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


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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  1. Arbelbide M, Bernardo R (2005) Mixed-model QTL mapping for kernel hardness and dough strength in bread wheat. Theor Appl Genet (DOI 10.1007/s00122-005-0190-1)

  2. Beavis WD (1994) The power and deceit of QTL experiments: lessons from comparative QTL studies. Proc Corn Sorghum Ind Res Conf 49:250–266

  3. Beavis WD (1998) QTL analyses: power, precision, and accuracy. In: Paterson AH (ed) Molecular dissection of complex traits. CRC Press, Boca Raton , pp 145–162

  4. Bernardo R (2004) What proportion of declared QTL in plants is false? Theor Appl Genet 109:419–424

  5. Crepieux S, Lebreton C, Servin B, Charmet G (2004) Quantitative trait loci (QTL) detection in multicross inbred designs: recovering QTL identical-by descent status information from marker data. Genetics 168:1737–1749

  6. Crepieux S, Lebreton C, Flament P, Charmet G (2005) Application of a new IBD-based mapping method to common wheat breeding population: analysis of kernel hardness and dough strength. Theor Appl Genet 111:1409–1419

  7. Darvasi A, Weinreb A, Minke V, Weller JI, Soller M (1993) Detecting marker-QTL linkage and estimating QTL gene effect and map location using a saturated genetic map. Genetics 134:943–951

  8. Delacy IH, Cooper M (1990) Pattern analysis for the analysis of regional variety trials. In: Kang MS (ed) Genotype-by-environment interaction and plant breeding. Louisiana State Univ Agr Cent, Baton Rouge

  9. Delannay X, Rodgers DM, Palmer RG (1983) Relative genetic contributions among ancestral lines to north American soybean cultivars. Crop Sci 23:944–949

  10. Emik LO, Terrill CE (1949) Systematic procedures for calculating inbreeding coefficients. J Hered 40:51–55

  11. Farnir F, Coppieters W, Arranz J-J, Berzi P, Cambisano N, Grisart B, Karim L, Marcq F, Moreau L, Mni M, Nezer C, Simon P, Vanmanshoven P, Wagengaar D, Georges M (2000) Extensive linkage disequilibrium in cattle. Genome Res 10:220–227

  12. George AW, Visscher PM, Haley CS (2000) Mapping quantitative traits in complex pedigrees: a two-step variance component approach. Genetics 156:2081–2092

  13. Gizlice Z, Carter TE Jr, Burton JW (1994) Genetic base for north American public soybean cultivars released between 1947 and 1988. Crop Sci 34:1143–1151

  14. Grupe A, Germer S, Usuka J, Aud D, Belknap JK, Klein RF, Ahluwalia MK, Higuchi R, Peltz G (2001) In silico mapping of complex disease-related traits in mice. Science 292:1915–1918

  15. Haldane JBS, Waddington CH. (1931) Inbreeding and linkage. Genetics 16:357–374

  16. Henderson CR (1984) Applications of linear models in animal breeding. University of Guelph, Ontario

  17. Kearsey MJ, Farquhar AGL (1998) QTL analysis in plants; where are we now? Heredity 80:137–142

  18. Kennedy BW, Quinton M, van Arendonk JAM (1992) Estimation of effects of single genes on quantitative traits. J Anim Sci 70:2000–2012

  19. Lande R, Thompson R (1990) Efficiency of marker-assisted selection in the improvement of quantitative traits. Genetics 124:743–756

  20. van Ooijen JW (1992) Accuracy of mapping quantitative trait loci in autogamous species. Theor Appl Genet 84:803–811

  21. Parisseaux B, Bernardo R (2004) In silico mapping of quantitative trait loci in maize. Theor Appl Genet 109:508–514

  22. Rebaï A, Goffinet B, Mangin B (1995) Comparing power of different methods for QTL detection. Biometrics 51:87–99

  23. Smith JSC, Ertl DS, Orman BA (1995) Identification of maize varieties. In: Wrigley CW (ed) Identification of food-grain varieties. Am Assoc Cereal Chemists, St Paul, pp 253–264

  24. Smith OS, Hoard K, Shaw F, Shaw R (1999) Prediction of single-cross performance. In: Coors JG, Pandey S (eds) The genetics and exploitation of heterosis in crops. Am Soc Agron Crop Sci, Crop Science Society of America, Madison, Wisconsin, pp 277–285

  25. Smith S, Beavis W (1996) Molecular marker assisted breeding in a company environment. In: Sobral BWS (ed) The impact of plant molecular genetics. Birkhauser, Boston, pp 259–272

  26. Song QJ, Marek LF, Shoemaker RC, Lark KG, Concibido VC, Delannay X, Spetch JE, Cregan PB (2004) A new integrated linkage map of the soybean. Theor Appl Genet 109:122–128

  27. Whittaker JC, Thompson R, Visscher PM (1996) On the mapping of QTL by regression of phenotype on marker type. Heredity 77:23–32

  28. Xu S, Atchley WR (1995) A random model approach to interval mapping of quantitative trait loci. Genetics 93:580–586

  29. Yu J, Arbelbide M, Bernardo R (2005) Power of in silico QTL mapping from phenotypic, pedigree and marker data in a hybrid breeding program. Theor Appl Genet 110:1061–1067

  30. Zhang Y-M, Mao Y, Xie C, Smith H, Luo L, Xu S (2005) Mapping QTL using naturally occurring genetic variance among commercial inbred lines of maize. Genetics 169:2267–2275

  31. Zhu YL, Song QJ, Hyten DL, Van Tassell CP, Matukumalli LK, Grimm DR, Hyatt SM, Fickus EW, Young ND, Cregan PB (2003) Single-nucleotide polymorphisms in soybean. Genetics 163:1123–1134

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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.

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Correspondence to R. Bernardo.

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