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What proportion of declared QTL in plants are false?

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Abstract

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, α C. 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 α C increased. Notably, the FDR was often 10–30 times higher than the α C level used. Regardless of the number of QTL, heritability, or size of the genome, the FDR was ≤0.01 when α C was 0.0001. The FDR increased to 0.82 when α C was 0.05, heritability was low, and only one QTL controlled the trait. An α C 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 α C=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., α C≅0.0001.

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References

  • 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 102:1164–1168

    CAS  Google Scholar 

  • Baker RJ (1984) Quantitative genetic principles in plant breeding. In: Gustafson JP (ed) Gene manipulation in plant improvement. Plenum, New York, pp 147–176

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

    Google Scholar 

  • Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc B 57:289–300

    Google Scholar 

  • Bernardo R (2002) Breeding for quantitative traits in plants. Stemma, Woodbury, Minn.

  • 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 153:2001–2012

    Google Scholar 

  • Churchill GA, Doerge RW (1994) Empirical threshold values for quantitative trait mapping. Genetics 138:963–971

    CAS  PubMed  Google Scholar 

  • Coors JG (2001) Changing role of plant breeding in the public sector. Proc Corn Sorghum Ind Res Conf 56:48–56

    Google Scholar 

  • 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 15–26

  • Dudley JW (1993) Molecular markers in plant improvement: manipulation of genes affecting quantitative traits. Crop Sci 33:660–668

    CAS  Google Scholar 

  • 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 33–40

  • Fernando RL (2002) Methods to map QTL. http://meishan.ansci.iastate.edu/rohan/notes-dir/QTL.pdf

  • 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 105:413–422

    Article  Google Scholar 

  • Hallauer AR (1990) Methods used in developing maize inbreds. Maydica 35:1–16

    Google Scholar 

  • Hospital F, Moreau L, Lacoudre F, Charcosset A, Gallais A (1997) More on the efficiency of marker-assisted selection. Theor Appl Genet 95:1181–1189

    Article  Google Scholar 

  • Johnson L (2001) Marker assisted sweet corn breeding: A model for specialty crops. Proc Corn Sorghum Ind Res Conf 56:25–30

    Google Scholar 

  • Kacser H, Burns JA (1981) The molecular basis of dominance. Genetics 97:639–666

    Google Scholar 

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

    PubMed  Google Scholar 

  • Keightley PD (1989) Models of quantitative variation of flux in metabolic pathways. Genetics 121:869–876

    Google Scholar 

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

    CAS  PubMed  Google Scholar 

  • Lynch M, Walsh B (1998) Genetics and analysis of quantitative traits. Sinauer, Sunderland, Mass.

  • 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 102:517–523

    CAS  Google Scholar 

  • 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 37:1611–1615

    CAS  Google Scholar 

  • Openshaw S, Frascaroli E (1997) QTL detection and marker-assisted selection for complex traits in maize. Proc Corn Sorghum Ind Res Conf 52:44–53

    Google Scholar 

  • 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 36:1676–1683

    CAS  Google Scholar 

  • 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 103:75–83

    Article  CAS  Google Scholar 

  • Thompson JN (1975) Quantitative variation and gene number. Nature 258:665–668

    PubMed  Google Scholar 

  • 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 150:1699–1706

    CAS  PubMed  Google Scholar 

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

    Article  Google Scholar 

  • 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 94:619–632

    Article  CAS  Google Scholar 

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Correspondence to R. Bernardo.

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Communicated by H.C. Becker

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Bernardo, R. What proportion of declared QTL in plants are false?. Theor Appl Genet 109, 419–424 (2004). https://doi.org/10.1007/s00122-004-1639-3

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