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.

  • Review Article
  • Published:

Multifactorial genetics

Mapping and analysis of quantitative trait loci in experimental populations

Nature Reviews Genetics volume 3, pages 43–52 (2002)Cite this article

Key Points

  • Powerful statistical methods are available for the analysis of quantitative traits in experimental species. To a large extent, the development of these methods has been driven by the increased availability of molecular genetic markers and the generation of high-resolution genetic maps.

  • Three broadly different types of statistical analysis can be used in the search for quantitative trait loci (QTL): single-marker tests, interval mapping and composite interval mapping. The advantage of composite interval mapping methods is that they accommodate multiple QTL.

  • However, none of the existing methods can accommodate the full complexity of multifactorial traits that arise because of extensive gene-by-gene (epistatic) and gene-by-environment interactions. This is an area of active research and a recent computational approach provides a framework for the analysis of this problem, in which QTL number is estimated first, followed by QTL effect and locations.

  • Another statistical issue that is the subject of debate concerns how to assess the statistical significance of any model of a complex trait. Computational simulation and non-parametric resampling are two methods that are being used.

  • Despite the power of the statistical methods, and the wealth of genetic markers, there are few examples in which the genetic basis of a quantitative trait has been thoroughly dissected.

  • One exciting view of the future is to consider functional genomics approaches. Transcriptional profiling in particular could provide a way to move rapidly towards a more comprehensive view of gene interactions and epistasis.

  • Lessons learned from the statistical (QTL) analysis of complex phenotypes have the potential to be applied to an analysis of the variation of gene expression, and in this way a more complete understanding of functional networks of genes and their action might arise. This knowledge could benefit our current ability to understand the connection between phenotype and genotype.

Abstract

Simple statistical methods for the study of quantitative trait loci (QTL), such as analysis of variance, have given way to methods that involve several markers and high-resolution genetic maps. As a result, the mapping community has been provided with statistical and computational tools that have much greater power than ever before for studying and locating multiple and interacting QTL. Apart from their immediate practical applications, the lessons learnt from this evolution of QTL methodology might also be generally relevant to other types of functional genomics approach that are aimed at the dissection of complex phenotypes, such as microarray assessment of gene expression.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Experimental design for quantitative trait loci mapping.
Figure 2: A genetic map of mouse chromosome 11.
Figure 3: Choices of analysis for quantitative trait locus mapping.

Similar content being viewed by others

References

  1. Sax, K. The association of size difference with seed-coat pattern and pigmentation in Phaseolus vulgaris. Genetics 8, 552–560 (1923).The first paper on mapping a QTL using Mendelian markers.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Thoday, J. M. Location of polygenes. Nature 191, 368–370 (1961).Classic paper that is well known for being the first to describe interval mapping and for stating that “The main practical limitation of the [genetic mapping] technique is the availability of suitable markers.”

    Article  Google Scholar 

  3. Fisher, R. The Design of Experiments 3rd edn (Oliver & Boyd, London, 1935).

    Google Scholar 

  4. Watson, J. & Crick, F. A structure for deoxyribose nucleic acid. Nature 171, 737–738 (1953).

    Article  CAS  PubMed  Google Scholar 

  5. Southern, E. M. Detection of specific sequences among DNA fragments separated by gel electrophoresis. J. Mol. Biol. 98, 503–517 (1975).

    Article  CAS  PubMed  Google Scholar 

  6. Sanger, F., Nilken, S. & Coulson, A. R. DNA sequencing with chain-terminating inhibitors. Proc. Natl Acad. Sci. USA 74, 5463–5468 (1980).

    Article  Google Scholar 

  7. Saiki, R. K. et al. Enzymatic amplification of β-globin genomic sequences and restriction site analysis for diagnosis of sickle-cell anemia. Science 230, 1350–1354 (1985).

    Article  CAS  PubMed  Google Scholar 

  8. Kerem, B.-S. et al. Identification of the cystic fibrosis gene: genetic analysis. Science 245, 1073–1080 (1989).

    Article  CAS  PubMed  Google Scholar 

  9. The Huntington's Disease Collaborative Research Group. A novel gene containing a trinucleotide repeat that is expanded and unstable on Huntington's disease chromosome. Cell 72, 971–983 (1993).

