Model-Free Linkage Analysis of a Quantitative Trait

Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1666)

Abstract

Model-free methods of linkage analysis for quantitative traits are a class of easily implemented, computationally efficient and statistically robust approaches to searching for linkage to a quantitative trait. By “model-free” we refer to methods of linkage analysis that do not fully specify a genetic model (i.e., the causal allele frequency, and penetrance functions). In this chapter we briefly survey the methods that are available, and then we discuss the necessary steps to implement an analysis using the programs GENIBD, SIBPAL and RELPAL in the S.A.G.E. (Statistical Analysis for Genetic Epidemiology) software suite.

References

  1. 1.
    Haseman JK, Elston RC (1972) The investigation of linkage between a quantitative trait and a marker locus. Behav Genet 2:3–19CrossRefPubMedGoogle Scholar
  2. 2.
    Elston RC, Buxbaum S, Jacobs KB, Olson JM (2000) Haseman and Elston revisited. Genet Epidemiol 19:1–17CrossRefPubMedGoogle Scholar
  3. 3.
    Shete S, Jacobs KB, Elston RC (2003) Adding further power to the haseman and elston method for detecting linkage in larger sibships: weighting sums and differences. Hum Hered 55:79–85CrossRefPubMedGoogle Scholar
  4. 4.
    Wang T, Elston RC (2004) A modified revisited Haseman-Elston method to further improve power. Hum Hered 57:109–116CrossRefPubMedGoogle Scholar
  5. 5.
    Amos CI (1994) Robust variance-components approach for assessing genetic linkage in pedigrees. Am J Hum Genet 54:535–543PubMedPubMedCentralGoogle Scholar
  6. 6.
    Almasy L, Blangero J (1998) Multipoint quantitative-trait linkage analysis in general pedigrees. Am J Hum Genet 62:1198–1211CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Allison DB, Neale MC, Zannolli R, Schork NJ, Amos CI, Blangero J (1999) Testing the robustness of the likelihood-ratio test in a variance-component quantitative-trait loci-mapping procedure. Am J Hum Genet 65:531–544CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Sham PC, Purcell S (2001) Equivalence between Haseman-Elston and variance-components linkage analyses for sib pairs. Am J Hum Genet 68:1527–1532CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Chen WM, Broman KW, Liang KY (2004) Quantitative trait linkage analysis by generalized estimating equations: unification of variance components and Haseman-Elston regression. Genet Epidemiol 26:265–272CrossRefPubMedGoogle Scholar
  10. 10.
    Goldgar DE (1990) Multipoint analysis of human quantitative genetic variation. Am J Hum Genet 47:957–967PubMedPubMedCentralGoogle Scholar
  11. 11.
    Wang T, Elston RC (2005) Two-level Haseman-Elston regression for general pedigree data analysis. Genet Epidemiol 29:12–22CrossRefPubMedGoogle Scholar
  12. 12.
    Lange KL, Little RJA, Taylor JMG (1989) Robust statistical modeling using the T distribution. J Am Stat Assoc 84:881–896Google Scholar
  13. 13.
    Blangero J, Williams JT, Almasy L (2000) Robust LOD scores for variance component-based linkage analysis. Genet Epidemiol 19:S8–S14CrossRefPubMedGoogle Scholar
  14. 14.
    Chen WM, Broman KW, Liang KY (2005) Power and robustness of linkage tests for quantitative traits in general pedigrees. Genet Epidemiol 28:11–23CrossRefPubMedGoogle Scholar
  15. 15.
    Abecasis GR, Cherny SS, Cookson WO, Cardon LR (2001) Merlin—rapid analysis of dense genetic maps using sparse gene flow trees. Nat Genet 30:97–101CrossRefPubMedGoogle Scholar
  16. 16.
    Cardon LR, Smith SD, Fulker DW, Kimberling WJ, Pennington BF, DeFries JC (1994) Quantitative trait locus for reading disability on chromosome 6. Science 266:276CrossRefPubMedGoogle Scholar
  17. 17.
    Cardon LR, Smith SD, Fulker DW, Kimberling WJ, Pennington BF, DeFries JC (1995) Quantitative trait locus for reading disability: correction. Science 268:1553CrossRefPubMedGoogle Scholar
  18. 18.
    Goode EL, Jarvik GP (2005) Assessment and implications of linkage disequilibrium in genome wide single nucleotide polymorphism and microsatellite panels. Genet Epidemiol 29:S72–S76CrossRefPubMedGoogle Scholar
  19. 19.
    Kong X, Murphy K, Raj T, He C, White PS, Matise TC (2004) A combined linkage-physical map of the human genome. Am J Hum Genet 75:1143–1148CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Matise TC, Chen F, Chen W, De La Vega FM, Hansen M, He C, Hyland FCL, Kennedy GC, Kong X, Murray SS (2007) A second-generation combined linkage–physical map of the human genome. Genome Res 17:1783CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Sinha R, Gray-McGuire C (2008) Haseman Elston regression in ascertained samples: importance of dependent variable and mean correction factor selection. Hum Hered 65:66–76CrossRefPubMedGoogle Scholar
  22. 22.
    Morris NJ (2009) Multivariate and structural equation models for family data. Hum Hered 70:278–286CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media LLC 2017

Authors and Affiliations

  1. 1.Department of Population and Quantitative Health SciencesCase Western Reserve UniversityClevelandUSA

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