Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Mapping quantitative trait loci in plants: uses and caveats for evolutionary biology

Key Points

  • Geneticists are poised to move into a dissection of complex phenotypic traits.

  • The complexity of phenotypes probably arises from the segregation of alleles at many, interacting loci (quantitative trait loci, or QTL), the effects of which are sensitive to the environment.

  • QTL mapping will allow biologists to make the first steps towards understanding the genetics and evolution of complex phenotypic traits.

  • At its most basic level, QTL mapping simply involves finding an association between a genetic marker and a phenotype that can be measured.

  • Generally, QTL mapping requires two parental strains with different alleles that affect variation in a single trait and a polymorphic genetic map that allows the two strains to be distinguished genetically.

  • QTL analysis of adaptive traits has revealed the number of genes that underlie those traits, their mode of action, and their dependence on the starting material and the environment.

  • QTL analysis has provided vital insights into the processes of speciation and plant domestication, as well as having enormous practical potential for agriculture.

  • The fw2.2 QTL for fruit size in tomato has been characterized at the molecular level.

  • Comparative QTL mapping has revealed insights into plant domestication and has also shown how the merger of genomes of divergent evolutionary histories can produce 'unique avenues' for selection.

  • There are numerous caveats for the application of QTL mapping techniques, including the expense of map construction, the bias in location and effect of individual QTL, and the difficulty in moving from QTL to an actual genetic locus.

  • QTL analysis is as applicable to humans and animals as it is to plants, although working in a plant system has many advantages, including the ability to generate large numbers of progeny from inbred parental lines and to work in ecologically relevant environments.

Abstract

Gregor Mendel was either clever or lucky enough to study traits of simple inheritance in his pea plants; however, many plant characters of interest to modern geneticists are decidedly complex. Understanding the genetic basis of such complex, or quantitative, traits requires a combination of modern molecular genetic techniques and powerful statistical methods. These approaches have begun to give us insight into understanding the evolution of complex traits both in crops and in wild plants.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Figure 1: Principles of mapping quantitative trait loci.
Figure 2: F2 design: genetic mapping in monkeyflowers.
Figure 3: Backcross design: genetic mapping in the Louisiana irises.
Figure 4: Genetic basis of phenotypic variation in fruit size in the tomato.
Figure 5: The evolution of apical dominance in maize (Zea mays).

References

  1. Kaul, S. et al. Analysis of the genome sequence of the flowering plant Arabidopsis thaliana. Nature 408, 796– 815 (2000).

    CAS  Article  Google Scholar 

  2. Theologis, A. et al. Sequence and analysis of chromosome 1 of the plant Arabidopsis thaliana. Nature 408, 816– 820 (2000).

    PubMed  Article  Google Scholar 

  3. Lin, X. Y. et al. Sequence and analysis of chromosome 2 of the plant Arabidopsis thaliana. Nature 402, 761– 768 (1999).

    CAS  Article  PubMed  Google Scholar 

  4. Salanoubat, M. et al. Sequence and analysis of chromosome 3 of the plant Arabidopsis thaliana. Nature 408, 820– 822 (2000).

    CAS  PubMed  Article  Google Scholar 

  5. Mayer, K. et al. Sequence and analysis of chromosome 4 of the plant Arabidopsis thaliana. Nature 402, 769– 777 (1999).

    CAS  PubMed  Article  Google Scholar 

  6. Tabata, S. et al. Sequence and analysis of chromosome 5 of the plant Arabidopsis thaliana. Nature 408, 823– 826 (2000).

    CAS  PubMed  Article  Google Scholar 

  7. Adam, D. Now for the hard ones. Nature 408, 792– 793 (2000).

    CAS  PubMed  Article  Google Scholar 

  8. Bulmer, M. G. The Mathematical Theory of Quantitative Genetics (Clarendon, Oxford, 1985).

    Google Scholar 

  9. Falconer, D. S. & Mackay, T. F. C. Introduction to Quantitative Genetics (Addison–Wesley–Longman, Harlow, 1996).

    Google Scholar 

  10. Lynch, M. & Walsh, B. Genetics and the Analysis of Quantitative Traits (Sinauer Associates, Sunderland, Massachusetts, 1997).

