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Related Concept Videos

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.
GWAS does not require the identification of the target gene involved in...
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Updated: Oct 24, 2025

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

Mustafa Bulut1, Alisdair R Fernie2, Saleh Alseekh3

  • 1Max-Planck-Institute of Molecular Plant Physiology.

Journal of Visualized Experiments : Jove
|August 16, 2021
PubMed
Summary

This study presents an optimized metabolic workflow for high-throughput analysis in legume crops, crucial for genome-wide association studies (GWAS). The method enhances sample preparation and reduces analytical variations for large-scale metabolomics.

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Area of Science:

  • Plant metabolomics
  • Analytical chemistry
  • Genomics

Background:

  • Gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) are standard metabolomics techniques.
  • Analyzing large sample sets, especially for genome-wide association studies (GWAS), presents significant challenges due to complex interactions and analytical variations.

Purpose of the Study:

  • To describe an optimized metabolic workflow for high-throughput analysis of legume crop species.
  • To facilitate the application of metabolomics in genome-wide association studies (GWAS).

Main Methods:

  • A modified extraction method using methyl tert-butyl ether: methanol solvent to capture both polar and lipid metabolites.
  • A step-by-step protocol for reducing analytical variations in large-scale sample analysis.
  • Application of gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS).

Main Results:

  • The protocol enables efficient and fast sample preparation for a large number of samples.
  • The method allows for the capture of a wide range of metabolites (polar and lipid).
  • Reduced analytical variations are achieved, essential for high-throughput metabolomics in GWAS.

Conclusions:

  • This optimized workflow addresses the challenges of large-scale metabolomics in legume crops.
  • The protocol is suitable for high-throughput evaluation of metabolic variance in genome-wide association studies (GWAS).
  • The method enhances the utility of GC-MS and LC-MS for complex biological studies.