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Background and Environment Affect Phenotype02:27

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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.
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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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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Heritability is a statistical concept that measures the degree to which genetic differences among individuals contribute to trait variations within a population. It is a fundamental idea in genetics, often prone to misinterpretation. Heritability is expressed as a percentage, reflecting the proportion of variation in a specific trait across a population that can be linked to genetic differences. However, it's important to understand that heritability does not determine how "genetic"...
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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Exploring efficient linear mixed models to detect quantitative trait locus-by-environment interactions.

Eiji Yamamoto1,2, Hiroshi Matsunaga3

  • 1Graduate School of Agriculture, Meiji University, Kawasaki 214-8571, Japan.

G3 (Bethesda, Md.)
|April 19, 2021
PubMed
Summary
This summary is machine-generated.

Genotype-by-environment interactions improve quantitative trait locus detection in genome-wide association studies. A recommended linear mixed model enhances detection of genotype-by-environment and genotype-by-trial effects for robust genetic insights.

Keywords:
QTL-by-environment (Q×E) interactiongenome-wide association study (GWAS)genotype-by-environment (G×E) interactionlinear mixed model (LMM)quantitative trait locus (QTL)

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Area of Science:

  • Quantitative genetics
  • Genomic selection
  • Statistical genomics

Background:

  • Genotype-by-environment (G×E) interactions are crucial for understanding genotype-phenotype relationships.
  • Genomic selection (GS) and genome-wide association studies (GWAS) utilize statistical models to account for G×E effects, but their integration for GWAS efficacy is underexplored.

Purpose of the Study:

  • To comprehensively compare linear mixed models (LMMs) integrating G×E modeling methods for detecting quantitative trait loci (QTL) and QTL-by-environment (Q×E) interactions.
  • To evaluate the efficacy of different LMMs for G×E effect detection in GWAS.

Main Methods:

  • Simulation experiments were conducted to assess model performance.
  • LMMs were developed incorporating G×E and genotype-by-trial (G×T) effects as fixed and random terms.
  • The optimal LMM was applied to real tomato phenotype data from two distinct cropping seasons.

Main Results:

  • Simulations indicated that simultaneous scoring of specific and nonspecific environmental effects as fixed terms improved recall and genomic inflation factor.
  • Accounting for both G×E and G×T random effects was necessary to control genomic inflation.
  • The recommended LMM successfully identified QTLs with persistent effects and those exhibiting Q×E effects in tomato data.

Conclusions:

  • An optimized LMM, incorporating specific/nonspecific environmental effects and G×E/G×T random effects, is effective for detecting QTL and Q×E interactions in GWAS.
  • This approach enhances the understanding of genotype-phenotype relationships across different environments.