    Google Scholar 

  11. Tanksley, S. D. Mapping polygenes. Annu. Rev. Genet. 27, 205–233 (1993).

    CAS  PubMed  Article  Google Scholar 

  12. Liu, B.-H. Statistical Genomics: Linkage, Mapping and QTL Analysis (Boca Raton, Florida, USA, 1998).An excellent overview of QTL mapping techniques.

    Google Scholar 

  13. Burr, B. & Burr, F. A. Recombinant inbreds for molecular mapping in maize: theoretical and practical considerations. Trends Genet. 7, 55–60 ( 1991).

    CAS  PubMed  Google Scholar 

  14. Moreno-Gonzalez, J. Efficiency of generations for estimating marker-associated QTL effects by multiple regression. Genetics 135, 223– 231 (1993).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  15. Sax, K. The association of size differences with seed-coat pattern and pigmentation in Phaseolus vulgaris. Genetics 8, 552– 560 (1923).The first mapping of a QTL by association with a Mendelian marker.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  16. Lister, C. & Dean, C. Recombinant inbred lines for mapping RFLP and phenotypic markers in Arabidopsis thaliana. Plant J. 4, 745–750 ( 1993).

    CAS  Article  Google Scholar 

  17. Cho, R. J. et al. Genome-wide mapping with biallelic markers in Arabidopsis thaliana. Nature Genet. 23, 203– 207 (1999).

    CAS  PubMed  Article  Google Scholar 

  18. Barton, N. H. & Turelli, M. Evolutionary quantitative genetics: how little do we know? Annu. Rev. Genet. 23, 337–370 (1989).

    CAS  PubMed  Article  Google Scholar 

  19. Lande, R. The response to selection on major and minor mutations affecting a metrical trait. Heredity 50, 47– 65 (1983).

    Article  Google Scholar 

  20. Orr, H. A. & Coyne, J. A. The genetics of adaptation: a reassessment . Am. Nat. 140, 725–742 (1992).The landmark paper that exhumed the argument that genes of major effect could be important in adaptation.

    CAS  PubMed  Article  Google Scholar 

  21. Fisher, R. A. The Genetical Theory of Natural Selection (Dover, New York, 1958).

    Google Scholar 

  22. Alonso-Blanco, C., Blankestijn-de Vries, H., Hanhart, C. J. & Koornneef, M. Natural allelic variation at seed size loci in relation to other life history traits of Arabidopsis thaliana. Proc. Natl Acad. Sci. USA 96, 4710–4717 ( 1999).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  23. Stanton, M. L. Seed variation in wild radish: effect of seed size on components of seedling and adult fitness. Ecology 65, 1105– 1112 (1984).

    Article  Google Scholar 

  24. Fisher, R. A. The use of multiple measurements in taxonomic problems. Ann. Eugen. 7, 179–188 ( 1936).

    Article  Google Scholar 

  25. Juenger, T., Purugganan, M. D. & Mackay, T. F. C. Quantitative trait loci for floral morphology in Arabidopsis thaliana. Genetics 156, 1379–1392 (2000).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  26. Kelly, M. G. & Levin, D. A. Directional selection on initial flowering date in Phlox drummondii (Polemoniaceae). Am. J. Bot. 87, 382–391 ( 2000).

    CAS  PubMed  Article  Google Scholar 

  27. Kowalski, S. P., Lan, T. H., Feldmann, K. A. & Paterson, A. H. QTL mapping of naturally-occurring variation in flowering time of Arabidopsis thaliana. Mol. Gen. Genet. 245, 548– 555 (1994).

