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Finding the molecular basis of quatitative traits: successes and pitfalls

Nature Reviews Genetics volume 2, pages 437–445 (2001)Cite this article

Key Points

  • Although quantitative trait loci (QTL) have been successfully mapped in plants, insects and rodents, attempts to identify the genes that underlie quantitative traits have been frustrated by their complex genetic architecture. However, new mapping and technical approaches in several model organisms are helping to identify the molecular bases of QTL.

  • The successful mapping of QTL in inbred organisms has not been followed by the identification of the variants that underlie QTL for several reasons, such as:

  • inbred organisms contain only a fraction of the genetic variation present in the original populations from which they were derived;

  • QTL detection experiments do not map a gene, but a genetic effect that might consist of many linked genes. A single, large-effect locus might therefore contain several small-effect genes or genes that have opposite effects on a phenotype;

  • QTL detection experiments do not detect epistatic interactions between loci.

  • Fine mapping can reduce the interval to which a QTL maps. In plants, many F2 progeny are used to achieve this, but this is impractical in other species. Taking advantage of the recombinants that accumulate with each new generation can overcome this problem — an approach that has been successfully used to map QTL in cattle and mice. This approach also underlies human association studies.

  • High-resolution mapping and mutagenesis deliver candidate genes for QTL analysis, but proving the involvement of a candidate is still problematic. One approach to this is quantitative complementation testing, which has been successfully used in flies.

  • Expression profiling experiments might also provide candidate genes for QTL analysis, but as medium- and small-effect loci are unlikely to bring about large changes in gene expression, the usefulness of this approach remains to be shown.

  • Scientific interest and medical need demand that we identify the molecular bases of QTL. High-resolution mapping, high-throughput genotyping and whole genome sequences deliver candidate genes that map to a QTL but it remains difficult to quickly identify those that have the greatest likelihood of influencing the phenotype being studied. However, emerging technologies offer ways towards the solution of this problem.

Abstract

Understanding the molecular basis of quantitative genetic variation is a principal goal for biomedicine. Although the complex genetic architecture of quantitative traits has so far largely frustrated attempts to identify genes in humans by standard linkage methodologies, quantitative trait loci (QTL) have been mapped in plants, insects and rodents. However, identifying the molecular bases of QTL remains a challenge. Here, we discuss why this is and how new experimental strategies and analytical techniques, combined with the fruits of the genome projects, are beginning to identify candidate genes for QTL studies in several model organisms.

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Figure 1: Quantitative trait loci detection in inbred strain crosses.
Figure 2: The direction of allelic effects misleads quantitative trait loci detection.

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References

  1. Barton, N. H. & Turell, M. Evolutionary quantitative genetics. Annu. Rev. Genet. 23, 337–370 (1989).

    Article  CAS  PubMed  Google Scholar 

  2. Thoday, J. M. in Quantitative Genetic Variation (eds Thompson, J. N. & Thoday, J. M.) (Academic, New York, 1979) 219 –233 .

    Book  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  4. Lander, E. S. & Schork, N. J. Genetic dissection of complex traits. Science 265, 2037–2048 (1994).

    Article  CAS  PubMed  Google Scholar 

  5. Paterson, A. et al. Resolution of quantitative traits into Mendelian factors using a complete RFLP linkage map. Nature 335, 721–726 (1988).

    Article  CAS  PubMed  Google Scholar 

  6. Todd, J. A. et al. Genetic analysis of autoimmune type 1 diabetes mellitus in mice. Nature 351, 542–547 (1991).

    Article  CAS  PubMed  Google Scholar 

  7. Hilbert, P. et al. Chromosomal mapping of two genetic loci associated with blood-pressure regulation in hereditary hypertensive rats. Nature 353, 521–529 (1991).

    Article  CAS  PubMed  Google Scholar 

  8. Jacob, H. et al. Genetic mapping of a gene causing hypertension in the stroke-prone spontaneously hypertensive rat. Cell 67, 213–224 (1991).References 6 – 8 were the first studies to show QTL mapping using the genome-wide sets of polymorphic markers in crosses between inbred rodents.

