Quantitative Trait Locus Mapping to Identify Genes for Complex Traits in Mice

  • Jonathan D. Smith
Part of the Springer Protocols Handbooks book series (SPH)

1. Introduction

Let us say you performed a survey of five inbred mouse strains by following their body weight over time after feeding them a high fat diet. You identify three strains that became obese, whereas two strains did not. How can you identify the genes that are responsible for the different outcomes of these strains? One can apply the method of quantitative trait locus (QTL) mapping to identify the chromosomal region (locus) of a gene, or genes, that have an effect on a trait. This mapping is the first step in the identification of the responsible gene by a method that is referred to as positional cloning. In this chapter, the focus will be on the use of QTL mapping to identify genes for complex traits in mice; although, QTL mapping can be applied to any experimental system in which there is meiotic recombination and different inbred strains are available. A complex trait is a phenotype, such as body weight, that is influenced by several genes and the environment. An inbred...


Quantitative Trait Locus Quantitative Trait Locus Analysis Quantitative Trait Locus Mapping Congenic Strain Quantitative Trait Locus Locus 
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. 1.
    Markel P, Shu P, Ebeling C et al (1997) Theoretical and empirical issues for marker-assisted breeding of congenic mouse strains. Nat Genet 17:280–284PubMedCrossRefGoogle Scholar
  2. 2.
    Smith JD, Bhasin JM, Baglione J et al (2006) Atherosclerosis susceptibility loci identified from a strain intercross of apolipoprotein E-deficient mice via a high-density genome scan. Arterioscler Thromb Vasc Biol 26:597–603PubMedCrossRefGoogle Scholar
  3. 3.
    Lander ES, Green P, Abrahamson J et al (1987) MAPMAKER: an interactive computer package for constructing primary genetic linkage maps of experimental and natural populations. Genomics 1:174–181PubMedCrossRefGoogle Scholar
  4. 4.
    Manly KF, Cudmore RH, Jr, Meer JM (2001) Map Manager QTX, cross-platform software for genetic mapping. Mamm Genome 12:930–932PubMedCrossRefGoogle Scholar
  5. 5.
    Broman KW, Wu H, Sen S, Churchill GA (2003) R/qtl: QTL mapping in experimental crosses. Bioinformatics 19:889–890PubMedCrossRefGoogle Scholar
  6. 6.
    Broman KW (2001) Review of statistical methods for QTL mapping in experimental crosses. Lab Anim (NY) 30:44–52Google Scholar
  7. 7.
    Lander E, Kruglyak L (1995) Genetic dissection of complex traits: guidelines for interpreting and reporting linkage results. Nat Genet 11:241–247PubMedCrossRefGoogle Scholar
  8. 8.
    Churchill GA, Doerge RW (1994) Empirical threshold values for quantitative trait mapping. Genetics 138:963–971PubMedGoogle Scholar
  9. 9.
    Green EL (1966) in Biology of the Laboratory Mouse. McGraw-Hill, New York, N.Y.Google Scholar
  10. 10.
    Churchill GA, Airey DC, Allayee H et al (2004) The Collaborative Cross, a community resource for the genetic analysis of complex traits. Nat Genet 36: 1133–1137PubMedCrossRefGoogle Scholar
  11. 11.
    Singer JB, Hill AE, Burrage LC et al (2004) Genetic dissection of complex traits with chromosome substitution strains of mice. Science 304:445–448PubMedCrossRefGoogle Scholar
  12. 12.
    Davis RC, Schadt EE, Smith D J et al (2005) A genome-wide set of congenic mouse strains derived from DBA/2J on a C57BL/6J background. Genomics 86:259–270PubMedCrossRefGoogle Scholar
