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

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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.

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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

R 2 :

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

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Authors and Affiliations

  1. State Key Lab of Rice Biology, IAEA Collaborating Center, Zhejiang University, Hangzhou, China

    Xiaobai Li, Limeng Jia & Dianxing Wu

  2. Rice Research and Extension Center, University of Arkansas, Stuttgart, AR, USA

    Xiaobai Li, Hesham Agrama, Limeng Jia & Karen Moldenhauer

  3. Dale Bumpers National Rice Research Center, USDA-ARS, Stuttgart, AR, USA

    Xiaobai Li, Wengui Yan, Limeng Jia, Aaron Jackson & Anna McClung

  4. China National Rice Research Institute, Hangzhou, China

    Xihong Shen

  5. Southern Plains Area, USDA-ARS, College Station, TX, USA

    Kathleen Yeater

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  1. Xiaobai Li
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  9. Anna McClung
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  10. Dianxing Wu
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Correspondence to Wengui Yan or Dianxing Wu.

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Li, X., Yan, W., Agrama, H. et al. Mapping QTLs for improving grain yield using the USDA rice mini-core collection. Planta 234, 347–361 (2011). https://doi.org/10.1007/s00425-011-1405-0

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  • DOI: https://doi.org/10.1007/s00425-011-1405-0

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