Population structure and association mapping on chromosome 7 using a diverse panel of Chinese germplasm of rice (Oryza sativa L.)

  • Weiwei Wen
  • , Hanwei Mei
  • , Fangjun Feng
  • , Sibin Yu
  • , Zhicheng Huang
  • , Jinhong Wu
  • , Liang Chen
  • , Xiaoyan Xu
  • , Lijun Luo

Research output: Contribution to journalArticlepeer-review

58 Scopus citations

Abstract

The majority of 170 rice accessions used in this study were diverse landraces or varieties from a putative mini-core collection of Chinese germplasm along with some widely used parental lines in genetic analysis or breeding (a few from abroad). The population was genotyped using 84 SSR or InDel markers on chromosome 7 and 48 markers on other chromosomes. The phenotyping of heading date, plant height and panicle length were carried out in different locations for 2 years. Based on morphological characterization, distance-based clustering and model-based estimation of marker data, the population showed a predominant structure with two subpopulations in correspondence with indica and japonica subspecies. The estimation of linkage disequilibrium in 2 Mb windows varied along chromosome 7 and showed parallel changes with inter-subspecies differentiation of marker loci (Fst). Based on the mixed linear model considering population structure and family relatedness [i.e. the (Q + K) model], one to three associated markers (P ≤ 0.0001) per trait per experiment were scanned out on rice chromosome 7. Most significant loci were repeated for the data from both field experiments while two loci were associated with two or three traits. Marker-based allelic effects were shown in a couple of associated markers as examples. The application of association results in breeding program was also discussed.

Original languageEnglish
Pages (from-to)459-470
Number of pages12
JournalTheoretical and Applied Genetics
Volume119
Issue number3
DOIs
StatePublished - 1 Aug 2009
Externally publishedYes

ASJC Scopus subject areas

  • Biotechnology
  • Agronomy and Crop Science
  • Genetics

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