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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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Related Experiment Video

Updated: Nov 26, 2025

Multipronged Phenotyping Approaches to Characterize Sugarcane Root Systems

Multipronged Phenotyping Approaches to Characterize Sugarcane Root Systems

Published on: August 17, 2022

Soybean Root System Architecture Trait Study through Genotypic, Phenotypic, and Shape-Based Clusters.

Kevin G Falk¹, Talukder Zaki Jubery², Jamie A O'Rourke^1,3

¹Department of Agronomy, Iowa State University, Ames, Iowa, USA.

Plant Phenomics (Washington, D.C.)

|December 14, 2020

Summary

This summary is machine-generated.

This study examined soybean root system architecture (RSA) genetic diversity using advanced imaging and SNP data. Findings reveal limited diversity in U.S. soybean genotypes, highlighting opportunities for breeding programs to introduce new genetic variations.

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Last Updated: Nov 26, 2025

Multipronged Phenotyping Approaches to Characterize Sugarcane Root Systems

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Published on: August 17, 2022

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Published on: February 10, 2023

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Published on: October 22, 2018

Area of Science:

Agricultural Science
Plant Genetics
Bioinformatics

Background:

Root system architecture (RSA) traits are crucial for crop performance but challenging to phenotype due to scale, scope, and measurement variability.
Understanding genetic diversity in RSA is essential for targeted breeding and improving soybean yield and resilience.

Purpose of the Study:

To investigate the genetic diversity of root system architecture (RSA) traits in a large set of soybean accessions.
To develop an informative root (iRoot) categorization system for RSA traits.
To identify potential sources of genetic variation for RSA traits in soybean breeding.

Main Methods:

Phenotyped 292 soybean accessions for RSA traits using an imaging platform and 35,448 single nucleotide polymorphisms (SNPs).
Developed informative root (iRoot) categories based on root shape and morphology parameters.
Employed machine learning (convolutional neural networks, Fourier transformations) for shape-based clustering.
Integrated genetic and phenotypic analyses with mathematical models.

Main Results:

RSA traits exhibited significant genetic variability in root shape, length, number, mass, and angle.
Soybean accessions clustered into eight distinct genotype- and phenotype-based groups, correlating with geographical origins.
U.S. origin soybean genotypes showed limited genetic diversity for RSA traits.
Shape-based clustering enabled effective trait cataloging for breeding.

Conclusions:

Significant genetic variability exists for key soybean RSA traits.
Targeted breeding efforts using diverse accessions can enhance beneficial genetic variation for future gains.
The integration of advanced phenotyping, genetic analysis, and machine learning offers powerful tools for soybean improvement.