Association mapping of yield and its components in rice cultivars

H. A. Agrama*, G. C. Eizenga, W. Yan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

247 Citations (Scopus)

Abstract

To make advances in rice breeding it is important to understand the relatedness and ancestry of introduced rice accessions, and identify SSR markers associated with agronomically important phenotypic traits, for example yield. Ninety-two rice germplasm accessions recently introduced from seven geographic regions of Africa, Asia, and Latin America, and eleven US cultivars, included as checks, were evaluated for yield and kernel characteristics, and genotyped with 123 SSR markers. The SSR markers were highly polymorphic across all accessions. Population structure analysis identified eight main clusters for the accessions which corresponded to the major geographic regions, indicating agreement between genetic and predefined populations. Linkage disequilibrium (LD) patterns and distributions are of fundamental importance for genome-wide mapping association. LD between linked markers decreased with distance and with a substantial drop in LD decay values between 20 and 30 cM, suggesting it should be possible to achieve resolution down to the 25 cM level. For the 103 cultivars, the complex traits yield, kernel width, kernel length, kernel width/length ratio, and 1000-kernel weight, were estimated by analysis of variety trial data. The mixed linear model method was used to disclose marker-trait associations. Many of the associated markers were located in regions where QTL had previously been identified. In conclusion, association mapping in rice is a viable alternative to QTL mapping based on crosses between different lines.

Original languageEnglish
Pages (from-to)341-356
Number of pages16
JournalMolecular Breeding
Volume19
Issue number4
DOIs
Publication statusPublished - May 2007
Externally publishedYes

Keywords

  • Kinship coefficient
  • Linkage disequilibrium
  • Population structure
  • Relatedness
  • Unified mixed-model method

ASJC Scopus subject areas

  • Biotechnology
  • Molecular Biology
  • Agronomy and Crop Science
  • Genetics
  • Plant Science

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