

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Dihybrid Crosses

Overview
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...
Trihybrid Crosses02:27

Trihybrid Crosses

Trihybrid Crosses
Some of Mendel’s crosses examined three pairs of contrasting characteristics. Such a cross is called a trihybrid cross. A trihybrid cross is a combination of three individual monohybrid crosses. For example, plant height (tall vs. short), seed shape (round vs. wrinkled), and seed color (yellow vs. green).
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Related Experiment Video

Updated: Jul 1, 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

Mixed effects models for quantitative trait loci mapping with inbred strains.

Lara E Bauman1, Janet S Sinsheimer, Eric M Sobel

  • 1Department of Genetics, Southwest Foundation for Biomedical Research, San Antonio, Texas 78245-0549, USA. lbauman@sfbrgenetics.org

Genetics
|September 16, 2008
PubMed
Summary

This study introduces strain coefficients to enable random effect models for gene mapping in inbred strains. This approach overcomes limitations of traditional fixed effects models, improving quantitative trait locus (QTL) analysis.

Related Experiment Videos

Last Updated: Jul 1, 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

Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Fixed effects models are standard for genetic cross analysis but ignore polygenic background and require cross-specific tailoring.
  • Existing models for outbred populations are unsuitable for inbred strains due to genetic identity and homozygosity within strains.

Purpose of the Study:

  • To reexamine and enhance the application of random effect models in gene mapping for inbred strains.
  • To address the challenge of calculating trait covariances between relatives in inbred populations.
  • To reformulate quantitative trait locus (QTL) mapping and association analysis using mixed models.

Main Methods:

  • Introduction of novel combinatorial entities termed "strain coefficients" to bridge theoretical gaps.
  • Development of a theory to integrate fixed and random effects within mixed models for genetic analysis.
  • Application of the developed theory to simulated advanced intercross line (AIL) data, hormone data, and random mating among eight strains.

Main Results:

  • Demonstrated the capability of mixed effects models to incorporate polygenic background in gene mapping.
  • Successfully applied strain coefficients to calculate trait covariances in inbred crosses.
  • Validated the versatility of the mixed effects model approach across diverse genetic scenarios, including multivariate traits and complex mating designs.

Conclusions:

  • The proposed mixed effects model framework, utilizing strain coefficients, offers a more robust and versatile approach to gene mapping in inbred strains.
  • This methodology overcomes limitations of traditional fixed effects models, paving the way for more accurate QTL mapping and association studies.
  • The developed models are broadly applicable, handling complex genetic architectures and multivariate traits effectively.