    CAS  PubMed  Article  Google Scholar 

  28. Mitchell-Olds, T. Genetic constraints on life history evolution — quantitative trait loci influencing growth and flowering in Arabidopsis thaliana. Evolution 50, 140–145 ( 1996).

    PubMed  Google Scholar 

  29. Kuittinen, H., Sillanpää, M. J. & Savolainen, O. Genetic basis of adaptation: flowering time in Arabidopsis thaliana. Theor. Appl. Genet. 95, 573–583 (1997).

    Article  Google Scholar 

  30. Byrne, M. et al. Identification and mode of action of quantitative trait loci affecting seedling height and leaf area in Eucalyptus nitens. Theor. Appl. Genet. 94, 674–681 (1997).

    Article  Google Scholar 

  31. Byrne, M., Murrell, J. C., Owen, J. V., Williams, E. R. & Moran, G. F. Mapping of quantitative trait loci influencing frost tolerance in Eucalyptus nitens. Theor. Appl. Genet. 95, 975–979 (1997).

    CAS  Article  Google Scholar 

  32. Hurme, P., Silanpää, M. J., Arjas, E., Repo, T. & Savolainen, O. Genetic basis of climatic adaptation in Scots pine by Bayesian quantitative trait locus analysis. Genetics 156, 1309–1322 ( 2000).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  33. Bradshaw, H. D. Jr & Stettler, R. Molecular genetics of growth and development in Populus. IV. Mapping QTLs with large effects on growth, form, and phenology traits in a forest tree. Genetics 139, 963– 973 (1995).

    CAS  PubMed  Article  Google Scholar 

  34. Frewen, B. E. et al. Quantitative trait loci and candidate gene mapping of bud set and bud flush in Populus. Genetics 154, 837–845 (2000).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  35. Lewontin, R. C. The Genetic Basis of Evolutionary Change (Columbia University Press, New York, 1974).

    Google Scholar 

  36. Gottlieb, L. D. Genetics and morphological evolution in plants. Am. Nat. 123, 681–709 (1984). An early, but underappreciated, paper that raised the issue that genes of major effect could be important in speciation.

    Article  Google Scholar 

  37. Coyne, J. A. & Lande, R. The genetic basis of species differences in plants. Am. Nat. 126, 141– 145 (1985).

    Article  Google Scholar 

  38. Bradshaw, H. D. Jr, Wilbert, S. M., Otto, K. G. & Schemske, D. W. Genetic mapping of floral traits associated with reproductive isolation in monkeyflowers (Mimulus). Nature 376, 762–765 ( 1995).One of the first attempts in natural populations to identify genes that are important in speciation by QTL mapping.

    CAS  Article  Google Scholar 

  39. Bradshaw, H. D. Jr, Otto, K. G., Frewen, B. E., McKay, J. K. & Schemske, D. W. Quantitative trait loci affecting differences in floral morphology between two species of monkeyflower (Mimulus). Genetics 149, 367–382 (1998).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  40. Schemske, D. W. & Bradshaw, H. D. Jr. Pollinator preference and the evolution of floral traits in monkeyflowers (Mimulus). Proc. Natl Acad. Sci. USA 96, 11910–11915 (1999).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  41. Kelly, A. J. & Willis, J. H. Polymorphic microsatellite loci in Mimulus guttatus and related species. Mol. Ecol. 7, 769–774 (1998).

    CAS  Article  Google Scholar 

  42. Cruzan, M. B. & Arnold, M. L. Ecological and genetic associations in an Iris hybrid zone. Evolution 47, 1432–1445 (1993).

    PubMed  Article  Google Scholar 

  43. Arnold, M. L. Anderson's paradigm: Louisiana irises and the study of evolutionary phenomena . Mol. Ecol. 9, 1687–1698 (2000).

