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The analysis of QTL by simultaneous use of the full linkage map

  • Arūnas P. VerbylaEmail author
  • Brian R. Cullis
  • Robin Thompson
Original Paper

Abstract

An extension of interval mapping is presented that incorporates all intervals on the linkage map simultaneously. The approach uses a working model in which the sizes of putative QTL for all intervals across the genome are random effects. An outlier detection method is used to screen for possible QTL. Selected QTL are subsequently fitted as fixed effects. This screening and selection approach is repeated until the variance component for QTL sizes is not statistically significant. A comprehensive simulation study is conducted in which map uncertainty is included. The proposed method is shown to be superior to composite interval mapping in terms of power of detection of QTL. There is an increase in the rate of false positive QTL detected when using the new approach, but this rate decreases as the population size increases. The new approach is much simpler computationally. The analysis of flour milling yield in a doubled haploid population illustrates the improved power of detection of QTL using the approach, and also shows how vital it is to allow for sources of non-genetic variation in the analysis.

Keywords

False Discovery Rate Broman Interval Mapping Composite Interval Mapping Genetic Line 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

We gratefully acknowledge the Grains Research and Development Corporation (GRDC) for support through Key Programme 3 of their National Statistics Project. We thank the Australian Winter Cereals Molecular Marker Program and it’s predecessor the National Wheat Molecular Marker Program, both funded by GRDC, for the flour milling yield data analysed in this paper. We are grateful to Simon Diffey, New South Wales Department of Primary Industries, for his excellent implementation of the approach using R and the qtl package. Lastly, we thank the Associate Editor and the referees whose comments have led to substantial improvements and clarifications being incorporated into the paper.

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Copyright information

© Springer-Verlag 2007

Authors and Affiliations

  • Arūnas P. Verbyla
    • 1
    • 2
    Email author
  • Brian R. Cullis
    • 3
  • Robin Thompson
    • 4
    • 5
  1. 1.School of Agriculture, Food and WineThe University of AdelaideGlen OsmondAustralia
  2. 2.Statistical Bioinformatics—Agribusiness, Mathematical and Information SciencesCSIROGlen OsmondAustralia
  3. 3.Biometrics, Wagga Wagga Research InstituteNew South Wales Department of Primary IndustriesWagga WaggaAustralia
  4. 4.School of Mathematical Sciences, Queen Mary CollegeUniversity of LondonLondonUK
  5. 5.Centre for Mathematical and Computational Biology, Department of Biomathematics and BioinformaticsRothamsted ResearchHarpendenUK

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