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Genome-wide Association Studies-GWAS01:11

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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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Related Experiment Video

Updated: Apr 15, 2026

Author Spotlight: Streamlining Rice Breeding with CRISPR/Cas for Obtaining Optimal Phenotypic and Agronomic Traits
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GWAS and Regularised Regression Identify SNPs Associated with Candidate Genes for Stage-Specific Salinity Tolerance

Sampathkumar Renukadevi Sruthi1,2, Zishan Ahmad3, Anket Sharma4

  • 1Genetics and Plant Breeding, Amrita School of Agricultural Sciences, Amrita Vishwa Vidyapeetham, Coimbatore 642109, India.

Plants (Basel, Switzerland)
|April 14, 2026
PubMed
Summary
This summary is machine-generated.

This study identifies key genetic factors for salt tolerance in rice, developing machine learning models to speed up breeding for improved crop yields under saline conditions. It highlights genotype 91-382 as a top performer for salt resilience.

Keywords:
GWASSNPphenotypingphysiologyplant stresspredictionricesalinity

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Area of Science:

  • Plant Science
  • Genetics
  • Agronomy

Background:

  • Soil salinity severely limits rice productivity, especially in early growth stages, due to osmotic and ionic stress.
  • Developing salt-tolerant rice varieties is crucial for global food security in arid and semi-arid regions.

Purpose of the Study:

  • To investigate stage-specific salinity tolerance mechanisms in rice using diverse genotypes.
  • To develop machine learning models for rapid phenotypic screening and identification of salt-tolerant rice.

Main Methods:

  • Evaluated 201 rice genotypes under control and saline conditions, measuring morphological and physiological traits.
  • Calculated Stress Tolerance Indices (STIs) and Average Membership Function Values (AMFV) for salinity response classification.
  • Performed Genome-Wide Association Analysis (GWAS) and applied regularized machine learning (LASSO, Elastic Net) for predictive modeling.

Main Results:

  • Identified 36 significant marker-trait associations, including novel SNPs and candidate genes related to ion transport and stress response.
  • Genotype 91-382 showed superior salt tolerance via enhanced Na+ exclusion, K+ retention, and biomass resilience.
  • Machine learning models achieved high predictive accuracy, revealing a developmental shift in tolerance mechanisms from germination to seedling stages.

Conclusions:

  • Integrated physiological indices, GWAS, and machine learning provide a robust framework for identifying elite salt-tolerant rice donors.
  • This approach accelerates the breeding process for developing salt-tolerant rice varieties crucial for sustainable agriculture.
  • The study identified specific genetic loci and favorable SNP combinations for enhancing rice salinity tolerance.