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Identification of QTL for rice panicle length and grain weight using a doubled haploid population derived from 93–11 and Milyang352

  • Jiheon Han
  • , Seung Young Lee
  • , Yeeun Jun
  • , So Myeong Lee
  • , Gyu Hyeon Eom
  • , Jong Hee Lee
  • , Youngjun Mo*
  • *Corresponding author for this work
  • Jeonbuk National University
  • Rural Development Administration

Research output: Contribution to journalJournal articlepeer-review

Abstract

Enhancing productivity is a crucial objective in rice breeding programs. As genes for increased yield are present in indica and japonica subspecies distinctively, pyramiding favorable alleles from the two subspecies can increase yield effectively. In this study, 117 doubled haploid lines derived from the cross between 93–11 (indica) and Milyang352 (japonica) were evaluated for their yield-related traits under three different environments. QTL analysis with this population identified two and four stable QTLs for panicle length and thousand-grain weight, respectively. qPL3 and qPL7 increased panicle length with the 93–11 alleles, with significant pyramiding effects. For thousand grain weight, qTGW2, qTGW5, and qTGW10 increased the trait with the Milyang352 allele, while qTGW3 with the 93–11 allele, with beneficial pyramiding effects. In the long panicle background (qPL393−11 + qPL793−11), the pyramiding of MY352 alleles at qTGW2 and qTGW5 resulted in an increase in thousand-grain weight, while maintaining panicle length and grain number per panicle, indicating that using this QTL combination can be used for yield improvement. These findings provide valuable insights for developing high-yielding rice varieties and optimizing allele combinations for improved productivity.

Original languageEnglish
Article number964246
Pages (from-to)123-135
Number of pages13
JournalPlant Biotechnology Reports
Volume19
Issue number2
DOIs
StatePublished - 2025.04

Keywords

  • Doubled haploid
  • Panicle length
  • QTL
  • Rice
  • Thousand-grain weight

Quacquarelli Symonds(QS) Subject Topics

  • Agriculture & Forestry
  • Biological Sciences

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