Advertisement

Comparative Analysis of a Hierarchical Bayesian Method for Quantitative Trait Loci Analysis for the Arabidopsis Thaliana

  • Caroline Pearson
  • Susan J. Simmons
  • Karl RicanekJr.
  • Edward L. Boone
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4774)

Abstract

This work performs an analysis on two, quite different, techniques for Quantitative Trait Loci (QTL) Analysis. Interval Mapping (IM) as described by Karl Broman is compared to a Hierarchical Bayesian Model (HBM) technique that reduces the problem of QTL analysis down to one of model selection. Simulations were generated for the flowering plant of the Arabidopsis thaliana for evaluation of the techniques. It is shown that the HBM technique was much more successful at determining the appropriate loci/markers and corresponding chromosomes than the IM technique given a single loci. It was further elucidated through simulation runs that the HBM was robust against two loci/markers, whereas IM completely failed. The contribution of this work is in the comparison and analysis of the IM method to that of the HBM; hence, demonstrating through simulations that the HBM technique is superior to that of the IM for the Arabidopsis simulated data.

Keywords

Quantitative Trait Loci (QTL) Analysis Arabidopsis Interval Mapping Hierarchical Bayesian Model 

References

  1. 1.
    Sax, K.: The association of size differences with seed-coat pattern and pigmentation in Phaseolus vulgaris. Genetics 8, 552–560 (1923)Google Scholar
  2. 2.
    Lander, E.S., Botstein, D.: Mapping mendelian factors underlying traits using RFLP linkage maps. Genetics 121, 185–199 (1989)Google Scholar
  3. 3.
    Jansen, R.C.: A General Mixture Model for Mapping Quatitative Loci by Using Molecular Markers. Theoretical and Applied Genetics 85, 252–260 (1992)CrossRefGoogle Scholar
  4. 4.
    Zeng, Z.B.: Theoretical basis for separation of multiple linked gene effects in mapping quantitative trait loci. Proceedings of the National Academy of Science USA 90, 10972–10976 (1993)CrossRefGoogle Scholar
  5. 5.
    Wright, A.J., Mowers, R.P.: Multiple regression for molecular-marker: quantitative trait data from large F2 populations. Theoretical and Applied Genetics 89, 305–312 (1994)CrossRefGoogle Scholar
  6. 6.
    Kearsey, M.J., Hyne, V.: QTL Analysis, A simple marker regression approach. Theoretical and Applied Genetic 89, 698–702 (1994)Google Scholar
  7. 7.
    Wu, W.R., Li, W.M.: A New Approach for Mapping Quantitative Trait Loci Using Complete Genetic Marker Linkage Maps. Theoretical and Applied Genetics 89, 535–539 (1994)Google Scholar
  8. 8.
    Sen, S., Churchill, G.A.: A statistical framework for quantitative trait mapping. Genetics 159, 371–387 (2001)Google Scholar
  9. 9.
    Lamon, E.C., Clyde, M.A.: Accounting for Model Uncertainty in Prediction of Cholophyll A in Lake Okeechobee. Journal of Agricultural Biological and Environmental Statistics 5, 297–322 (2000)CrossRefMathSciNetGoogle Scholar
  10. 10.
    Zeng, Z.B.: Precision mapping of quantitative trait loci. Genetics 136, 1457–1468 (1994)Google Scholar
  11. 11.
    Zeng, Z.B., Kao, C.H., Basten, C.J.: Estimating the genetic architecture of quantitative traits. Genetic Research 74, 279–289 (1999)CrossRefGoogle Scholar
  12. 12.
    Bao, H.: Bayesian Hierarchical Regression Model to Detect Quantitative Trait Loci. UNCW Thesis (2006)Google Scholar
  13. 13.
    Satagopan, J.M., Yandell, B.S., Newton, M.A., Osborn, T.C.: A Bayesian approach to detect quantitative trait loci using Markov chain Monte Carlo. Genetics 144, 805–816 (1996)Google Scholar
  14. 14.
    Sillanpaa, M.J., Arjas, E.: Bayesian mapping of multiple quantitative trait loci from incomplete inbred line cross data. Genetics 148, 1373–1388 (1998)Google Scholar
  15. 15.
    Sillanpaa, M.J., Corander, J.: Model choice in gene mapping, what and why. Trends in Genetics 18, 301–307 (2002)CrossRefGoogle Scholar
  16. 16.
    Xu, S.: Estimating Polygenic Effects Using Markers of the Entire Genome. Genetics 163, 789–801 (2003)Google Scholar
  17. 17.
    Yi, N., Xu, S., Allison, D.B.: Bayesian model choice and search strategies for mapping interacting quantitative trait Loci. Genetics 165, 867–883 (2003)Google Scholar
  18. 18.
    Boone, E.L., Ye, K., Smith, E.P.: Evaluating the Relationship Between Ecological and Habitat Conditions Using Hierarchical Models. Journal of Agriculture, Biological, and Environmental Statistics 10(2), 1–17 (2005)MathSciNetGoogle Scholar
  19. 19.
    Bjornstad, A., Westad, F., Martens, H.: Analysis of genetic marker-phenotype relationships by jack-knifed partial least squares regression (PLSR). Hereditas 141, 149–165 (2004)CrossRefGoogle Scholar
  20. 20.
    Broman, K.W., Wu, H., Sen, Ś., Churchill, G.A.: R/qtl, QTL mapping in experimental crosses. Bioinformatics 19, 889–890 (2003)CrossRefGoogle Scholar
  21. 21.
    Simmons, S.J., Piegorsch, W.W., Nitcheva, D., Zeiger, E.: Combining environmental information via hierarchical modeling, an example using mutagenic potencies. Environmentrics 14, 159–168 (2003)CrossRefGoogle Scholar
  22. 22.
    Boone, E., Ye, K., Smith, E.P.: Assessment of Two Approximation Methods for Computing Posterior Model Probabilities. Computational Statistics and Data Analysis 48, 221–234 (2005)CrossRefMathSciNetGoogle Scholar
  23. 23.
    Loudet, O., Chaillou, S., Camilleri, C., Bouchez, D., Daniel-Vedele, F.: Bay-0 x Shahdara recombinant inbred lines population, a powerful tool for the genetic dissection of complex traits in Arabidopsis. Theoretical and Applied Genetics 104(6-7), 1173–1184 (2002)CrossRefGoogle Scholar
  24. 24.
    Lynch, M., Walsh, B.: Genetics and Analysis of Quantitative Traits. Sinauer Associates, Inc., Sunderland, MA (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Caroline Pearson
    • 1
  • Susan J. Simmons
    • 1
  • Karl RicanekJr.
    • 2
  • Edward L. Boone
    • 3
  1. 1.University of North Carolina Wilmington, Department of Mathematics and Statistics 
  2. 2.University of North Carolina Wilmington, Department of Computer Science, 601 South College Road, Wilmington, North Carolina, 28403USA
  3. 3.Virginia Commonwealth University, Department of Statistical Sciences and Operations Research, Richmond, Virginia, 23284USA

Personalised recommendations