  10. Blumenfeld, A. et al. Localization of the gene of familial dysautomia on chromosome 9 and definition of DNA markers for genetic diagnosis. Nature Genet. 4, 160–163 (1993).

    Article  CAS  PubMed  Google Scholar 

  11. Georges, M. et al. Microsatellite mapping of a gene affecting horn development in Bos taurus. Nature Genet. 4, 206–210 (1993).

    Article  CAS  PubMed  Google Scholar 

  12. Keim, P. et al. RFLP mapping in soybean: association between marker loci and variation in quantitative traits. Genetics 126, 735–742 (1990).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Edwards, M. D., Stuber, C. W. & Wendel, J. F. Molecular-marker-facilitated investigations of quantitative-trait loci in maize. I. Numbers, genomic distribution and types of gene action. Genetics 116, 113–125 (1987).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Beckmann, J. S. & Soller, M. Detection of linkage between marker loci and loci affecting quantitative traits in crosses between segregating populations. Theor. Appl. Genet. 76, 228–236 (1988).

    Article  CAS  PubMed  Google Scholar 

  15. Luo, Z. W. & Kearsey, M. J. Maximum likelihood estimation of linkage between a marker gene and a quantitative trait locus. Heredity 63, 401–408 (1989).

    Article  PubMed  Google Scholar 

  16. Lander, E. S. & Botstein, D. Mapping Mendelian factors underlying quantitative traits using RFLP linkage maps. Genetics 121, 185–199 (1989); erratum 136, 705 (1994).One of the key papers on QTL mapping that used intervals of molecular markers.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Knapp, S. J., Bridges, W. C. & Birkes, D. Mapping quantitative trait loci using molecular marker linkage maps. Theor. Appl. Genet. 79, 583–592 (1990).

    Article  CAS  PubMed  Google Scholar 

  18. Carbonell, E. A., Gerig T. M., Balansard, E. & Asins, M. J. Interval mapping in the analysis of nonadditive quantitative trait loci. Biometrics 48, 305–315 (1992).

    Article  Google Scholar 

  19. Jansen, R. C. A general mixture model for mapping quantitative trait loci by using molecular markers. Theor. Appl. Genet. 85, 252–260 (1992).One of many influential publications by Ritsert Jansen detailing the use of general mixture models for QTL mapping.

    Article  CAS  PubMed  Google Scholar 

  20. Knott, S. A. & Haley, C. S. Aspects of maximum likelihood methods for the mapping of quantitative trait loci in line crosses. Genet. Res. 60, 139–151 (1992).

    Article  Google Scholar 

  21. Lincoln, S., Daly, M. & Lander, E. Mapping Genes Controlling Quantitative Traits with MAPMAKER/QTL 1.1. 2nd edition (Whitehead Institute Technical Report, Cambridge, Massachusetts, 1992).The first technical report detailing QTL-mapping software for interval mapping using the Lander and Botstein approach (see reference 16).

    Google Scholar 

  22. Darvasi, A. & Weller, J. I. On the use of the moments method of estimation to obtain approximate maximum likelihood estimates of linkage between a genetic marker and a quantitative locus. Heredity 68, 43–46 (1992).

    Article  PubMed  Google Scholar 

  23. Doerge, R. W. Statistical Methods for Locating Quantitative Trait Loci with Molecular Markers. Ph.D. thesis, Department of Statistics, North Carolina State University, Raleigh, North Carolina, USA (1993).

    Google Scholar 

  24. Zeng, Z.-B. Theoretical basis of precision mapping of quantitative trait loci. Proc. Natl Acad. Sci. USA 90, 10972–10976 (1993).This paper details the theoretical development of composite interval mapping.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Cooper, M. & DeLacy, I. H. Relationships among analytical methods used to study genotypic variation and genotype-by-environmental interaction in plant breeding multi-environment experiments. Theor. Appl. Genet. 88, 561–572 (1994).

