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Genetic Variation

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Genetic variation is the diversity in DNA sequences found among individuals of the same species. This diversity is crucial for a species' survival because it helps organisms adapt to environmental changes. Genetic variation begins with fertilization, where an egg and sperm cell merge. Each of these cells carries 23 chromosomes, up to 46 in the fertilized egg. Chromosomes are long DNA strands that contain genes, the basic units of heredity.
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Updated: Jul 26, 2025

A Rapid and Efficient Method for Assessing Pathogenicity of Ustilago maydis on Maize and Teosinte Lines
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Diversifying maize genomic selection models.

Brian R Rice1, Alexander E Lipka1

  • 1Department of Crop Sciences, University of Illinois, Urbana, IL USA.

Molecular Breeding : New Strategies in Plant Improvement
|June 13, 2023
PubMed
Summary
This summary is machine-generated.

Genomic selection (GS) accelerates maize breeding by using genome-wide markers to predict genetic values. This review explores GS history, applications, and advanced models for faster, efficient genetic gains in maize.

Keywords:
GBLUPGenomic selectionHybrid predictionMaizeMulti-kernelOmics

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Area of Science:

  • Plant breeding
  • Genetics
  • Agricultural science

Background:

  • Genomic selection (GS) is a powerful tool in modern maize breeding.
  • It utilizes genome-wide marker data to estimate breeding values, enhancing genetic gains and reducing breeding cycles.

Purpose of the Study:

  • To review the history and milestones of genomic selection in maize breeding.
  • To discuss applications of GS in developing superior maize inbreds and hybrids.
  • To explore model refinements for improved prediction accuracy.

Main Methods:

  • Review of historical data and adaptation milestones of GS in maize.
  • Discussion of current and potential applications of GS in breeding programs.
  • Characterization of advanced GS models incorporating genetic effects and interactions.

Main Results:

  • GS significantly increases genetic gains and shortens breeding cycles in maize.
  • Refined GS models can account for non-additive genetic effects and genotype-by-environment interactions.
  • Integration of multi-omics data can further improve prediction accuracy.

Conclusions:

  • Genomic selection is crucial for efficient maize improvement.
  • Advanced GS models and data integration are key to maximizing genetic gains.
  • Strategic application of GS across breeding stages is recommended for optimal results.