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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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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.
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A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
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Related Experiment Video

Updated: Jan 13, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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GRE: A Framework for Significant SNP Identification Associated with Wheat Yield Leveraging GWAS-Random Forest Joint

Mei Song1, Shanghui Zhang1, Shijie Qiu1

  • 1School of Mathematics and Statistics, Ludong University, Yantai 264025, China.

Genes
|October 29, 2025
PubMed
Summary

Genomic selection (GS) models were improved using a novel framework (GRE) that combines GWAS and random forest for accurate wheat yield prediction. This approach enhances breeding efficiency and aids in identifying key genetic markers for sustainable agriculture.

Keywords:
GWASSHAPexplainable machine learninggenomic selectionrandom forestwheat yield

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Area of Science:

  • Agricultural Science
  • Genetics
  • Bioinformatics

Background:

  • Global wheat production faces challenges from environmental degradation and reduced arable land, necessitating breeding innovations for yield enhancement.
  • Genomic selection (GS) offers efficiency in wheat breeding by increasing genetic gain but is hindered by high-dimensional genomic data.
  • Accurate prediction of genomic estimated breeding values (GEBV) is crucial for effective breeding programs.

Purpose of the Study:

  • To develop an explainable machine learning framework (GRE) for genomic selection in wheat.
  • To integrate the biological significance of GWAS with the predictive power of RF for improved GS models.
  • To enhance the accuracy and interpretability of wheat yield trait prediction.

Main Methods:

  • Proposed GRE framework combining GWAS and Random Forest (RF) for SNP selection and analysis.
  • Evaluated six GS algorithms, including GBLUP and five machine learning models, using prediction accuracy (PCC) and error metrics.
  • Utilized Shapley Additive Explanations (SHAP) for model interpretability, revealing SNP effects on wheat yield.

Main Results:

  • XGBoost and ElasticNet models achieved high prediction accuracy (PCC > 0.864) and stability (SD < 0.005) using 383 SNPs identified by GRE.
  • SHAP analysis effectively explained the main and interaction effects of significant SNPs on wheat yield traits.
  • The study identified optimal SNP subsets and machine learning algorithms for efficient wheat breeding.

Conclusions:

  • The GRE framework provides a powerful, explainable tool for advancing genomic selection in wheat breeding.
  • This approach supports intelligent breeding chip design and the mining of important trait genes.
  • Findings contribute to the transformation of GS technology for sustainable global agricultural productivity.