    Article  CAS  PubMed  Google Scholar 

  26. Dupuis, J. Statistical Problems Associated with Mapping Complex and Quantitative Traits from Genomic Mismatch Scanning Data. Ph.D. thesis, Department of Statistics, Stanford University, USA (1994).

    Google Scholar 

  27. Kearsey, M. J. & Hyne, V. QTL analysis: a simple 'marker–regression' approach. Theor. Appl. Genet. 8, 698–702 (1994).

    Article  Google Scholar 

  28. Zeng, Z.-B. Precision mapping of quantitative trait loci. Genetics 136, 1457–1468 (1994).Companion paper to Zeng's 1993 (reference 24 ) theoretical development of composite interval mapping.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Xu, S. & Yi, N. Mixed model analysis of quantitative trait loci. Proc. Natl Acad. Sci. USA 97, 14542–14547 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Jansen, R. C. Interval mapping of multiple quantitative trait loci. Genetics 135, 205–211 (1993).Extends the concepts of interval mapping from a single QTL analysis to multiple QTL, and coins the term 'multiple QTL mapping' (MQM).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Jansen, R. C. & Stam, P. High resolution of quantitative traits into multiple loci via interval mapping. Genetics 136, 1447–1455 (1994).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Jiang, C. & Zeng, Z.-B. Multiple trait analysis of genetic mapping for quantitative trait loci. Genetics 140, 1111–1127 (1995).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Ronin, Y. I., Kirzhner, V. M. & Korol, A. B. Linkage between loci of quantitative traits and marker loci: multi-trait analysis with a single marker. Theor. Appl. Genet. 90, 776–786 (1995).

    Article  CAS  PubMed  Google Scholar 

  35. Korol, A. B., Ronin, Y. I. & Kirzhner, V. M. Interval mapping of quantitative trait loci employing correlated trait complexes. Genetics 140, 1137–1147 (1995).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Korol, A. B., Ronin, Y. I., Nevo, E. & Hayes, P. M. Multi-interval mapping of correlated trait complexes. Heredity 80, 273–284 (1998).

    Article  Google Scholar 

  37. Frary, A. et al. A quantitative trait locus key to the evolution of tomato fruit size. Science 289, 85–88 (2000).

    Article  CAS  PubMed  Google Scholar 

  38. The Chipping Forecast. Nature Genet. 21 (Suppl.) (1999).

  39. Ewing, R. M. et al. Large-scale statistical analyses of rice ESTs reveal correlated patterns of gene expression. Genome Res. 9, 950–959 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Kim, S. K. et al. A gene expression map for Caenorhabditis elegans. Science 293, 2087–2092 (2001).

    Article  CAS  PubMed  Google Scholar 

  41. Iyer, V. R. et al. The transcriptional program in the response of human fibrolasts to serum. Science 283, 83–87 (1999).

    Article  CAS  PubMed  Google Scholar 

  42. Perou, C. M. et al. Distinctive gene expression patterns in human mammary epithelial cells and breast cancers. Proc. Natl Acad. Sci. USA 96, 9212–9217 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Zhu, H. et al. Global analysis of protein activities using proteome chips. Science 293, 2101–2105 (2001).

    Article  CAS  PubMed  Google Scholar 

  44. Thiellement, H. et al. Proteomics for genetic and physiological studies in plants. Electrophoresis 20, 2013–2026 (1999).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  46. 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).Detailed review of the statistical issues involved in QTL mapping.

    Article  Google Scholar 

  47. Lynch, M. & Walsh, B. Genetics and Analysis of Quantitative Traits (Sinauer Associates, Sunderland, Massachusetts, 1998).

    Google Scholar 

  48. Liu, B.-H. Genomics: Linkage Mapping and QTL Analysis (CRC, Boca Raton, Florida, 1998).

    Google Scholar 

  49. Jansen, R. C. in Handbook of Statistical Genetics (eds Balding, D. et al.) (John Wiley & Sons, New York, 2001).

    Google Scholar 

  50. Quackenbush, J. Computational analysis of microarray data. Nature Rev. Genet. 2, 418–427 (2001).

    Article  CAS  PubMed  Google Scholar 

  51. Schadt, E. Enhancing candidate gene detection via experimental genome annotation and treating transcript abundance as quantitative traits. Pezcoller Found. J. 10 (2001).