  13. 13.
    Glazier AM, Nadeau JH, Aitman TJ (2002) Finding genes that underlie complex traits. Science 298:2345–2349PubMedCrossRefGoogle Scholar
  14. 14.
    Abiola O, Angel JM, Avner P et al (2003) The nature and identification of quantitative trait loci: a community's view. Nat Rev Genet 4:911–916PubMedGoogle Scholar
  15. 15.
    Nord AS, Chang PJ, Conklin BR et al (2006) The International Gene Trap Consortium Website: a portal to all publicly available gene trap cell lines in mouse. Nucleic Acids Res 34:D642–D648PubMedCrossRefGoogle Scholar
  16. 16.
    Kissler S, Stern P, Takahashi K et al (2006) In vivo RNA interference demonstrates a role for Nrampl in modifying susceptibility to type 1 diabetes. Nat. Genet 38:479–483PubMedCrossRefGoogle Scholar
  17. 17.
    Aitman TJ, Glazier AM, Wallace CA et al (1999) Identification of Cd36 (Fat) as an insulin-resistance gene causing defective fatty acid and glucose metabolism in hypertensive rats. Nat Genet 21:76–83PubMedCrossRefGoogle Scholar
  18. 18.
    Trogan E, Choudhury R P, Dansky HM et al (2002) Laser capture microdissection analysis of gene expression in macrophages from atherosclerotic lesions of apoli-poprotein E-deficient mice. Proc Natl Acad Sci U. S. A 99:2234–2239PubMedCrossRefGoogle Scholar
  19. 19.
    Schadt EE, Monks SA, Drake TA et al (2003) Genetics of gene expression surveyed in maize, mouse and man. Nature 422:297–302PubMedCrossRefGoogle Scholar
  20. 20.
    Brem RB, Yvert G, Clinton R, Kruglyak L (2002) Genetic dissection of transcriptional regulation in budding yeast. Science 296:752–755PubMedCrossRefGoogle Scholar
  21. 21.
    Hubner N, Wallace CA, Zimdahl H et al (2005) Integrated transcriptional profiling and linkage analysis for identification of genes underlying disease. Nat Genet 37:243–353PubMedCrossRefGoogle Scholar
  22. 22.
    Kroymann J, Mitchell-Olds T (2005) Epistasis and balanced polymorphism influencing complex trait variation. Nature 435:95–98PubMedCrossRefGoogle Scholar
  23. 23.
    Vitt U, Gietzen D, Stevens K et al (2004) Identification of candidate disease genes by EST alignments, synteny, and expression and verification of Ensembl genes on rat chromosome 1q43–54. Genome Res 14:640–50PubMedCrossRefGoogle Scholar
  24. 24.
    Wang X, Ishimori N, Korstanje R et al (2005) Identifying novel genes for atherosclerosis through mouse-human comparative genetics. Am J Hum Genet 77:1–15PubMedCrossRefGoogle Scholar
  25. 25.
    Stoll M, Kwitek-Black AE, Cowley AW, Jr et al (2000) New target regions for human hypertension via comparative genomics. Genome Res. 10:473–82PubMedCrossRefGoogle Scholar
  26. 26.
    Wang X, Ria M, Kelmenson PM et al (2005) Positional identification of TNFSF4, encoding OX40 ligand, as a gene that influences atherosclerosis susceptibility. Nat Genet 37:365–372PubMedCrossRefGoogle Scholar
  27. 27.
    Mehrabian M, Allayee H, Wong J et al (2002) Identification of 5-lipoxygenase as a major gene contributing to atherosclerosis susceptibility in mice. Circ Res 91:120–126PubMedCrossRefGoogle Scholar
  28. 28.
    Helgadottir A, Manolescu A, Thorleifsson G et al (2004) The gene encoding 5-lipoxygenase activating protein confers risk of myocardial infarction and stroke. Nat Genet 36:233–239PubMedCrossRefGoogle Scholar

Copyright information

© Humana Press, a part of Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Jonathan D. Smith
    • 1
  1. 1.Department of Cell BiologyCleveland Clinic FoundationCleveland

Personalised recommendations