    Article  CAS  PubMed  Google Scholar 

  9. Cardon, L. R. & Bell, J. I. Association study designs for complex diseases. Nature Rev. Genet. (2001).

  10. Risch, N. J. Searching for genetic determinants in the new millennium. Nature 405, 847–856 (2000).References 9 and 10 are excellent reviews of the problems of mapping complex traits in humans.

    Article  CAS  PubMed  Google Scholar 

  11. Podolin, P. L. et al. Localization of two insulin-dependent diabetes (Idd) genes to the Idd10 region on mouse chromosome 3. Mamm. Genome 9, 283–286 (1998).

    Article  CAS  PubMed  Google Scholar 

  12. Legare, M. E., Bartlett, F. S. & Frankel, W. N. A major effect QTL determined by multiple genes in epileptic EL mice. Genome Res. 10, 42–48 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Darvasi, A. & Soller, M. A simple method to calculate resolving power and confidence interval of QTL map location. Behav. Genet. 27, 125–132 (1997).

    Article  CAS  PubMed  Google Scholar 

  14. Cohen, B. A., Mitra, R. D., Hughes, J. D. & Church, G. M. A computational analysis of whole-genome expression data reveals chromosomal domains of gene expression. Nature Genet. 26, 183–186 (2000).

    Article  CAS  PubMed  Google Scholar 

  15. Frankel, W. N. & Schork, N. J. Who's afraid of epistasis? Nature Genet. 14, 371–373 (1996).

    Article  CAS  PubMed  Google Scholar 

  16. Gurganus, M. C., Nuzhdin, S. V., Leips, J. W. & Mackay, T. F. High-resolution mapping of quantitative trait loci for sternopleural bristle number in Drosophila melanogaster. Genetics 152, 1585–1604 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Long, A. D. et al. High resolution mapping of genetic factors affecting abdominal bristle number in Drosophila melanogaster. Genetics 139, 1273–1291 (1995).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Lukens, L. N. & Doebley, J. Epistatic and environmental interactions for quantitative trait loci involved in maize evolution. Genet. Res. 74, 291–302 (1999).

    Article  CAS  Google Scholar 

  19. Lark, K. G., Chase, K., Adler, F., Mansur, L. M. & Orf, J. H. Interactions between quantitative trait loci in soybean in which trait variation at one locus is conditional upon a specific allele at another. Proc. Natl Acad. Sci. USA 92, 4656–4660 (1995).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Fijneman, R. J., de Vries, S. S., Jansen, R. C. & Demant, P. Complex interactions of new quantitative trait loci, Sluc1, Sluc2, Sluc3, and Sluc4, that influence the susceptibility to lung cancer in the mouse. Nature Genet. 14, 465–467 (1996).An excellent demonstration of the importance of epistasis and the need to design studies specifically to detect its effect.

    Article  CAS  PubMed  Google Scholar 

  21. Van Wezel, T. et al. Gene interaction and single gene effects in colon tumour susceptibility in mice. Nature Genet. 14, 468–470 (1996).

    Article  CAS  PubMed  Google Scholar 

  22. Van Wezel, T., Ruivenkamp, C. A., Stassen, A. P., Moen, C. J. & Demant, P. Four new colon cancer susceptibility loci, Scc6 to Scc9 in the mouse. Cancer Res. 59, 4216–4218 (1999).

    CAS  PubMed  Google Scholar 

  23. Rikke, B. A. & Johnson, T. E. Towards the cloning of genes underlying murine QTLs. Mamm. Genome 9, 963–968 (1998).

    Article  CAS  PubMed  Google Scholar 

  24. Darvasi, A. Experimental strategies for the genetic dissection of complex traits in animal models. Nature Genet. 18, 19–24 (1998).An outstanding review of QTL mapping in animals.

    Article  CAS  PubMed  Google Scholar 

  25. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Fridman, E., Pleban, T. & Zamir, D. A recombination hotspot delimits a wild-species quantitative trait locus for tomato sugar content to 484 bp within an invertase gene. Proc. Natl Acad. Sci. USA 97, 4718–4723 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Denny, P. et al. Mapping of the IDDM locus Idd3 to a 0.35-cM interval containing the interleukin-2 gene. Diabetes 46, 695–700 (1997).

