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Related Experiment Video

Updated: Aug 12, 2025

Author Spotlight: Streamlining Rice Breeding with CRISPR/Cas for Obtaining Optimal Phenotypic and Agronomic Traits

Author Spotlight: Streamlining Rice Breeding with CRISPR/Cas for Obtaining Optimal Phenotypic and Agronomic Traits

Published on: January 3, 2025

Lost genome segments associate with trait diversity during rice domestication.

Xiaoming Zheng^1,2,3, Limei Zhong⁴, Hongbo Pang⁵

¹National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.

|February 2, 2023

Summary

Structural variations (SVs) in rice genomes were analyzed using long-read sequencing, revealing their role in domestication and trait evolution. This study provides a comprehensive SV map for cultivated and wild rice, aiding future breeding efforts.

Keywords:

Association Domestication Oryza Phenotype Structure variation

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Published on: January 25, 2022

Area of Science:

Genomics
Evolutionary Biology
Plant Science

Background:

DNA mutations drive phenotypic variation and evolution.
Structural variations (SVs) significantly impact gene expression, plant phenotypes, and stress resistance.
Previous SV studies in rice were limited by short-read sequencing and lacked data for wild rice.

Purpose of the Study:

To generate genome assemblies for wild rice (O. rufipogon) using long-read sequencing.
To investigate the evolutionary patterns and effects of SVs on morphological traits during rice domestication.
To provide a comprehensive SV dataset for cultivated and wild rice.

Main Methods:

Generated two genome assemblies for O. rufipogon using long-read sequencing.
Analyzed 13 high-quality rice genomes to identify over 318,000 SVs.
Genotyped SVs in 649 rice accessions and performed genome-wide association studies.

Main Results:

Identified 318,589 SVs, with wild rice containing 49% unique SVs.
Found an average of 1.76% gene loss during rice domestication.
Associated 413 candidate causal variants with nine agronomic traits, including a deletion linked to grain length.

Conclusions:

Developed accurate and complete SV datasets for cultivated and wild rice, particularly in TE-rich regions.
Created an integrated rice SV map and identified candidate genes and variants.
These resources are valuable for future rice genomic research and breeding.