Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

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.

Multiple Allele Traits

Multiple Allele Traits

The Concept of Multiple Allelism

Light Acquisition

Light Acquisition

In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.

Improving Translational Accuracy

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

Improving Translational Accuracy

Improving Translational Accuracy

Heritability

Heritability

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"...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

A dual genome-methylome map of clonal evolution in grapevine.

Genome biology·2026

Same author

Exploring standing genetic variation for barley leaf rust resistance in Australian breeding panel.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik·2026

Same author

Exploring intra-varietal variation for complex traits in grapevine (Vitis vinifera L.).

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik·2025

Same author

Integration of physiological and remote sensing traits for improved genomic prediction of wheat yield.

The plant genome·2025

Same author

A chickpea MAGIC population to dissect the genetics of complex traits.

The plant genome·2025

Same author

Haplotype-based insights into seminal root angle in barley.

The plant genome·2025

Search research articles

Related Experiment Video

Updated: Jan 11, 2026

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions

Published on: August 5, 2020

Accelerating perennial crop improvement via multi-omics-based predictive breeding.

Hannah Robinson¹, Carlos A Robles-Zazueta¹, Kai P Voss-Fels¹

¹Department of Plant Breeding, Hochschule Geisenheim University, Geisenheim, Germany.

The Plant Genome

|November 19, 2025

Summary

This summary is machine-generated.

Perennial crop breeding is slow, but multi-omics data can accelerate genetic improvement. Integrating diverse omics data offers a promising strategy for enhancing crop resilience and productivity in the face of environmental challenges.

More Related Videos

Author Spotlight: Streamlining Rice Breeding with CRISPR/Cas for Obtaining Optimal Phenotypic and Agronomic Traits

Author Spotlight: Streamlining Rice Breeding with CRISPR/Cas for Obtaining Optimal Phenotypic and Agronomic Traits

Published on: January 3, 2025

High-Throughput Analysis of Non-Photochemical Quenching in Crops Using Pulse Amplitude Modulated Chlorophyll Fluorometry

High-Throughput Analysis of Non-Photochemical Quenching in Crops Using Pulse Amplitude Modulated Chlorophyll Fluorometry

Published on: July 6, 2022

Related Experiment Videos

Last Updated: Jan 11, 2026

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions

Published on: August 5, 2020

Author Spotlight: Streamlining Rice Breeding with CRISPR/Cas for Obtaining Optimal Phenotypic and Agronomic Traits

Author Spotlight: Streamlining Rice Breeding with CRISPR/Cas for Obtaining Optimal Phenotypic and Agronomic Traits

Published on: January 3, 2025

High-Throughput Analysis of Non-Photochemical Quenching in Crops Using Pulse Amplitude Modulated Chlorophyll Fluorometry

High-Throughput Analysis of Non-Photochemical Quenching in Crops Using Pulse Amplitude Modulated Chlorophyll Fluorometry

Published on: July 6, 2022

Area of Science:

Agricultural Science
Plant Breeding
Genomics

Background:

Perennial crops face environmental challenges threatening productivity.
Slow breeding cycles in perennials hinder adaptation to climate change.
Annual crops utilize predictive breeding with multi-omics for accelerated genetic gains.

Purpose of the Study:

Assess current predictive breeding in perennials.
Compare with multi-omics frameworks in annual crops.
Outline considerations for implementing multi-omics in perennial breeding.

Main Methods:

Review of single-omic and emerging omics resources in perennials.
Comparison with established multi-omics prediction frameworks in annual crops.
Analysis of challenges and strategies for multi-omics integration.

Main Results:

Multi-omics approaches capture system features of regulatory networks.
Established multi-omics frameworks in annual crops enhance predictive ability.
Key considerations for perennials include end-to-end systems and modeling complex interactions.

Conclusions:

Multi-omics predictive breeding shows promise for rapid genetic improvement in perennials.
Addressing challenges like data dimensionality and GxE interactions is crucial.
Cross-institutional collaboration and simulation tools can optimize multi-omics integration.