  52. Jansen, R. C. & Nap, J.-N. Genetical genomics: the added value from segregation. Trends Genet. 17, 388–391 (2001).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Xie, C. G. & Xu, S. H. Mapping quantitative trait loci in tetraploid populations. Genet. Res. 76, 105–115 (2000).

    Article  CAS  PubMed  Google Scholar 

  55. Mackay, T. F. C. Quantitative trait loci in Drosophila. Nature Rev. Genet. 2, 11–20 (2001).

    Article  CAS  PubMed  Google Scholar 

  56. Göring, H. H., Terwilliger, J. D. & Blangero, J. Large upward bias in estimation of locus-specific effects from genomewide scans. Am. J. Hum. Genet. 69, 1357–1369 (2001).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Neter, J., Kutner, M. H., Nachtshiem, C. J. & Wasserman, W. Applied Linear Statistical Models 4th edn (Irwin, Chicago, Illinois, 1997).Detailed reference text on the use of linear models in applied regression.

    Google Scholar 

  58. Sourdille, P. et al. Linkage between RFLP markers and genes affecting kernel hardness in wheat. Theor. Appl. Genet. 93, 580–586 (1996).

    Article  CAS  PubMed  Google Scholar 

  59. Timmerman-Vaughan, G. A., McCallum, J. A., Frew, T. J., Weeden, N. F. & Russell, A. C. Linkage mapping of quantitative trait loci controlling seed weight in pea (Pisum sativum L.). Theor. Appl. Genet. 93, 431–439 (1996).

    Article  CAS  PubMed  Google Scholar 

  60. Varshney, M., Prasad, R., Kumar, N., Harjit-Singh, H. S. & Gupta, P. K. Identification of eight chromosomes and a microsatellite marker on 1AS associated with QTL for grain weight in bread wheat. Theor. Appl. Genet. 8, 1290–1294 (2000).

    Article  Google Scholar 

  61. Lander, E. S. & Green, P. Construction of multilocus genetic linkage maps in humans. Proc. Natl Acad. Sci. USA 84, 2363–2367 (1987).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Stam, P. Construction of integrated genetic linkage maps by means of a new computer package: Joinmap. Plant J. 5, 739–744 (1993).

    Article  Google Scholar 

  63. Bailey, D. W. The Mathematical Theory of Genetic Linkage (Clarendon Press, Oxford, UK, 1961).

    Google Scholar 

  64. Lincoln, S., Daly, M. & Lander, E. Constructing Genetic Maps with MAPMAKER/EXP 3.0 3rd edn (Whitehead Institute Technical Report, Cambridge, Massachusetts, 1992).Companion technical report and software to MAPMAKER/QTL.

    Google Scholar 

  65. Soller, M., Brody, T. & Genizi, A. On the power of experimental designs for the detection of linkage between marker loci and quantitative loci in crosses between inbred lines. Theor. Appl. Genet. 47, 35–39 (1979).One of the first papers proposing interval mapping.

    Article  Google Scholar 

  66. Soller, M., Brody, T. & Genizi, A. The expected distribution of marker-linked quantitative effects in crosses between inbred lines. Heredity 43, 179–190 (1979).

    Article  Google Scholar 

  67. Martinez, O. & Curnow, R. N. Estimating the locations and the size of the effects of quantitative trait loci using flanking markers. Theor. Appl. Genet. 85, 480–488 (1992).

    Article  CAS  PubMed  Google Scholar 

  68. Churchill, G. A. & Doerge, R. W. Empirical threshold values for quantitative trait mapping. Genetics 138, 963–971 (1994).Details the use of resampling through permutation to estimate experiment-specific QTL threshold values.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. QTL CARTOGRAPHER: A Reference Manual and Tutorial for QTL Mapping Department of Statistics, North Carolina State University, Raleigh, North Carolina (1995–2001).