    CAS  PubMed  Article  Google Scholar 

  44. Hodges, S. A. & Arnold, M. L. Floral and ecological isolation between Aquilegia formosa and Aquilegia pubescens. Proc. Natl Acad. Sci. USA 91, 2493– 2496 (1994).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  45. Rieseberg, L. H., Sinervo, B., Linder, C. R., Ungerer, M. C. & Arias, D. M. Role of gene interactions in hydrid speciation: evidence from ancient and experimental hybrids. Science 272, 741–745 ( 1996).

    CAS  PubMed  Article  Google Scholar 

  46. Kim, S.-C. & Rieseberg, L. H. Genetic architecture of species differences in annual sunflowers: implications for adaptive trait introgression . Genetics 153, 965–977 (1999).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  47. Rieseberg, L. H., Whitton, J. & Gardner, K. Hybrid zones and the genetic architecture of a barrier to gene flow between two sunflower species. Genetics 152, 713–977 (1999).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  48. Hodges, S. A., Burke, J. M. & Arnold, M. L. Natural formation of Iris hybrids: experimental evidence on the establishment of hybrid zones. Evolution 50, 2504–2509 (1996).

    PubMed  Article  Google Scholar 

  49. Stuber, C. W. Mapping and manipulating quantitative traits in maize. Trends Genet. 11, 477–481 ( 1995).

    CAS  PubMed  Article  Google Scholar 

  50. Young, N. D. A cautiously optimistic vision for marker-assisted breeding. Mol. Breed. 5, 505–510 ( 1999).

    Article  Google Scholar 

  51. Grandillo, S., Ku, H. M. & Tanksley, S. D. Identifying the loci responsible for natural variation in fruit size and shape in tomato. Theor. Appl. Genet. 99, 978–987 (1999).

    CAS  Article  Google Scholar 

  52. Alpert, K. B., Grandillo, S. & Tanksley, S. D. fw-2.2 — a major QTL controlling fruit weight is common to both red-fruited and green-fruited tomato species. Theor. Appl. Genet. 91, 994–1000 (1995).

    CAS  PubMed  Article  Google Scholar 

  53. Alpert, K. B. & Tanksley, S. D. High-resolution mapping and isolation of a yeast artificial chromosome contig containing fw2.2: a major fruit weight quantitative trait locus in tomato. Proc. Natl Acad. Sci. USA 93, 15503–15507 (1996).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  54. Frary, A. et al. fw2.2: A quantitative trait locus key to the evolution of tomato fruit size. Science 289, 85– 88 (2000).A landmark in QTL analysis: the first molecular characterization of a locus that was originally identified entirely by QTL mapping. References 51 53 are the prologue to this milestone.

    CAS  PubMed  Article  Google Scholar 

  55. Doebley, J. & Stec, A. Genetic analysis of the morphological differences between maize and teosinte. Genetics 129 , 285–295 (1991).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  56. Doebley, J. Mapping the genes that made maize. Trends Genet. 8, 302–307 (1992).

    CAS  PubMed  Article  Google Scholar 

  57. Doebley, J. & Stec, A. Inheritance of the morphological differences between maize and teosinte: comparison of results for two F2 populations . Genetics 134, 559–570 (1993).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  58. Doebley, J., Stec, A. & Gustus, C. Teosinte branched 1 and the origin of maize: evidence for epistasis and the evolution of dominance. Genetics 141, 333–346 (1995).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  59. White, S. & Doebley, J. Of genes and genomes and the origin of maize. Trends Genet. 14, 327– 332 (1998).

    CAS  PubMed  Article  Google Scholar 

  60. Dorweiler, J., Stec, A., Kermicle, J. & Doebley, J. Teosinte glume architecture 1: a genetic locus controlling a key step in maize evolution . Science 262, 233–235 (1993).

