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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...
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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

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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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Incomplete Dominance01:43

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Gregor Mendel's work (1822 - 1884) was primarily focused on pea plants. Through his initial experiments, he determined that every gene in a diploid cell has two variants called alleles inherited from each parent. He suggested that amongst these two alleles, one allele is dominant in character and the other recessive. The combination of alleles determines the phenotype of a gene in an organism.
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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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Human Genetics01:28

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Human genetics provides a profound framework for understanding the interplay between genetic predispositions and human psychology. At the heart of this discipline lies the study of how genes influence physical traits, behaviors, and susceptibility to diseases. Each person carries a unique genetic code that subtly or significantly shapes their psychological and behavioral landscape.
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Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
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Genomic Prediction Methods Accounting for Nonadditive Genetic Effects.

Luis Varona1,2, Andres Legarra3, Miguel A Toro4

  • 1Departamento de Anatomía, Embriología y Genética Animal, Universidad de Zaragoza, Zaragoza, Spain. lvarona@unizar.es.

Methods in Molecular Biology (Clifton, N.J.)
|April 22, 2022
PubMed
Summary

Genomic prediction models can improve accuracy by incorporating nonadditive genetic effects. This enhances prediction of future traits and optimizes breeding strategies in plants and animals.

Keywords:
CrossbreedingDominanceEpistasisGenetic evaluationGenomic predictionGenomic selection

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Area of Science:

  • Quantitative genetics
  • Animal breeding
  • Plant breeding

Background:

  • Genomic prediction (GP) is standard for predicting breeding values using additive genetic effects.
  • Nonadditive genetic variation (dominance, epistasis) is often ignored in current GP models.
  • Incorporating nonadditive effects can enhance prediction accuracy and breeding scheme efficiency.

Purpose of the Study:

  • To review methods for incorporating nonadditive genetic effects into genomic prediction.
  • To explore applications in predicting phenotypic performance and mate allocation.
  • To discuss benefits for crossbred and purebred breeding schemes.

Main Methods:

  • Literature review of existing genomic prediction methodologies.
  • Analysis of models accounting for dominance and epistasis.
  • Examination of applications in breeding program design.

Main Results:

  • Nonadditive effects can significantly improve prediction ability.
  • Models incorporating nonadditive variation offer advanced mate allocation strategies.
  • These methods can leverage genetic variation in crossbreeding and purebred selection.

Conclusions:

  • Integrating nonadditive genetic effects into genomic prediction is crucial for maximizing genetic gain.
  • Further research is needed to refine methods and explore novel applications.
  • Advanced genomic prediction models will drive innovation in animal and plant breeding.