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Related Concept Videos

Frequency-dependent Selection01:21

Frequency-dependent Selection

When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.Positive Frequency-Dependent SelectionIn positive...
Monohybrid Crosses01:20

Monohybrid Crosses

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Genetic Variation01:25

Genetic Variation

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.
Genes exist in different versions called alleles, which...
Dihybrid Crosses01:18

Dihybrid Crosses

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Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...

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Related Experiment Video

Updated: Jun 13, 2026

Breeding by Design for Functional Rice with Genome Editing Technologies
09:43

Breeding by Design for Functional Rice with Genome Editing Technologies

Published on: January 3, 2025

Missing comparability: When genomic selection faces field variability. A case study in soybeans.

Edmundo Caballero1, Julian Garcia-Abadillo1, Diego Jarquin1

  • 1Agronomy Department, University of Florida, Gainesville, Florida, USA.

The Plant Genome
|June 12, 2026
PubMed
Summary
This summary is machine-generated.

Processing phenotypic data for genomic selection (GS) models is crucial. Spatial models can improve breeding value prediction by isolating genetic signals, but benchmarks may be misleading.

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Last Updated: Jun 13, 2026

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09:43

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09:32

An Array-based Comparative Genomic Hybridization Platform for Efficient Detection of Copy Number Variations in Fast Neutron-induced Medicago truncatula Mutants

Published on: November 8, 2017

Area of Science:

  • Plant breeding
  • Quantitative genetics
  • Agricultural science

Background:

  • Phenotypic data processing is vital for genomic selection (GS) model training.
  • Existing methods often fail to fully separate genetic signals from field variability.
  • Statistical metrics for model comparison can be inconclusive due to unknown true breeding values.

Purpose of the Study:

  • To evaluate spatial models for separating genetic and field variability components.
  • To assess the impact of these models on genomic selection (GS) implementation.
  • To investigate the influence of field variability correction on breeding value prediction.

Main Methods:

  • Analysis of real soybean (Glycine max L. Merr.) data.
  • Simulation study under controlled conditions.
  • Implementation of three spatial models (M1: block, M2: block + row + column, M3: block + row + column + row × column).

Main Results:

  • Real data: Accounting for field variability reduced the predictive ability of isolated genetic signals.
  • Simulated data: Field variability corrections enhanced the predictive ability of breeding values.
  • The choice of spatial model impacts the effectiveness of genetic signal isolation.

Conclusions:

  • Training GS models with isolated genetic signals enhances breeding value predictability.
  • Standard benchmarks (correlation between predicted and observed values) can be misleading.
  • Accurate separation of genetic and field variability is essential for reliable GS.