    Article  CAS  PubMed  Google Scholar 

  28. Hanson, W. D. Theoretical distribution of the initial linkage block lengths intact in the gametes of a population intermarried for n generations. Genetics 44, 839–846 (1959).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Hanson, W. D. The breakup of initial linkage blocks under selected mating systems. Genetics 44, 857–868 (1959).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Darvasi, A. & Soller, M. Advanced intercross lines, an experimental population for fine genetic mapping. Genetics 141, 1199–1207 (1995).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Xiong, M. I. & Guo, S. W. Fine-scale mapping of quantitative trait loci using historical recombinations. Genetics 145, 1201–1218 (1997).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Haley, C. S. Livestock QTLs — bringing home the bacon? Trends Genet. 11, 488–492 (1995).

    Article  CAS  PubMed  Google Scholar 

  33. Riquet, J. et al. Fine-mapping of quantitative trait loci by identity by descent in outbred populations: application to milk production in dairy cattle. Proc. Natl Acad. Sci. USA 96, 9252–9257 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Coppieters, W. et al. A QTL with major effect on milk yield and composition maps to bovine chromosome 14. Mamm. Genome 9, 540–544 (1998).

    Article  CAS  PubMed  Google Scholar 

  35. Iraqi, F. et al. Fine mapping of trypanosomiasis resistance loci in murine advanced intercross lines. Mamm. Genome 11, 645–648 (2000).

    Article  CAS  PubMed  Google Scholar 

  36. Talbot, C. J. et al. High-resolution mapping of quantitative trait loci in outbred mice. Nature Genet. 21, 305–308 (1999).

    Article  CAS  PubMed  Google Scholar 

  37. Mott, R., Talbot, C., Turri, M., Collins, A. C. & Flint, J. A new method for fine mapping quantitative trait loci in outbred animal stocks. Proc. Natl Acad. Sci. USA 97, 12649–12654 (2000).References 36 and 37 introduced QTL mapping in outbred animals to mouse genetics.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Lyman, R. F., Lai, C. & Mackay, T. F. Linkage disequilibrium mapping of molecular polymorphisms at the scabrous locus associated with naturally occurring variation in bristle number in Drosophila melanogaster. Genet. Res. 74, 303–311 (1999).

    Article  CAS  PubMed  Google Scholar 

  39. Wang, R. L., Stec, A., Hey, J., Lukens, L. & Doebley, J. The limits of selection during maize domestication. Nature 398, 236–239 (1999).

    Article  CAS  PubMed  Google Scholar 

  40. Higgs, D. R. et al. A major positive regulatory region located far upstream of the human α-globin gene locus. Genes Dev. 4, 1588–1601 (1990).

    Article  CAS  PubMed  Google Scholar 

  41. Symula, D. J. et al. Functional screening of an asthma QTL in YAC transgenic mice. Nature Genet. 23, 241–244 (1999).The first attempt to use large insert YAC clones to identify QTL. The method relies on the addition of exogenous DNA and is thus a departure from tests in which the animal is diploid at the QTL.

    Article  CAS  PubMed  Google Scholar 

  42. Cormier, R. T. et al. Secretory phospholipase Pla2g2a confers resistance to intestinal tumorigenesis. Nature Genet. 17, 88–91 (1997).One of the first attempts to use functional complementation to identify a QTL in mice.

    Article  CAS  PubMed  Google Scholar 

  43. Aitman, T. J. et al. Identification of Cd36 (Fat) as an insulin-resistance gene causing defective fatty acid and glucose metabolism in hypertensive rats. Nature Genet. 21, 76–83 (1999). The first use of combined mapping and expression profiling to identify a QTL in animals.

    Article  CAS  PubMed  Google Scholar 

  44. Frary, A. et al. fw2.2: a quantitative trait locus key to the evolution of tomato fruit size. Science 289, 85–88 (2000).The first QTL to be cloned by positional information and complementation.