    CAS  PubMed  Article  Google Scholar 

  61. Doebley, J., Stec, A. & Hubbard, L. The evolution of apical dominance in maize. Nature 386, 485–488 ( 1997).

    CAS  PubMed  Article  Google Scholar 

  62. Wang, R. L., Stec, A., Hey, J., Lukens, L. & Doebley, J. The limits of selection during maize domestication. Nature 398, 236–239 ( 1999).References 55 62 chronicle a decade of work on understanding the genetics of the domestication of modern maize.

    CAS  PubMed  Article  Google Scholar 

  63. Paterson, A. H. et al. Convergent domestication of cereal crops by independent mutations at corresponding genetic loci. Science 269, 1714–1718 (1995). An excellent example of the power of comparative QTL mapping.

    CAS  PubMed  Article  Google Scholar 

  64. Ramsey, J. & Schemske, D. W. Pathways, mechanisms, and rates of polyploid formation in flowering plants. Annu. Rev. Ecol. Syst. 29, 467–501 ( 1998).

    Article  Google Scholar 

  65. Soltis, P. S. & Soltis, D. E. The role of genetic and genomic attributes in the success of polyploids. Proc. Natl Acad. Sci. USA 97, 7051–7057 ( 2000).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  66. Hilu, K. W. Polyploidy and the evolution of domesticated plants. Am. J. Bot. 80, 1494–1499 ( 1993).

    Article  Google Scholar 

  67. Paterson, A. H. et al. Comparative genomics of plant chromosomes. Plant Cell 12, 1523–1539 ( 2000).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  68. Jiang, C. X., Wright, R. J., El-Zik, K. M. & Paterson, A. H. Polyploid formation created unique avenues for response to selection in Gossypium (cotton). Proc. Natl Acad. Sci. USA 95 , 4419–4424 (1998).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  69. Wright, R. J., Thaxton, P. M., El-Zik, K. M. & Paterson, A. H. D-subgenome bias of Xcm resistance genes in tetraploid Gossypium (cotton) suggests that polyploid formation has created novel avenues for evolution. Genetics 149, 1987– 1996 (1998).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  70. Beavis, W. D. The power and deceit of QTL experiments: lessons from comparative QTL studies . Proc. Corn and Sorghum Industry Res. Conf., Am. Seed Trade Assoc., Washington DC, 255–266 ( 1994).

  71. Beavis, W. D. in Molecular Dissection of Complex Traits (ed. Paterson, A. H.) 145 –162 (CRC, Boca Raton, Florida, 1998 ).References 70 and 71 provided the first and most influential caveats for the use of QTL analysis in both agriculture and evolutionary biology.

    Google Scholar 

  72. Copenhaver, G. P., Browne, W. E. & Preuss, D. Assaying genome-wide recombination and centromere functions with Arabidopsis tetrads. Proc. Natl Acad. Sci. USA 95, 247–252 (1998).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  73. Begun, D. J. & Aquadro, C. F. Levels of naturally occurring DNA polymorphism correlate with recombination rates in D. melanogaster . Nature 356, 519–520 (1992).

    CAS  Article  PubMed  Google Scholar 

  74. Risch, N. J. Searching for genetic determinants in the new millennium. Nature 405, 847–856 ( 2000).

    CAS  Article  PubMed  Google Scholar 

  75. Weiss, K. M. & Terwilliger, J. D. How many diseases does it take to map a gene with SNPs? Nature Genet. 26, 151–157 (2000).

    CAS  PubMed  Article  Google Scholar 

  76. Cardon, L. R. & Bell, J. I. Association study designs for complex diseases. Nature Rev. Genet. 2, 91– 99 (2001).

    CAS  PubMed  Article  Google Scholar 

  77. Doerge, R. W., Zeng, Z. B. & Weir, B. S. Statistical issues in the search for genes affecting quantitative traits in experimental populations. Stat. Sci. 12, 195–219 (1997). An excellent overview of the statistical issues involved in QTL analysis.

