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Power of mixed-model QTL mapping from phenotypic, pedigree and marker data in self-pollinated crops

Abstract

The power of QTL mapping by a mixed-model approach has been studied for hybrid crops but remains unknown in self-pollinated crops. Our objective was to evaluate the usefulness of mixed-model QTL mapping in the context of a breeding program for a self-pollinated crop. Specifically, we simulated a soybean (Glycine max L. Merr.) breeding program and applied a mixed-model approach that comprised three steps: variance component estimation, single-marker analyses, and multiple-marker analysis. Average power to detect QTL ranged from <1 to 47% depending on the significance level (0.01 or 0.0001), number of QTL (20 or 80), heritability of the trait (0.40 or 0.70), population size (600 or 1,200 inbreds), and number of markers (300 or 600). The corresponding false discovery rate ranged from 2 to 43%. Larger populations, higher heritability, and fewer QTL controlling the trait led to a substantial increase in power and to a reduction in the false discovery rate and bias. A stringent significance level reduced both the power and false discovery rate. There was greater power to detect major QTL than minor QTL. Power was higher and the false discovery rate was lower in hybrid crops than in self-pollinated crops. We conclude that mixed-model QTL mapping is useful for gene discovery in plant breeding programs of self-pollinated crops.

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Acknowledgments

The research was funded by the United States Department of Agriculture National Research Initiative Competitive Grants Program (Plant Genomics – Bioinformatics) and supported in part by the University of Minnesota Supercomputing Institute (UMSI). We thank Dr. Shuxia Zhang of UMSI for technical support.

Author information

Correspondence to R. Bernardo.

Additional information

Communicated by H. Becker

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Arbelbide, M., Yu, J. & Bernardo, R. Power of mixed-model QTL mapping from phenotypic, pedigree and marker data in self-pollinated crops. Theor Appl Genet 112, 876–884 (2006). https://doi.org/10.1007/s00122-005-0189-7

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Keywords

  • False Discovery Rate
  • Average Power
  • Recombinant Inbred
  • Best Linear Unbiased Prediction
  • Average Bias