Theoretical and Applied Genetics

, Volume 111, Issue 7, pp 1260–1270 | Cite as

Effect of population size on the estimation of QTL: a test using resistance to barley stripe rust

  • M. I. Vales
  • C. C. Schön
  • F. Capettini
  • X. M. Chen
  • A. E. Corey
  • D. E. Mather
  • C. C. Mundt
  • K. L. Richardson
  • J. S. Sandoval-Islas
  • H. F. Utz
  • P. M. Hayes
Original Paper


The limited population sizes used in many quantitative trait locus (QTL) detection experiments can lead to underestimation of QTL number, overestimation of QTL effects, and failure to quantify QTL interactions. We used the barley/barley stripe rust pathosystem to evaluate the effect of population size on the estimation of QTL parameters. We generated a large (n=409) population of doubled haploid lines derived from the cross of two inbred lines, BCD47 and Baronesse. This population was evaluated for barley stripe rust severity in the Toluca Valley, Mexico, and in Washington State, USA, under field conditions. BCD47 was the principal donor of resistance QTL alleles, but the susceptible parent also contributed some resistance alleles. The major QTL, located on the long arm of chromosome 4H, close to the Mlo gene, accounted for up to 34% of the phenotypic variance. Subpopulations of different sizes were generated using three methods—resampling, selective genotyping, and selective phenotyping—to evaluate the effect of population size on the estimation of QTL parameters. In all cases, the number of QTL detected increased with population size. QTL with large effects were detected even in small populations, but QTL with small effects were detected only by increasing population size. Selective genotyping and/or selective phenotyping approaches could be effective strategies for reducing the costs associated with conducting QTL analysis in large populations. The method of choice will depend on the relative costs of genotyping versus phenotyping.


Hordeum vulgare Puccinia striiformis f. sp. hordei Quantitative trait loci Selective genotyping Selective phenotyping Random sampling 



This work was supported in part by the USDA-NRI program (Plant-Microbe Interactions) and the North American Barley Genome Project (NABGP). We thank Tanya Filichkin, Jeanine DeNoma, Dr. Ariel Castro, and graduate and undergraduate students in the OSU barley program for their technical support in the lab and in the greenhouse. We wish to extend our thanks to Dr. Oscar Riera-Lizarazu for participating in helpful discussions.

Supplementary material

122_2005_43_MOESM1_ESM.pdf (41 kb)
Supplementary material


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Copyright information

© Springer-Verlag 2005

Authors and Affiliations

  • M. I. Vales
    • 1
  • C. C. Schön
    • 2
  • F. Capettini
    • 3
  • X. M. Chen
    • 4
  • A. E. Corey
    • 1
  • D. E. Mather
    • 5
  • C. C. Mundt
    • 6
  • K. L. Richardson
    • 1
  • J. S. Sandoval-Islas
    • 7
  • H. F. Utz
    • 8
  • P. M. Hayes
    • 1
  1. 1.Department of Crop and Soil ScienceOregon State UniversityCorvallisUSA
  2. 2.State Plant Breeding InstituteUniversity of HohenheimStuttgartGermany
  3. 3.ICARDA/CIMMYT Barley ProgramMexicoMexico
  4. 4.U.S. Department of Agriculture, Agricultural Research ServiceWashington State UniversityPullmanUSA
  5. 5.School of Agriculture and WineUniversity of Adelaide / Molecular Plant Breeding Cooperative Research CentreGlen OsmondAustralia
  6. 6.Department of Botany and Plant PathologyOregon State UniversityCorvallisUSA
  7. 7.Instituto de FitosanidadColegio de PostgraduadosMexicoMexico
  8. 8.Institute of Plant Breeding, Seed Science and Population GeneticsUniversity of HohenheimStuttgartGermany

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