  70. Jansen, R. C. Genetic Mapping of Quantitative Trait Loci in Plants – a Novel Statistical Approach. Ph.D. thesis, CIP–data Koninklijke Biblotheek, Den Haag, The Netherlands (1995).

    Google Scholar 

  71. Piepho, H. P. & Gauch, H. G. Marker pair selection for mapping quantitative trait loci. Genetics 157, 433–444 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Nakamichi, R., Ukai, Y. & Kishino, H. Detection of closely linked multiple quantitative trait loci using a genetic algorithm. Genetics 158, 463–475 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Sen, S. & Churchill, G. A. A statistical framework for quantitative trait mapping. Genetics 159, 371–387 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Green, P. Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika 82, 711–732 (1995).

    Article  Google Scholar 

  75. Carlborg, O., Andersson, L. & Kinghorn, B. The use of a genetic algorithm for simultaneous mapping of multiple interacting quantitative trait loci. Genetics 155, 2003–2010 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Foster, J. A. Evolutionary computation. Nature Rev. Genet. 428–436 (2001).

  77. Holland, J. Adaption in Natural and Artifical Systems (Univ. of Michigan Press, Ann Arbor, Michigan, 1975).

    Google Scholar 

  78. Baker, J. E. in Genetic Algorithms and their Applications, Proc. 2nd Intl Conf. (ed. Grefenstette, J. J.) 14–21 (LEA, Cambridge, Massachusetts, 1987).

    Google Scholar 

  79. Ghosh, J. K. & Sen, P. K. On the asymptotic performance of the log likelihood ratio statistic for the mixture model and related results. Proc. Berkeley Conf. 2, 789–807 (1985).

    Google Scholar 

  80. Hartigan, J. A. A failure of likelihood asymptotics for normal distributions. Proc. Berkeley Conf. 2, 807–810 (1985).

    Google Scholar 

  81. Self, S. G. & Liang, K.-Y. Asymptotic properties of maximum likelihood estimators and likelihood ratio tests under nonstandard conditions. J. Am. Stat. Assoc. 82, 605–610 (1987).

    Article  Google Scholar 

  82. Rebaï, A., Goffinet, B. & Mangin, B. Approximate thresholds of interval mapping tests for QTL detection. Genetics 138, 235–240 (1994).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  84. Efron, B. & Tibshirani, R. J. An Introduction to the Bootstrap (Chapman & Hall, New York, 1993).

    Book  Google Scholar 

  85. Good, I. P. Permutation Tests: a Practical Guide to Resampling Methods for Testing Hypothesis (Springer, New York, 2000).

    Book  Google Scholar 

  86. Piepho, H.-P. A quick method for computing approximate thresholds for quantitative trait loci detection. Genetics 157, 425–432 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Davies, R. B. Hypothesis testing when a nuisance parameter is present only under the alternative. Biometrika 64, 247–254 (1977).

    Article  Google Scholar 

  88. Davies, R. B. Hypothesis testing when a nuisance parameter is present only under the alternative. Biometrika 74, 33–53 (1987).

    Google Scholar 

  89. Van Ooijen, J. W. LOD significance thresholds for QTL analysis in experimental populations of diploid species. Heredity 83, 613–624 (1999).

    Article  PubMed  Google Scholar 

  90. Doerge, R. W. & Rebai, A. Significance thresholds for QTL mapping tests. Heredity 76, 459–464 (1996).

    Article  Google Scholar 

  91. Wilson, I. W., Schiff, C. L., Hughes, D. E. & Somerville, S. C. Quantitative trait loci analysis of powdery mildew disease resistance in the Arabidopsis thaliana accession Kashmir-1. Genetics 158, 1301–1309 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Kerr, M. K. & Churchill, G. A. Statistical design and the analysis of gene expression microarrays. Genet. Res. 77, 123–128 (2001).

    Article  CAS  PubMed  Google Scholar 

  93. Kerr, M. K., Martin, M. & Churchill, G. A. Analysis of variance for gene expression microarray data. J. Comput. Biol. 7, 819–837 (2001).

