Positional Gene Cloning in Experimental Populations

  • Maja JagodicEmail author
  • Pernilla Stridh
Part of the Methods in Molecular Biology book series (MIMB, volume 1304)


Positional cloning is a technique that identifies a trait-associated gene based on its location in the genome and involves methods such as linkage analysis, association mapping, and bioinformatics. This approach can be used for gene identification even when little is known about the molecular basis of the trait. Vast majority of traits are regulated by multiple genomic loci called quantitative trait loci (QTL). We describe experimental populations and designs that can be used for positional cloning, including backcrosses, intercrosses, and heterogeneous stocks, and advantages and disadvantages of different approaches. Once the phenotype and genotype of each individual in an experimental population have been determined, QTL identification can be accomplished. We describe the statistical tools used to identify the existence, location, and significance of QTLs. These different methods have advantages and disadvantages to consider when selecting the appropriate model to be used, which is briefly discussed.

Although the objective of QTL mapping is to identify genomic regions associated with a trait, the ultimate goal is to identify the gene and the genetic variation (which is often quantitative trait nucleotide, QTN) or haplotype that is responsible for the phenotype. By discovering the function of causative variants or haplotypes we can understand the molecular changes that lead to the phenotype. We briefly describe how the genomic sequences can be exploited to identify QTNs and how these can be validated in congenic strains and functionally tested to understand their influence on phenotype expression.


Positional cloning Gene identification Linkage analysis Association mapping Quantitative trait loci (QTL) Quantitative trait nucleotide (QTN) Intercross Backcross Advanced intercross line Heterogeneous stock Inbred and congenic strains 


