Likelihood, Bayesian and MCMC Methods in Quantitative Genetics


Author(s): Daniel Sorensen, Daniel Gianola

Springer-Verlag 2002 (ix + 740 pp) ISBN 0-387-954406 cost Eur 90.

The book is extensive in scope, covering so much background material that quantitative genetics applications are not reached until page 560. The overview of statistical theory starts with an introduction to the basic probability distributions and distribution theory, and covers the fundamentals of classical inference before reaching Bayesian methods around page 200. The coverage of Bayesian theory is extensive, and includes a discussion of information and entropy, and of the notion of "uninformative" priors, as well as model assessment and model averaging. There follows a chapter on the EM algorithm, which forms a prelude to a substantial introduction to MCMC. Turning then to quantitative genetics applications, the authors cover Bayesian formulations of (and Gibbs sampler algorithms for) the basic linear models with t-distributed errors, as well as categorical response and longitudinal data, finishing with an introduction to models for segregation and quantitative trait locus (QTL) analysis.

There are some simple data examples distributed through the text, and occasional outline algorithms for computational implementation. However this is not a "practical" book, it is about the ideas motivating the theory. Some detailed derivations and proofs are given. With so much material packed into one volume, it is inevitable that the reader will need some mathematical sophistication to be comfortable with the formulas and derivations. However, the mathematical level is not high -- the reader need only be familiar with elementary calculus and matrix notation to glean the most important ideas. Even previous familiarity with probability and statistical inference is not strictly needed, but is in practice a prerequisite -- there is too much for a reader to absorb without some head start.

I found the coverage of material to be excellent: well chosen and well written, and I didn't spot a single typographical error. There is little that I would have omitted: the discussion of information and entropy is perhaps unnecessary at this level, and possibly the entire chapter devoted to the EM algorithm is excessive, given the prominence of MCMC in the applications. Conversely I can think of little to ask for that is not provided. The coverage of MCMC convergence is thin and would have benefited from examples. Similarly, problems of model mispecification and its diagnosis could have been given more attention, with examples. The authors do not discuss software, arguing that developments happen too quickly. I find this justification weak: some guidance is better than nothing, and software web sites are becoming more stable.

There are no exercises, and the book isn't particularly suited as the basis for a course text. It can serve as a resource book for masters-level taught courses, but will be most useful for PhD students and other researchers who need to fill in gaps in their knowledge, grasp the intuition behind statistical techniques, models, and algorithms, and find pointers to more extensive treatments. Overall, I find that the authors have succeeded admirably in their goals. I highly recommend this excellent book to any researcher seeking a graduate-level introduction to the modern statistical methods applied in quantitative genetics.

David Balding, Imperial College, UK


  1. Molecular Evolution (S. Nagl)
  2. Gene Finding (J. G. Sgorous and R. M. Twyman)
  3. Sequence Comparison Methods (C. Orengo)
  4. Amino Acid Residue Conservation (W. S. J. Valdar and D. T. Jones)
  5. Function Prediction from Protein Sequence (S. B. Nagl)
  6. Protein Structure Comparison(I. Sillitoe and C. Orengo)
  7. Protein Structure Classifications (F. Pearl and C. Orengo)
  8. Comparative Modeling (A. C. R. Martin)
  9. Protein Structure Prediction (D. T. Jones)
  10. From Protein Structure to Function(A. E. Todd)
  11. From Structure-Based Genome Annotation to Understanding Genes and Proteins (S. A. Teichmann)
  12. Global Approaches for Studying Protein-Protein Interactions (S.A. Teichmann)
  13. Predicting the Structure of a Protein-Biomolecular Interactions (R.M. Jackson)
  14. Experimental Use of DNA Arrays (P. Kellam and X. Liu)
  15. Mining Gene Expression Data (X. Liu and P. Kellam)
  16. Proteomics (M. P. Weir, P. Blackstock and R. M. Twyman)
  17. Data Management of Biological Information (N. J. Martin )
  18. Internet Technologies for Bioinformatics (A. C. R. Martin)

Hardcover, 760 pages; 2002; Springer-Verlag