    Article  CAS  PubMed  Google Scholar 

  45. Monforte, A. J. & Tanksley, S. D. Development of a set of near isogenic and backcross recombinant inbred lines containing most of the Lycopersicon hirsutum genome in a L. esculentum genetic background: a tool for gene mapping and gene discovery. Genome 43, 803–813 (2000).

    Article  CAS  PubMed  Google Scholar 

  46. Mackay, T. F. Quantitative trait loci in Drosophila. Nature Rev. Genet. 2, 11–20 (2001).An excellent review of the approaches available to Drosophila geneticists for cloning QTL.

    Article  CAS  PubMed  Google Scholar 

  47. Zheng, B. et al. Engineering a mouse balancer chromosome. Nature Genet. 22, 375–378 (1999).

    Article  CAS  PubMed  Google Scholar 

  48. Su, H., Wang, X. & Bradley, A. Nested chromosomal deletions induced with retroviral vectors in mice. Nature Genet. 24, 92–95 (2000).

    Article  CAS  PubMed  Google Scholar 

  49. Ramirez-Solis, R., Liu, P. & Bradley, A. Chromosome engineering in mice. Nature 378, 720–724 (1995).References 47 – 49 show that the genetic reagents available to fly geneticists can also be generated for use in mice.

    Article  CAS  PubMed  Google Scholar 

  50. Wakayama, T., Tateno, H., Mombaerts, P. & Yanagimachi, R. Nuclear transfer into mouse zygotes. Nature Genet. 24, 108–109 (2000).

    Article  CAS  PubMed  Google Scholar 

  51. Rideout, W. M. et al. Generation of mice from wild-type and targeted ES cells by nuclear cloning. Nature Genet. 24, 109–110 (2000).

    Article  CAS  PubMed  Google Scholar 

  52. Loots, G. G. et al. Identification of a coordinate regulator of interleukins 4, 13, and 5 by cross-species sequence comparisons. Science 288, 136–140 (2000).

    Article  CAS  PubMed  Google Scholar 

  53. Karp, C. L. et al. Identification of complement factor 5 as a susceptibility locus for experimental allergic asthma. Nature Immunol. 1, 221–226 (2000).

    Article  CAS  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  55. Castensson, A., Emilsson, L., Preece, P. & Jazin, E. E. High-resolution quantification of specific mRNA levels in human brain autopsies and biopsies. Genome Res. 10, 1219–1229 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Horikawa, Y. et al. Genetic variation in the gene encoding calpain-10 is associated with type 2 diabetes mellitus. Nature Genet. 26, 163–175 (2000).

    Article  CAS  PubMed  Google Scholar 

  57. Mackay, T. F. & Langley, C. H. Molecular and phenotypic variation in the achaete-scute region of Drosophila melanogaster. Nature 348, 64–66 (1990).

    Article  CAS  PubMed  Google Scholar 

  58. Lai, C., Lyman, R. F., Long, A. D., Langley, C. H. & Mackay, T. F. Naturally occurring variation in bristle number and DNA polymorphisms at the scabrous locus of Drosophila melanogaster. Science 266, 1697–1702 (1994).

    Article  CAS  PubMed  Google Scholar 

  59. Long, A. D., Mullaney, S. L., Mackay, T. F. C. & Langley, C. H. Genetic interactions between naturally occurring alleles at quantitative trait loci and mutant alleles at candidate loci affecting bristle number in Drosophila melanogaster. Genetics 144, 1497–1510 (1996).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Long, A. D., Lyman, R. F., Morgan, A. H., Langley, C. H. & Mackay, T. F. Both naturally occurring insertions of transposable elements and intermediate frequency polymorphisms at the achaete-scute complex are associated with variation in bristle number in Drosophila melanogaster. Genetics 154, 1255–1269 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Lynch, M. & Walsh, B. Genetics and Analysis of Quantitative Traits (Sinauer Associates, Sunderland, Massachusetts, 1998).All you could wish to know about the statistical analysis of QTL mapping. A classic textbook on quantitative genetics.

