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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...

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Ultrasonic-Assisted Extraction of Cannabidiolic Acid from Cannabis Biomass

Ultrasonic-Assisted Extraction of Cannabidiolic Acid from Cannabis Biomass

Published on: May 27, 2022

Machine learning-enhanced multi-trait genomic prediction for optimizing cannabinoid profiles in cannabis.

Mohsen Yoosefzadeh Najafabadi¹, Davoud Torkamaneh^2,3,4,5

¹Department of Plant Agriculture, University of Guelph, Guelph, Ontario, Canada.

The Plant Journal : for Cell and Molecular Biology

|November 27, 2024

Summary

This summary is machine-generated.

Genomic selection (GS) and machine learning (ML) optimize cannabis breeding for targeted cannabinoid profiles. This study identifies key genetic loci for tetrahydrocannabinol (THC) and cannabidiol (CBD) development.

Keywords:

Cannabis sativa breeding strategies cannabinoid biosynthesis genomic selection machine learning multi trait genome prediction

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Non-Aqueous Isolation and Enrichment of Glandular Capitate Stalked and Sessile Trichomes from Cannabis sativa

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Published on: May 12, 2023

Area of Science:

Plant genetics
Cannabis biology
Agricultural science

Background:

Cannabis sativa L. possesses medicinal and psychoactive properties, driving market growth.
Fundamental cannabis biology is understudied due to historical research limitations.
Optimizing cannabinoid profiles is crucial for breeding desirable cannabis varieties.

Purpose of the Study:

To implement genomic selection (GS) and machine learning (ML) for optimizing cannabinoid profiles in cannabis breeding.
To identify genetic factors influencing major cannabinoid production, including THC and CBD.
To demonstrate the effectiveness of integrated GS and ML approaches in cannabis trait development.

Main Methods:

Analysis of a drug-type cannabis population with quantification of major cannabinoids.
High-density genotyping using 250,000 single nucleotide polymorphisms (SNPs) for GS.
Evaluation of various predictive models, including ML algorithms, statistical methods, and Bayesian approaches.

Main Results:

Random Forest models demonstrated superior predictive accuracy for single and multi-trait genomic predictions.
Accurate predictions were achieved for tetrahydrocannabinol (THC), cannabidiol (CBD), and their biosynthetic precursors.
Multi-trait analyses revealed complex genetic interdependencies and identified key loci involved in cannabinoid biosynthesis.

Conclusions:

The integration of GS and ML is effective for optimizing cannabinoid profiles in cannabis breeding.
This approach facilitates the development of cannabis varieties with precisely tailored cannabinoid content.
The study provides a foundation for advancing molecular-assisted breeding in Cannabis sativa L.