Theoretical and Applied Genetics

, Volume 109, Issue 2, pp 419–424 | Cite as

What proportion of declared QTL in plants are false?

  • R. BernardoEmail author
Original Paper


The false discovery rate (FDR) is the probability that a quantitative trait locus (QTL) is false, given that a QTL has been declared. A misconception in QTL mapping is that the FDR is equal to the comparison-wise significance level, αC. The objective of this simulation study was to determine the FDR in an F2 mapping population, given different numbers of QTL, population sizes, and trait heritabilities. Markers linked to QTL were detected by multiple regression of phenotype on marker genotype. Phenotypic selection and marker-based recurrent selection were compared. The FDR increased as αC increased. Notably, the FDR was often 10–30 times higher than the αC level used. Regardless of the number of QTL, heritability, or size of the genome, the FDR was ≤0.01 when αC was 0.0001. The FDR increased to 0.82 when αC was 0.05, heritability was low, and only one QTL controlled the trait. An αC of 0.05 led to a low FDR when many QTL (30 or 100) controlled the trait, but this lower FDR was accompanied by a diminished power to detect QTL. Larger mapping populations led to both lower a FDR and increased power. Relaxed significance levels of αC=0.1 or 0.2 led to the largest responses to marker-based recurrent selection, despite the high FDR. To prevent false QTL from confusing the literature and databases, a detected QTL should, in general, be reported as a QTL only if it was identified at a stringent significance level, e.g., αC≅0.0001.


Quantitative Trait Locus False Discovery Rate Mapping Population Quantitative Trait Locus Mapping Phenotypic Selection 
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.


  1. Anderson JA, Stack RW, Liu S, Waldron BL, Fjeld AD, Coyne C, Moreno-Sevilla B, Mitchell Fetch J, Song QJ, Cregan PB, Frohberg RC (2001) DNA markers for Fusarium head blight resistance QTLs in two wheat populations. Theor Appl Genet 102:1164–1168Google Scholar
  2. Baker RJ (1984) Quantitative genetic principles in plant breeding. In: Gustafson JP (ed) Gene manipulation in plant improvement. Plenum, New York, pp 147–176Google Scholar
  3. Beavis WD (1994) The power and deceit of QTL experiments: lessons from comparative QTL studies. Proc Corn Sorghum Ind Res Conf 49:250–266Google Scholar
  4. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc B 57:289–300Google Scholar
  5. Bernardo R (2002) Breeding for quantitative traits in plants. Stemma, Woodbury, Minn.Google Scholar
  6. Bost B, Dillmann C, de Vienne D (1999) Fluxes and metabolic pools as model traits for quantitative genetics. I. The L-shaped distribution of gene effects. Genetics 153:2001–2012Google Scholar
  7. Churchill GA, Doerge RW (1994) Empirical threshold values for quantitative trait mapping. Genetics 138:963–971PubMedGoogle Scholar
  8. Coors JG (2001) Changing role of plant breeding in the public sector. Proc Corn Sorghum Ind Res Conf 56:48–56Google Scholar
  9. Doerge RW, Zeng Z-B, Weir BS (1994) Statistical issues in the search for genes affecting quantitative traits in populations. In: Analysis of molecular marker data. Joint plant breeding symposium series, Corvallis, Ore., 5–6 August 1994, pp 15–26Google Scholar
  10. Dudley JW (1993) Molecular markers in plant improvement: manipulation of genes affecting quantitative traits. Crop Sci 33:660–668Google Scholar
  11. Edwards M, Johnson L (1994) RFLPs for rapid recurrent selection. In: Analysis of molecular marker data. Joint plant breeding symposium series, Corvallis, Ore., 5–6 August 1994, pp 33–40Google Scholar
  12. Fernando RL (2002) Methods to map QTL.
  13. Gupta PK, Balyan HS, Edwards KJ, Isaac P, Korzun V, Roder M, Gautier M-F, Joudrier P, Schlatter AR, Dubcovsky J, de la Pena RC, Khairallah M, Penner G, Hayden MJ, Sharp P, Keller B, Wang RCC, Hardouin JP, Jack P, Leroy P (2002) Genetic mapping of 66 new microsattelite (SSR) loci in bread wheat. Theor Appl Genet 105:413–422CrossRefGoogle Scholar
  14. Hallauer AR (1990) Methods used in developing maize inbreds. Maydica 35:1–16Google Scholar
  15. Hospital F, Moreau L, Lacoudre F, Charcosset A, Gallais A (1997) More on the efficiency of marker-assisted selection. Theor Appl Genet 95:1181–1189CrossRefGoogle Scholar
  16. Johnson L (2001) Marker assisted sweet corn breeding: A model for specialty crops. Proc Corn Sorghum Ind Res Conf 56:25–30Google Scholar
  17. Kacser H, Burns JA (1981) The molecular basis of dominance. Genetics 97:639–666Google Scholar
  18. Kearsey MJ, Farquhar AGL (1998) QTL analysis in plants; where are we now? Heredity 80:137–142PubMedGoogle Scholar
  19. Keightley PD (1989) Models of quantitative variation of flux in metabolic pathways. Genetics 121:869–876Google Scholar
  20. Lande R, Thompson R (1990) Efficiency of marker-assisted selection in the improvement of quantitative traits. Genetics 124:743–756PubMedGoogle Scholar
  21. Lynch M, Walsh B (1998) Genetics and analysis of quantitative traits. Sinauer, Sunderland, Mass.Google Scholar
  22. Ma X-F, Wanous MK, Houchins K, Rodriguez Milla MA, Goicoechea PG, Wang Z, Xie M, Gustafson JP (2001) Molecular linkage mapping in rye (Secale cereale L.). Theor Appl Genet 102:517–523Google Scholar
  23. Mudge J, Cregan PB, Kenworthy JP, Kenworthy WJ, Orf JH, Young ND (1997) Two microsatellite markers that flank the major soybean cyst nematode resistance locus. Crop Sci 37:1611–1615Google Scholar
  24. Openshaw S, Frascaroli E (1997) QTL detection and marker-assisted selection for complex traits in maize. Proc Corn Sorghum Ind Res Conf 52:44–53Google Scholar
  25. Senior ML, Chin ECL, Lee M, Smith JSC, Stuber CW (1996) Simple sequence repeat markers developed from maize sequences found in the GENBANK database: map construction. Crop Sci 36:1676–1683Google Scholar
  26. Shen L, Courtois B, McNally KL, Robin S, Li Z (2001) Evaluation of near-isogenic lines of rice introgressed with QTLs for root depth through marker-aided selection. Theor Appl Genet 103:75–83CrossRefGoogle Scholar
  27. Thompson JN (1975) Quantitative variation and gene number. Nature 258:665–668PubMedGoogle Scholar
  28. Weller JI, Song JZ, Heyen DW, Lewin HA, Ron M (1998) A new approach to the problem of multiple comparisons in the genetic dissection of complex traits. Genetics 150:1699–1706PubMedGoogle Scholar
  29. Whittaker JC, Thompson R, Visscher PM (1996) On the mapping of QTL by regression of phenotypes on marker-type. Heredity 77:23–32CrossRefGoogle Scholar
  30. Yadav R, Courtois B, Huang N, McLaren G (1997) Mapping genes controlling root morphology and root distribution in a doubled-haploid population of rice. Theor Appl Genet 94:619–632CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2004

Authors and Affiliations

  1. 1.Department of Agronomy and Plant GeneticsUniversity of MinnesotaSt. PaulUSA

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