    Article  Google Scholar 

  78. Melchinger, A. E., Utz, H. F. & Schon, C. C. Quantitative trait locus (QTL) mapping using different testers and independent population samples in maize reveals low power of QTL detection and large bias in estimates of QTL effects. Genetics 149, 383–403 ( 1998).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  79. Otto, S. P. & Jones, C. D. Detecting the undetected: estimating the total number of loci underlying a quantitative trait. Genetics 156, 2093–2107 ( 2000).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  80. Paterson, A. H. et al. Mendelian factors underlying quantitative traits in tomato: comparison across species, generations, and environments. Genetics 127, 181–197 ( 1991).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  81. Jansen, R. C. Interval mapping of multiple quantitative trait loci. Genetics 135, 205–211 ( 1993).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  82. Lander, E. & Kruglyak, L. Genetic dissection of complex traits: guidelines for interpreting and reporting linkage results. Nature Genet. 11, 241–247 ( 1995).

    CAS  Article  PubMed  Google Scholar 

  83. Lander, E. S. & Botstein, D. Mapping Mendelian factors underlying quantitative traits using RFLP linkage maps. Genetics 121, 185–199 (1989). In this paper, interval mapping with molecular markers to map QTL was first proposed for species in which many morphological markers were unavailable. A maximum-likelihood statistical approach for QTL mapping was also developed.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  84. Knott, S. A. & Haley, C. S. Maximum likelihood mapping of quantitative trait loci using full-sib families. Genetics 132, 1211–1222 (1992).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  85. Zeng, Z.-B. Precision mapping of quantitative trait loci. Genetics 136, 1457–1468 (1994).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  86. Churchill, G. A. & Doerge, R. W. Empirical threshold values for quantitative trait mapping. Genetics 138 , 963–971 (1994).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  87. Doerge, R. W. & Churchill, G. A. Permutation tests for multiple loci affecting a quantitative character. Genetics 142 , 285–294 (1996).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  88. Sillanpää, M. J. & Arjas, E. Bayesian mapping of multiple quantitative trait loci from incomplete inbred line cross data . Genetics 148, 1373–1388 (1998).

    PubMed  PubMed Central  Article  Google Scholar 

  89. Haley, C. S., Knott, S. A. & Elsen, J.-M. Mapping quantitative trait loci in crosses between outbred lines using least squares. Genetics 136, 1195–1207 (1994).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  90. Hoeschele, I., Uimari, P., Grignola, F. E., Zhang, Q. & Gage, K. M. Advances in statistical methods to map quantitative trait loci in outbred populations. Genetics 147, 1445–1457 ( 1997).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  91. Sillanpää, M. J. & Arjas, E. Bayesian mapping of multiple quantitative trait loci from incomplete outbred offspring data . Genetics 151, 1605–1619 (1999).

    PubMed  PubMed Central  Article  Google Scholar 

  92. Pérez-Enciso, M. & Varona, L. Quantitative trait loci mapping in F2 crosses between outbred lines. Genetics 155, 391–405 ( 2000).

    PubMed  PubMed Central  Article  Google Scholar 

  93. George, A. W., Visscher, P. M. & Haley, C. S. Mapping quantitative trait loci in complex pedigrees: a two-step variance component approach. Genetics 156 , 2081–2092 (2000).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  94. Hoeschele, I. in Handbook of Statistical Genetics (eds Balding, D., Bishop, M. & Cannings, C.) 599–644 (John Wiley & Sons Ltd, London, 2001).

    Google Scholar 

  95. Liu, S.-C., Lin, Y.-R., Irvine, J. E. & Paterson, A. H. in Molecular Dissection of Complex Traits (ed. Paterson, A. H.) 95–101 (CRC, Boca Raton, Florida, 1998).

