Planta

, Volume 234, Issue 2, pp 347–361

Mapping QTLs for improving grain yield using the USDA rice mini-core collection

  • Xiaobai Li
  • Wengui Yan
  • Hesham Agrama
  • Limeng Jia
  • Xihong Shen
  • Aaron Jackson
  • Karen Moldenhauer
  • Kathleen Yeater
  • Anna McClung
  • Dianxing Wu
Original Article

DOI: 10.1007/s00425-011-1405-0

Cite this article as:
Li, X., Yan, W., Agrama, H. et al. Planta (2011) 234: 347. doi:10.1007/s00425-011-1405-0

Abstract

Yield is the most important and complex trait for genetic improvement in crops, and marker-assisted selection enhances the improvement efficiency. The USDA rice mini-core collection derived from over 18,000 accessions of global origins is an ideal panel for association mapping. We phenotyped 203 O. sativa accessions for 14 agronomic traits and identified 5 that were highly and significantly correlated with grain yield per plant: plant height, plant weight, tillers, panicle length, and kernels/branch. Genotyping with 155 genome-wide molecular markers demonstrated 5 main cluster groups. Linkage disequilibrium (LD) decayed at least 20 cM and marker pairs with significant LD ranged from 4.64 to 6.06% in four main groups. Model comparisons revealed that different dimensions of principal component analysis affected yield and its correlated traits for mapping accuracy, and kinship did not improve the mapping in this collection. Thirty marker–trait associations were highly significant, 4 for yield, 3 for plant height, 6 for plant weight, 9 for tillers, 5 for panicle length and 3 for kernels/branch. Twenty-one markers contributed to the 30 associations, because 8 markers were co-associated with 2 or more traits. Allelic analysis of OSR13, RM471 and RM7003 for their co-associations with yield traits demonstrated that allele 126 bp of RM471 and 108 bp of RM7003 should receive greater attention, because they had the greatest positive effect on yield traits. Tagging the QTLs responsible for multiple yield traits may simultaneously help dissect the complex yield traits and elevate the efficiency to improve grain yield using marker-assisted selection in rice.

Keywords

Association mappingLinkage disequilibrium (LD)RiceGermplasmGrain yield

Abbreviations

ARO

Aromatic

AUS

Aus

BIC

Bayesian information criterion

GSOR

Genetic Stock Oryza

IND

Indica

LD

Linkage disequilibrium

NJ

Neighbor-Joining

PCA

Principal component analysis

PCR

Polymerase chain reaction

PIC

Polymorphic Information Content

QTL

Quantitative trait loci

R2

Squared allele frequency correlation estimates

SNP

Single nucleotide polymorphism

SSR

Simple sequence repeat

TEJ

Temperate japonica

TRJ

Tropical japonica

UPGMA

Unweighted pair-group method using arithmetic average

URMC

USDA rice mini-core collection

Supplementary material

425_2011_1405_MOESM1_ESM.doc (4.1 mb)
Supplementary Fig. 1 (DOC 4183 kb)
425_2011_1405_MOESM2_ESM.doc (94 kb)
Supplementary Table 1 (DOC 123 kb)
425_2011_1405_MOESM3_ESM.doc (123 kb)
Supplementary Table 2 (DOC 94 kb)

Copyright information

© Springer-Verlag (outside the USA) 2011

Authors and Affiliations

  • Xiaobai Li
    • 1
    • 2
    • 3
  • Wengui Yan
    • 3
  • Hesham Agrama
    • 2
  • Limeng Jia
    • 1
    • 2
    • 3
  • Xihong Shen
    • 4
  • Aaron Jackson
    • 3
  • Karen Moldenhauer
    • 2
  • Kathleen Yeater
    • 5
  • Anna McClung
    • 3
  • Dianxing Wu
    • 1
  1. 1.State Key Lab of Rice Biology, IAEA Collaborating CenterZhejiang UniversityHangzhouChina
  2. 2.Rice Research and Extension CenterUniversity of ArkansasStuttgartUSA
  3. 3.Dale Bumpers National Rice Research CenterUSDA-ARSStuttgartUSA
  4. 4.China National Rice Research InstituteHangzhouChina
  5. 5.Southern Plains AreaUSDA-ARSCollege StationUSA