  1. 1.
    Darvasi A (1998) Experimental strategies for the genetic dissection of complex traits in animal models. Nat Genet 18:19–24PubMedCrossRefGoogle Scholar
  2. 2.
    Stridh P, Ruhrmann S, Bergman P, Thessen Hedreul M, Flytzani S, Beyeen AD, Gillett A, Krivosija N, Ockinger J, Ferguson-Smith AC, Jagodic M (2014) Parent-of-origin effects implicate epigenetic regulation of experimental autoimmune encephalomyelitis and identify imprinted Dlk1 as a novel risk gene. PLoS Genet 10:e1004265PubMedCentralPubMedCrossRefGoogle Scholar
  3. 3.
    Darvasi A, Soller M (1995) Advanced intercross lines, an experimental population for fine genetic mapping. Genetics 141:1199–1207PubMedCentralPubMedGoogle Scholar
  4. 4.
    Hansen C, Spuhler K (1984) Development of the National Institutes of Health genetically heterogeneous rat stock. Alcohol Clin Exp Res 8:477–479PubMedCrossRefGoogle Scholar
  5. 5.
    Demarest K, Koyner J, McCaughran J Jr, Cipp L, Hitzemann R (2001) Further characterization and high-resolution mapping of quantitative trait loci for ethanol-induced locomotor activity. Behav Genet 31:79–91PubMedCrossRefGoogle Scholar
  6. 6.
    Caballero A, Toro MA (2000) Interrelations between effective population size and other pedigree tools for the management of conserved populations. Genet Res 75:331–343PubMedCrossRefGoogle Scholar
  7. 7.
    Valdar W, Solberg LC, Gauguier D, Burnett S, Klenerman P, Cookson WO, Taylor MS, Rawlins JN, Mott R, Flint J (2006) Genome-wide genetic association of complex traits in heterogeneous stock mice. Nat Genet 38:879–887PubMedCrossRefGoogle Scholar
  8. 8.
    Mott R, Flint J (2002) Simultaneous detection and fine mapping of quantitative trait loci in mice using heterogeneous stocks. Genetics 160:1609–1618PubMedCentralPubMedGoogle Scholar
  9. 9.
    Broman KW (2001) Review of statistical methods for QTL mapping in experimental crosses. Lab Anim 30:44–52Google Scholar
  10. 10.
    Lander ES, Botstein D (1989) Mapping mendelian factors underlying quantitative traits using RFLP linkage maps. Genetics 121:185–199PubMedCentralPubMedGoogle Scholar
  11. 11.
    Haley CS, Knott SA (1992) A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. Heredity 69:315–324PubMedCrossRefGoogle Scholar
  12. 12.
    Lander E, Kruglyak L (1995) Genetic dissection of complex traits: guidelines for interpreting and reporting linkage results. Nat Genet 11:241–247PubMedCrossRefGoogle Scholar
  13. 13.
    Churchill GA, Doerge RW (1994) Empirical threshold values for quantitative trait mapping. Genetics 138:963–971PubMedCentralPubMedGoogle Scholar
  14. 14.
    Marta M, Stridh P, Becanovic K, Gillett A, Ockinger J, Lorentzen JC, Jagodic M, Olsson T (2010) Multiple loci comprising immune-related genes regulate experimental neuroinflammation. Genes Immun 11:21–36PubMedCrossRefGoogle Scholar
  15. 15.
    Broman KW, Wu H, Sen S, Churchill GA (2003) R/qtl: QTL mapping in experimental crosses. Bioinformatics 19:889–890PubMedCrossRefGoogle Scholar
  16. 16.
    Mott R, Talbot CJ, Turri MG, Collins AC, Flint J (2000) A method for fine mapping quantitative trait loci in outbred animal stocks. Proc Natl Acad Sci U S A 97:12649–12654PubMedCentralPubMedCrossRefGoogle Scholar
  17. 17.
    Ball RD (2001) Bayesian methods for quantitative trait loci mapping based on model selection: approximate analysis using the Bayesian information criterion. Genetics 159:1351–1364PubMedCentralPubMedGoogle Scholar
  18. 18.
    Broman KW, Speed TP (2002) A model selection approach for the identification of quantitative trait loci in experimental crosses. J Roy Stat Soc B Stat Meth 64:641–656CrossRefGoogle Scholar
  19. 19.
    Sillanpaa MJ, Corander J (2002) Model choice in gene mapping: what and why. Trends Genet 18:301–307PubMedCrossRefGoogle Scholar
  20. 20.
    Kang HM, Zaitlen NA, Wade CM, Kirby A, Heckerman D, Daly MJ, Eskin E (2008) Efficient control of population structure in model organism association mapping. Genetics 178:1709–1723PubMedCentralPubMedCrossRefGoogle Scholar
  21. 21.
    Wakeland E, Morel L, Achey K, Yui M, Longmate J (1997) Speed congenics: a classic technique in the fast lane (relatively speaking). Immunol Today 18:472–477PubMedCrossRefGoogle Scholar
  22. 22.
    Jagodic M, Colacios C, Nohra R, Dejean AS, Beyeen AD, Khademi M, Casemayou A, Lamouroux L, Duthoit C, Papapietro O, Sjoholm L, Bernard I, Lagrange D, Dahlman I, Lundmark F, Oturai AB, Soendergaard HB, Kemppinen A, Saarela J, Tienari PJ, Harbo HF, Spurkland A, Ramagopalan SV, Sadovnick DA, Ebers GC, Seddighzadeh M, Klareskog L, Alfredsson L, Padyukov L, Hillert J, Clanet M, Edan G, Fontaine B, Fournie GJ, Kockum I, Saoudi A, Olsson T (2009) A role for VAV1 in experimental autoimmune encephalomyelitis and multiple sclerosis. Sci Transl Med 1:10ra21PubMedGoogle Scholar
  23. 23.
    Rat Genome S, Mapping C, Baud A, Hermsen R, Guryev V, Stridh P, Graham D, McBride MW, Foroud T, Calderari S, Diez M, Ockinger J, Beyeen AD, Gillett A, Abdelmagid N, Guerreiro-Cacais AO, Jagodic M, Tuncel J, Norin U, Beattie E, Huynh N, Miller WH, Koller DL, Alam I, Falak S, Osborne-Pellegrin M, Martinez-Membrives E, Canete T, Blazquez G, Vicens-Costa E, Mont-Cardona C, Diaz-Moran S, Tobena A, Hummel O, Zelenika D, Saar K, Patone G, Bauerfeind A, Bihoreau MT, Heinig M, Lee YA, Rintisch C, Schulz H, Wheeler DA, Worley KC, Muzny DM, Gibbs RA, Lathrop M, Lansu N, Toonen P, Ruzius FP, de Bruijn E, Hauser H, Adams DJ, Keane T, Atanur SS, Aitman TJ, Flicek P, Malinauskas T, Jones EY, Ekman D, Lopez-Aumatell R, Dominiczak AF, Johannesson M, Holmdahl R, Olsson T, Gauguier D, Hubner N, Fernandez-Teruel A, Cuppen E, Mott R, Flint J (2013) Combined sequence-based and genetic mapping analysis of complex traits in outbred rats. Nat Genet 45:767–775CrossRefGoogle Scholar
  24. 24.
    Serikawa T, Mashimo T, Takizawa A, Okajima R, Maedomari N, Kumafuji K, Tagami F, Neoda Y, Otsuki M, Nakanishi S, Yamasaki K, Voigt B, Kuramoto T (2009) National BioResource project-rat and related activities. Exp Anim 58:333–341PubMedCrossRefGoogle Scholar
  25. 25.
    Darvasi A, Soller M (1997) A simple method to calculate resolving power and confidence interval of QTL map location. Behav Genet 27:125–132PubMedCrossRefGoogle Scholar
  26. 26.
    Hospital F (2005) Selection in backcross programmes. Philos Trans R Soc Lond B Biol Sci 360:1503–1511PubMedCentralPubMedCrossRefGoogle Scholar
  27. 27.
    Cui Y, Cheverud JM, Wu R (2007) A statistical model for dissecting genomic imprinting through genetic mapping. Genetica 130:227–239PubMedCrossRefGoogle Scholar
  28. 28.
    Zou F (2009) QTL mapping in intercross and backcross populations. Methods Mol Biol 573:157–173PubMedCrossRefGoogle Scholar
  29. 29.
    Hitzemann B, Dains K, Kanes S, Hitzemann R (1994) Further studies on the relationship between dopamine cell density and haloperidol-induced catalepsy. J Pharmacol Exp Ther 271:969–976PubMedGoogle Scholar
  30. 30.
    Valdar W, Holmes CC, Mott R, Flint J (2009) Mapping in structured populations by resample model averaging. Genetics 182:1263–1277PubMedCentralPubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Department of Clinical Neuroscience, Center for Molecular MedicineKarolinska InstitutetStockholmSweden

Personalised recommendations