    Google Scholar 

  62. Falconer, D. S. & Mackay, T. F. C. Quantitative Genetics (Longman, Harlow, UK, 1996). A classic textbook on quantitative genetics.

    Google Scholar 

  63. Brown, S. D. & Nolan, P. M. Mouse mutagenesis — systematic studies of mammalian gene function. Hum. Mol. Genet. 7, 1627–1633 (1998).

    Article  CAS  PubMed  Google Scholar 

  64. Hunter, A. J., Nolan, P. M. & Brown, S. D. Towards new models of disease and physiology in the neurosciences: the role of induced and naturally occurring mutations. Hum. Mol. Genet. 9, 893–900 (2000).

    Article  CAS  PubMed  Google Scholar 

  65. Nadeau, J. H. & Frankel, W. N. The roads from phenotypic variation to gene discovery: mutagenesis versus QTLs. Nature Genet. 25, 381–384 (2000).This article argues that mutagenesis is the way ahead for the genetic dissection of complex traits.

    Article  CAS  PubMed  Google Scholar 

  66. Comuzzie, A. G. & Allison, D. B. The search for human obesity genes. Science 280, 1374–1377 (1998).

    Article  CAS  PubMed  Google Scholar 

  67. Hager, J. et al. A genome-wide scan for human obesity genes reveals a major susceptibility locus on chromosome 10. Nature Genet. 20, 304–308 (1998).

    Article  CAS  PubMed  Google Scholar 

  68. Jackson, R. S. et al. Obesity and impaired prohormone processing associated with mutations in the human prohormone convertase 1 gene. Nature Genet. 16, 303–306 (1997).

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

J. F. and R. M. are supported by the Wellcome Trust.

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Authors and Affiliations

  1. Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, OX3 7BN, Oxford, UK

    Jonathan Flint & Richard Mott

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DATABASE LINKS

Scc4

Scc5

Apc

Mom1

Pla2g2a

Cd36

C5

type 2 diabetes mellitus

sca

Dl

ASC

FURTHER INFORMATION

Holstein Friesian cattle breed

Trudy Mackay

Chromosome engineering and mutagenesis

Wellcome Trust Centre for Human Genetics

Glossary

QUANTITATIVE VARIATION

Variability that is measured on a numerical scale, for example height or litter size, in contrast to categorical variation, such as blood group or sex.

QUANTITATIVE TRAIT LOCI

Loci that contribute to a quantitative trait.

VARIANCE

A measure of the variation around the central class of a distribution (the average squared deviation of the observations from their mean value).

ASSOCIATION TESTING

A statistical approach to test for an association between diseases and marker or candidate gene alleles.

CONGENIC

A strain produced by a breeding strategy that delineates a genomic region that contains a trait locus. Recombinants between two inbred strains are backcrossed to produce a strain that carries a single segment from one strain on the genetic background of the other.

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.

LOGARITHM OF ODDS (LOD) SCORE

The logarithm of the ratio between likelihoods under the null and alternative hypotheses.

GENETIC DRIFT

The random fluctuation in allele frequencies as genes are transmitted from one generation to the next.

MULTIPOINT MAPPING

A statistical technique for mapping genetic loci that combines information from nearby genetic markers to increase the power to detect genetic association over that possible when each marker is analysed separately.

FUNCTIONAL COMPLEMENTATION

Physical addition of complementary wild-type DNA into the nucleus to restore the effect of a mutation in the host DNA.

QUANTITATIVE COMPLEMENTATION

A complementation test that seeks a quantitative change in a phenotype in the presence of the experimental DNA rather than a restoration of function.

DEFICIENCY STOCKS

Strains of flies with well-characterized chromosomal deletions.

BALANCER CHROMOSOME

Chromosomes with inverted segments that suppress recombination. They are used as a genetic reagent as they allow lethal mutations to be maintained without selection.

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Flint, J., Mott, R. Finding the molecular basis of quatitative traits: successes and pitfalls . Nat Rev Genet 2, 437–445 (2001). https://doi.org/10.1038/35076585

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