Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

14.4K
Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
14.4K
Multiple Regression01:25

Multiple Regression

3.2K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.2K
Plant Breeding and Biotechnology01:59

Plant Breeding and Biotechnology

19.8K
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.
19.8K
Light Acquisition02:16

Light Acquisition

8.6K
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.
8.6K
Dihybrid Crosses01:18

Dihybrid Crosses

76.3K
Overview
76.3K

You might also read

Related Articles

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

Sort by
Same author

Correction: Genome‑wide association study of soybean germplasm derived from modern Canadian and Chinese soybean cultivars to identify novel genes conferring soybean cyst nematode resistance.

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

Identification of Genomic Regions for Partial Resistance to Soybean Rust Under Field Conditions Using FarmCPU and Machine Learning Approaches.

Plants (Basel, Switzerland)·2026
Same author

Genetic architecture of phenological, morphological, and phytochemical traits in Cannabis landraces.

The plant genome·2026
Same author

QTL-seq analysis of seed protein quantity and quality traits in two soybean recombinant inbred line populations.

Frontiers in plant science·2026
Same author

Genome-wide association study of soybean germplasm derived from modern Canadian and Chinese soybean cultivars to identify novel genes conferring soybean cyst nematode resistance.

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

Sex-specific ethylene responses drive floral sexual plasticity in Cannabis sativa.

The Plant journal : for cell and molecular biology·2026

Related Experiment Video

Updated: Sep 21, 2025

Author Spotlight: Soybean Hairy Root Transformation for the Analysis of Gene Function
07:34

Author Spotlight: Soybean Hairy Root Transformation for the Analysis of Gene Function

Published on: May 5, 2023

4.0K

Machine-Learning-Based Genome-Wide Association Studies for Uncovering QTL Underlying Soybean Yield and Its

Mohsen Yoosefzadeh-Najafabadi1, Milad Eskandari1, Sepideh Torabi1

  • 1Department of Plant Agriculture, University of Guelph, Guelph, ON N1G 2W1, Canada.

International Journal of Molecular Sciences
|May 28, 2022
PubMed
Summary

Machine learning algorithms like support-vector machine (SVR) enhance genome-wide association studies (GWAS) for identifying marker-trait associations (MTAs) in soybean breeding. SVR-GWAS successfully identified MTAs for yield components, improving breeding efficiency.

Keywords:
FarmCPUMLMQTLdata-driven modelsgenome-wide association studysoybean breedingsupport-vector machine

More Related Videos

A Simple Method for Isolation of Soybean Protoplasts and Application to Transient Gene Expression Analyses
09:22

A Simple Method for Isolation of Soybean Protoplasts and Application to Transient Gene Expression Analyses

Published on: January 25, 2018

25.3K
Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

3.8K

Related Experiment Videos

Last Updated: Sep 21, 2025

Author Spotlight: Soybean Hairy Root Transformation for the Analysis of Gene Function
07:34

Author Spotlight: Soybean Hairy Root Transformation for the Analysis of Gene Function

Published on: May 5, 2023

4.0K
A Simple Method for Isolation of Soybean Protoplasts and Application to Transient Gene Expression Analyses
09:22

A Simple Method for Isolation of Soybean Protoplasts and Application to Transient Gene Expression Analyses

Published on: January 25, 2018

25.3K
Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

3.8K

Area of Science:

  • Plant genetics
  • Genomics
  • Agricultural science

Background:

  • Genome-wide association studies (GWAS) are crucial for identifying marker-trait associations (MTAs) in complex plant traits.
  • Conventional GWAS methods often lack statistical power, particularly in species with narrow genetic bases.
  • Machine learning (ML) offers potential solutions to enhance GWAS power and applicability in plant breeding.

Purpose of the Study:

  • To evaluate the efficacy of two ML algorithms, Support-Vector Machine (SVR) and Random Forest (RF), within a GWAS framework.
  • To compare the performance of ML-based GWAS with conventional methods like Mixed Linear Models (MLM) and FarmCPU.
  • To identify MTAs for key soybean yield components using advanced statistical approaches.

Main Methods:

  • A panel of 227 soybean genotypes was phenotyped for yield components (nodes, pods, yield, maturity) across four environments.
  • Two ML algorithms (SVR, RF) were applied to GWAS, alongside MLM and FarmCPU.
  • Marker-trait associations (MTAs) were identified and validated through colocalization with known quantitative trait loci (QTL) and candidate gene analysis.

Main Results:

  • The SVR-mediated GWAS successfully identified MTAs for soybean yield components.
  • Discovered MTAs colocalized with previously reported QTL, suggesting potential causal relationships.
  • Functional annotation of candidate genes provided further support for the identified MTAs.

Conclusions:

  • Sophisticated mathematical approaches, specifically SVR, can significantly enhance GWAS for identifying MTAs in soybean.
  • ML-based GWAS methods complement conventional approaches, offering increased statistical power and efficiency.
  • The findings support the integration of advanced ML techniques into genomic-based soybean breeding programs for accelerated genetic improvement.