    Article  Google Scholar 

  94. Newton, M. A. et al. On differential variability of expression ratios: improving statistical inference about gene expression changes from microarray data. J. Comput. Biol. 8, 37–52 (2001).

    Article  CAS  PubMed  Google Scholar 

  95. Eisen, M. B., Spellman, P. T., Brown, P. O. & Botstein, D. Cluster analysis of genome-wide expression patterns. Proc. Natl Acad. Sci. USA 95, 14863–14868 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Holter, N. S. et al. Fundamental patterns underlying gene expression profiles: simplicity from complexity. Proc. Natl Acad. Sci. USA 97, 8409–8414 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Dudoit, S., Yang, Y. H., Callow, M. J. & Speed, T. P. Statistical Methods for Identifying Differentially Expressed Genes in Replicated cDNA Microarray Experiments. Technical Report #578, Stanford University (2000).

    Google Scholar 

  98. Munneke, B. Null Model Methods for Cluster Analysis of Gene Expression Data. Ph.D. thesis, Department of Statistics, Purdue University, West Lafayette, Indiana (2001).

    Google Scholar 

  99. Butterfield, R. J. et al. Genetic analysis of disease subtype and sexual dimorphism in mouse EAE: relapsing–remitting and monophasic remitting/non-relapsing EAE are immunogenetically distinct. J. Immunol. 162, 3096–3102 (1999).

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

This work is partially supported by the USDA-IFAFS. An extended thank you to the Teuscher group for allowing access and use of their data, and to the anonymous reviewers for many challenging opinions.

Author information

Authors and Affiliations

  1. Department of Statistics, and Department of Agronomy, and Computational Genomics, Purdue University, West Lafayette, 47907-1399, Indiana, USA

    Rebecca W. Doerge

Authors
  1. Rebecca W. Doerge
    View author publications

    You can also search for this author in PubMed Google Scholar

Related links

Related links

DATABASES

OMIM

multiple sclerosis

FURTHER INFORMATION

An alphabetical list of genetic analysis software

Minitab

QTL mapping software

QTL references

Quantitative genetics resources

SAS

Splus

Glossary

COMPLEX TRAIT

A trait determined by many genes, almost always interacting with environmental influences.

QUANTITATIVE TRAIT LOCUS

A genetic locus identified through the statistical analysis of complex traits (such as plant height or body weight). These traits are typically affected by more than one gene, and also by the environment.

EPISTASIS

In the broad sense used here, it refers to any genetic interaction in which the combined phenotypic effect of two or more loci exceeds the sum of effects at individual loci.

RECOMBINANT INBRED LINES

A population of fully homozygous individuals that is obtained by repeated selfing from an F1 hybrid, and that comprises ∼50% of each parental genome in different combinations.

SEGREGATION DISTORTION

The non-random segregation of alleles. Apparent segregation distortion can result from incorrect genotype classification.

CLOSED FORM ESTIMATE

An estimate of a population parameter that can be calculated directly from an equation to obtain an exact solution.

SIGNIFICANCE LEVEL

A probability for a test statistic that gives the maximum acceptable value of rejecting a 'true' null hypothesis.

GENETIC INTERFERENCE

The presence of a recombinational event in one region affects the occurrence of recombinational events in adjacent regions.

GHOST QTL

Quantitative trait locus (QTL) effects that occur as artefacts due to real QTL in surrounding intervals.

GENETIC ALGORITHM

Numerical optimization procedures based on evolutionary principles, such as mutation, deletion and selection.

DENDROGRAM

A branching 'tree' diagram that represents a hierarchy of categories on the basis of degree of similarity or number of shared characteristics, especially in biological taxonomy. The results of hierarchical clustering are presented as dendrograms, in which the distance along the tree from one element to the next represents their relative degree of similarity in terms of gene expression.

NON-PARAMETRIC

Statistical procedures that are not based on models, or assumptions pertaining to the distribution of the quantitative trait.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Doerge, R. Mapping and analysis of quantitative trait loci in experimental populations. Nat Rev Genet 3, 43–52 (2002). https://doi.org/10.1038/nrg703

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1038/nrg703

This article is cited by

Search

Advanced 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