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Related Experiment Video

Updated: Mar 30, 2026

Development of Targeting Induced Local Lesions IN Genomes TILLING Populations in Small Grain Crops by Ethyl Methanesulfonate Mutagenesis
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Sequencing-based high throughput mutation detection in bread wheat.

Gaganjot Sidhu1, Amita Mohan2, Ping Zheng3

  • 1Department of Crop and Soil Sciences, Washington State University, PO Box 646420, Pullman 99164-6420, WA, USA. gaganjot.sidhu@wsu.edu.

BMC Genomics
|November 19, 2015
PubMed
Summary

This study developed a sequencing method to detect mutations in wheat, differentiating induced changes from natural variations. This approach is valuable for large-scale mutation identification in crop gene function studies.

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Area of Science:

  • Plant genetics
  • Crop improvement
  • Molecular biology

Background:

  • Forward genetics is limited for complex agronomic traits in crops.
  • Reverse genetics is crucial for gene function studies in wheat.
  • A sequence-based resource is needed to detect induced mutations.

Purpose of the Study:

  • To assess the feasibility of a sequencing-based method for mutation detection in hexaploid wheat.
  • To differentiate induced mutations from natural homoeologous sequence variations.
  • To establish a mutation detection resource for wheat.

Main Methods:

  • Developed a reduced representation ApeKI library from Ethylmethane Sulfonate (EMS) induced wheat mutants.
  • Utilized individual barcode adapters for sequencing.
  • Created a novel bioinformatics pipeline to identify sequence variants against a wheat transcriptome reference.

Main Results:

  • Detected 14,130 mutational changes (SNPs and INDELs) and 150,511 homoeologous sequence changes.
  • Observed an average of 662 SNPs and 10 INDELs per mutant, with a mutation frequency of 1 per 5 Kb.
  • Found genes in distal chromosomal regions to be more susceptible to EMS mutagenesis.

Conclusions:

  • Sequencing-based mutation detection is a valuable and effective method for large-scale identification of induced mutations in wheat.
  • The developed bioinformatics pipeline accurately distinguishes induced mutations from homoeologous variations.
  • This approach supports gene function studies and crop breeding.