    Google Scholar 

  96. Ming, R. et al. Detailed alignment of Saccharum and Sorghum chromosomes: comparative organization of closely related diploid and polyploid genomes . Genetics 150, 1663–1682 (1998).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  97. Doerge, R. W. & Craig, B. A. Model selection for quantitative trait locus analysis in polyploids. Proc. Natl Acad. Sci. USA 97, 7951–7956 (2000).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  98. Rieseberg, L. H. & Buerkle, C. A. Genetic mapping in hybrid zones. Am. Nat. (in the press).

  99. Provine, W. B. The Origins of Theoretical Population Genetics (Chicago Univ. Press, Illinois, 1971).A superb history of the early conflict between the 'Mendelians' and the 'biometricians', and its resolution.

    Google Scholar 

  100. East, E. M. A Mendelian interpretation of variation that is apparently continuous. Am. Nat. 44, 65–82 ( 1910).

    Article  Google Scholar 

  101. Fisher, R. A. The correlation between relatives on the supposition of Mendelian inheritance . Trans. R. Soc. Edinb. 52, 399– 433 (1918).

    Article  Google Scholar 

  102. Lande, R. The maintenance of genetic variability by mutation in a polygenic character with linked loci. Genet. Res. 26, 221– 235 (1976).

    Article  Google Scholar 

  103. Lande, R. Quantitative genetic analysis of multivariate evolution, applied to brain:body size allometry. Evolution 33, 402– 416 (1979).

    PubMed  Article  Google Scholar 

  104. Lande, R. & Arnold, S. J. The measurement of selection on correlated characters. Evolution 37, 1210 –1226 (1983).

    Article  PubMed  Google Scholar 

  105. Via, S. & Lande, R. Genotype–environment interaction and the evolution of phenotypic plasticity. Evolution 39, 505–522 (1985).

    PubMed  Article  Google Scholar 

  106. Turelli, M. & Barton, N. H. Dynamics of polygenic characters under selection. Theor. Popul. Biol. 38, 1–57 (1990).

    Article  Google Scholar 

  107. Barton, N. H. & Turelli, M. Natural and sexual selection on many loci. Genetics 127, 229 –255 (1991).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  108. Turelli, M. & Barton, N. H. Genetic and statistical analyses of strong selection on polygenic traits: what, me normal? Genetics 138, 913–941 ( 1994).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  109. Robertson, A. in Heritage from Mendel (ed. Brink, A.) 265– 280 (Wisconsin Univ. Press, Madison, Wisconsin, 1967 ).

    Google Scholar 

  110. Haley, C. S. & Knott, S. A. A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. Heredity 69, 315–324 ( 1992).

    CAS  Article  PubMed  Google Scholar 

  111. Thoday, J. M. Location of polygenes. Nature 191, 368– 370 (1961).

    Article  Google Scholar 

  112. Zeng, Z.-B. Theoretical basis for separation of multiple linked gene effects in mapping quantitative trait loci. Proc. Natl Acad. Sci. USA 90, 10972–10976 (1993).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  113. Kao, C. H., Zeng, Z.-B. & Teasdale, R. D. Multiple interval mapping for quantitative trait loci. Genetics 152, 1203– 1216 (1999).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  114. Zeng, Z.-B., Kao, C. H. & Basten, C. J. Estimating the genetic architecture of quantitative traits. Genet. Res. 74, 279– 289 (1999).

    CAS  PubMed  Article  Google Scholar 

  115. Basten, C. J., Weir, B. S. & Zeng, Z.-B. QTL Cartographer: A Reference Manual and Tutorial for QTL Mapping (Department of Statistics, North Carolina Univ. Press, Raleigh, North Carolina, 1995).

    Google Scholar 

  116. Reinisch, A. J. et al. A detailed RFLP map of cotton, Gossypium hirsutum x Gossypium barbadense: chromosome organization and evolution in a disomic polyploid genome. Genetics 138, 829– 847 (1994).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

Download references

Acknowledgements

I thank M. Arnold, R. Baucom, A. Bouck, C. Goodwillie, A. Johnson, A. Paterson, L. Rieseberg and J. Willis for unpublished material, helpful discussions and constructive comments on the manuscript.

