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Polygenic Traits

Polygenic Traits

When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...

Protein Complex Assembly

Protein Complex Assembly

Multiple Allele Traits

Multiple Allele Traits

The Concept of Multiple Allelism

mRNA Stability and Gene Expression

mRNA Stability and Gene Expression

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.

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: Jan 5, 2026

Discrimintion and Mapping of the Primary and Processed Transcripts in Maize Mitochondrion Using a Circular RT-PCR-based Strategy

Discrimintion and Mapping of the Primary and Processed Transcripts in Maize Mitochondrion Using a Circular RT-PCR-based Strategy

Published on: July 29, 2019

Transcriptome-Based Prediction of Complex Traits in Maize.

Christina B Azodi^1,2, Jeremy Pardo^1,3, Robert VanBuren^3,4

¹Department of Plant Biology, Michigan State University, East Lansing, Michigan 48824.

|October 24, 2019

Summary

This summary is machine-generated.

Transcriptome data, like genetic markers, can predict plant traits. Combining both data types improves prediction accuracy, revealing genetic links beyond DNA sequence. This enhances genomic prediction and trait understanding.

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Related Experiment Videos

Last Updated: Jan 5, 2026

Discrimintion and Mapping of the Primary and Processed Transcripts in Maize Mitochondrion Using a Circular RT-PCR-based Strategy

Discrimintion and Mapping of the Primary and Processed Transcripts in Maize Mitochondrion Using a Circular RT-PCR-based Strategy

Published on: July 29, 2019

Scalable Transfection of Maize Mesophyll Protoplasts

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Published on: June 23, 2023

Obtaining High-Quality Transcriptome Data from Cereal Seeds by a Modified Method for Gene Expression Profiling

Obtaining High-Quality Transcriptome Data from Cereal Seeds by a Modified Method for Gene Expression Profiling

Published on: May 21, 2020

Area of Science:

Plant genetics
Genomics
Molecular biology

Background:

Genomic prediction uses genome-wide sequence data to predict complex traits and improve breeding.
Transcriptome data (gene expression levels) shows potential for genomic prediction, but its efficacy across different developmental stages is unclear.

Purpose of the Study:

To evaluate the predictive power of transcriptome data for mature plant traits using seedling data in maize (Zea mays).
To compare the performance of transcript-based models with traditional genetic marker-based models.
To investigate the synergy between transcriptomic and genomic data for enhanced trait prediction.

Main Methods:

Utilized maize genetic markers and seedling transcript levels to predict mature plant traits.
Developed separate genomic marker and transcriptome models.
Constructed a joint model incorporating important features from both individual models.
Analyzed the relationship between important genetic markers and regulatory variants of key transcripts.

Main Results:

Transcriptome and genetic marker models demonstrated comparable predictive performance for mature plant traits.
A joint model integrating top genetic markers and transcripts significantly improved prediction performance.
Important genetic markers for prediction were not physically or functionally linked to key transcripts.
Transcriptome models identified more flowering-time genes than genetic marker models.

Conclusions:

Transcript levels are valuable for predicting plant traits, offering complementary information to genetic markers.
The predictive power of transcript levels is not solely attributable to genetic variation within transcribed regions.
Transcriptome data provides insights into trait variation not captured by sequence-level information, enhancing genomic prediction and understanding of gene function.