The genomic pattern of insertion/deletion variations during rice improvement

BMC Genomics. 2024 Dec 31;25(1):1263. doi: 10.1186/s12864-024-11178-1.

Abstract

Background: Rice, as one of the most important staple crops, its genetic improvement plays a crucial role in agricultural production and food security. Although extensive research has utilized single nucleotide polymorphisms (SNPs) data to explore the genetic basis of important agronomic traits in rice improvement, reports on the role of other types of variations, such as insertions and deletions (INDELs), are still limited.

Results: In this study, we extracted INDELs from resequencing data of 148 rice improved varieties. We identified 938,585 INDELs and found that as the length of the variation increases, the number of variations decreases, with 89.0% of INDELs being 2-10 bp. The highest number of INDELs was found on chromosome 1, while the least was on chromosome 10. INDELs were unevenly distributed across the genome, generating a total of 33 hotspot regions. 47.0% of INDELs were located within 2 kb upstream and downstream of genes. Using phenotypic data from five agronomic traits (heading date, flag leaf length, flag leaf width, panicle number, and plant height) along with INDEL data to perform genome-wide association study (GWAS), we identified 6,331 significant loci involving 157 cloned genes. Haplotype analysis of candidate genes revealed INDELs affecting important functional genes, such as OsMED25 and OsRRMh related to heading date, and MOC2 related to plant height.

Conclusions: Our work analyzed the variation patterns of INDELs in rice improvement and identified INDELs associated with agronomic traits. These results will provide valuable genetic and material resources for the genetic improvement of rice.

Keywords: Genomic pattern; Improvement of rice; Insertion and deletions.

MeSH terms

  • Genome, Plant*
  • Genome-Wide Association Study
  • Genomics / methods
  • Haplotypes
  • INDEL Mutation*
  • Oryza* / genetics
  • Oryza* / growth & development
  • Phenotype
  • Polymorphism, Single Nucleotide
  • Quantitative Trait Loci