Author information

Authors and Affiliations

Authors

Related links

Related links

FURTHER INFORMATION

Arabidopsis thaliana

rice

Mendelism

R. A. Fisher

Francis Galton

Karl Pearson

Arabidopsis Biological Resource Centre (ABRC)

Cereon Genomics Arabidopsis SNP collection

Nottingham Arabidopsis Stock Centre: Columbia × Landsberg RI lines

Rockefeller University's collection of genetic analysis software

The Arabidopsis Information Resource (TAIR)

The Institute for Genomic Research

Rodney Mauricio's lab

ENCYCLOPEDIA OF LIFE SCIENCES

Adaptation genetics

Quantitative genetics

Glossary

MULTIVARIATE NORMAL DISTRIBUTION

The central limit theorem assures a normal (bell-shaped) distribution for a variable that is the summation of many independent, random inputs. This applies to single or multiple variables.

COROLLA

Collectively, the petals of a flower.

NECTAR GUIDES

Markings on the petals of flowers, often in contrasting colours or visible only in ultraviolet wavelengths, thought to act as directional beacons for pollinators, especially bees.

ANTHER

The pollen-bearing part of the male floral structure (stamen).

STIGMA

The region, usually the apex, of the gynoecium that receives pollen grains and on which the pollen germinates. The gynoecium is the seed-bearing organ of flowering plants, consisting of the stigma, style and ovary.

STYLAR TUBE

In the genus Iris, the styles (tubular columns of tissue arising from the top of the ovary) look like flower petals and are tightly appressed to the top of the actual petals, forming this tube between the petal and the style.

PHENOLOGY

The timing of periodic biological phenomena that are usually correlated with climactic conditions.

HYBRID ZONE

A region of reproduction between individuals of different species, usually occurring where the ranges of the species come together.

LIKELIHOOD-RATIO TEST STATISTIC

A maximum-likelihood method of hypothesis testing. The likelihood-ratio test statistic is twice the natural logarithm of the ratio of the maximum likelihood that the data fit the alternative hypothesis to the maximum likelihood that the data fit the null hypothesis.

VARIANCE

A statistic that quantifies the dispersion of data about the mean. In quantitative genetics, the phenotypic variance (Vp) is the observed variation of a trait in a population. Vp can be partitioned into components, owing to genetic variance (Vg), environmental variance (Ve) and gene-by-environment correlations and interactions.

SYMPATRIC

Occurring in the same area without loss of identity from interbreeding.

MACROEVOLUTION

Evolution at or above the level of species.

INTROGRESSIVE HYBRIDIZATION

Incorporation of genes from one species into the gene pool of another species.

DEHISCENCE

The splitting open of a fruit.

LOD SCORE

(Base 10 'logarithm of the odds', or 'log-odds'.) A method of hypothesis testing. The logarithm of the ratio between likelihoods under the null and alternative hypotheses.

MONTE CARLO SIMULATION

The use of randomly generated or sampled data and computer simulations to obtain approximate solutions to complex mathematical and statistical problems.

PERMUTATION TEST

A method of hypothesis testing. In these tests, an empirical distribution of a test statistic is obtained by permuting the original sample many times. Each permuted sample is considered to be a sample of the population under the null hypothesis.

BAYESIAN APPROACH

An alternative statistical method that allows the use of prior information to evaluate the posterior probabilities of different hypotheses.

SIMPLEX SEGREGATION

Segregation in polyploids. Segregation with no crossovers of the simplex genotype Aaaa would result in a gametic ratio of 1/2 Aa and 1/2 aa.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Mauricio, R. Mapping quantitative trait loci in plants: uses and caveats for evolutionary biology. Nat Rev Genet 2, 370–381 (2001). https://doi.org/10.1038/35072085

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1038/35072085

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing