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Plant Breeding and Biotechnology

Plant Breeding and Biotechnology

Crop cultivation has a long history in human civilization, with records showing the cultivation of cereal plants beginning at around 8000 BC. This early plant breeding was developed primarily to provide a steady supply of food.

Trihybrid Crosses

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).
The F1 generation plants of a trihybrid cross are heterozygous for all three traits and produce eight gametes. Upon self-fertilization, these gametes have an equal chance to...

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Related Experiment Video

Updated: May 12, 2026

Agrobacterium-Mediated Immature Embryo Transformation of Recalcitrant Maize Inbred Lines Using Morphogenic Genes

Agrobacterium-Mediated Immature Embryo Transformation of Recalcitrant Maize Inbred Lines Using Morphogenic Genes

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Genomic prediction in CIMMYT maize and wheat breeding programs.

J Crossa¹, P Pérez, J Hickey

¹Biometrics and Statistics Unit, International Maize and Wheat Improvement Center (CIMMYT), Mexico, Mexico.

|April 11, 2013

Summary

Genomic selection accelerates genetic gains in crops. Accurate predictions depend on factors like heritability and relatedness, with pedigree information crucial for global populations.

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Last Updated: May 12, 2026

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Published on: July 16, 2019

Area of Science:

Plant breeding
Quantitative genetics
Agricultural science

Background:

Genomic selection (GS) is a powerful tool for accelerating genetic gains in plants and animals.
Prediction accuracy in plant genomic selection varies based on heritability, marker density, sample size, and genotype × environment interactions (GE).

Purpose of the Study:

To evaluate genomic prediction results within the International Maize and Wheat Improvement Center's (CIMMYT's) maize and wheat breeding programs.
To assess the predictive ability of different models using pedigree and marker data for practical implementation of GS.

Main Methods:

Assessed predictive ability of models using pedigree and marker information.
Investigated the impact of population structure and relatedness on prediction accuracy.
Incorporated genotype × environment (GE) interactions into genomic prediction models.

Main Results:

Pedigree information significantly contributes to prediction accuracy in global populations.
Prediction accuracy diminishes when training and prediction populations are unrelated.
Modeling GE interactions enhances prediction accuracy by leveraging information from correlated environments.

Conclusions:

Genomic selection shows promise for accelerating genetic gains in maize and wheat breeding.
Further research is needed to optimize GS implementation, particularly for prediction within and between bi-parental crosses.
Quantifying breeding value components is essential for advancing GS in plant breeding populations.