Likelihood, Bayesian and MCMC Methods in Quantitative Genetics by Daniel Sorensen; DanielGianolaReview by: Rudy GuerraJournal of the American Statistical Association, Vol. 103, No. 481 (Mar., 2008), p. 432Published by: American Statistical AssociationStable URL: http://www.jstor.org/stable/27640063 .

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432 Book Reviews

The chapters are grouped into seven sections: "General Principles,"

"Population Pharmacokinetic Basis of Pharmacometrics," "Pharmacokinet

ics/Pharmacodynamics Relationship: Biomarkers and Pharmacogenomics, PK/PD Models for Continuous Data, and PK/PD Models for Outcomes Data," "Clinical Trial Designs," "Pharmacometric Knowledge Creation," "Pharmaco

metric Service and Communication," and "Specific Application Examples." The editors have compiled an index across all chapters.

It would not be just to simply list all of the book's chapter titles here or

to discuss particulars of the chapters. Instead, I refer you to the book's web

page, which provides all chapter titles and authors: http:V/www3'.interscience.

wiley.com/cgi-bin/bookhome/I12632609/. Furthermore, since you will most

likely not read the entire book but instead, read only selected chapters, it might be of interest that individual chapters can be purchased online directly from the

webpage for $25 per chapter.

Although the book comprises 47 chapters contributed by different authors, it is fairly homogeneous in terms of layout and writing style. However, I will never understand why references are given just as numbers and show up in the

reference list in the order of occurrence in the text, instead of alphabetically sorted by author.

Which software to use is always a lively topic, and not surprisingly differ ent authors favor different software. This book comprises graphs, listings, and

outputs from a range of software packages including NONMEM, WinNonLin,

S-PLUS, and Matlab; even Perl and Unix shell scripts can be found. Although this list reflects the range of tools used in practice, the inclusion of a given soft ware in a given chapter does not contribute to easy access to the contents of

that chapter unless you are familiar with that particular software package, and

at times the code is hard to follow.

In addition, Chapter 2 postulates some "general principles of programming," and even though these are fairly general (e.g., "avoid too many or too few func

tion parameters," "write modular code"), most chapters do not follow them.

Some data that are used in the program code are never described, let alone

provided; data sets and variable names are hard coded into the programs (e.g.,

Chaps. 12, 32); many do not wrap the code into modules like functions; and most are hardly documented or enhanced by comments. I even found testing code (although commented out) in various chapters (e.g., 12, 16, 29). In addi

tion, I did not find an online page from which to download the code. For these

reasons, the claim that "the reader is able to reproduce the examples in his/her

spare time" (p. xvii) seems unwarranted. However, I do not view this as a major drawback unless you expect a software library ready to be used.

In summary, if you work in drug development, have at least some back

ground in pharmacology and statistics, and want some insight into what phar macometrics or modeling (and, to some extent, simulation) is all about, or if you want to quickly learn the basics of a particular topic, then this is a wonderful

reference book.

Andreas Krause

Pharsight Corporation

REFERENCES

Bonate, P. (2005), Pharmacokinetic-Pharmacodynamic Modeling and Simula

tion, New York: Springer-Verlag. Ng, R. (2004), From Drug Discovery to Approval, Hoboken, NJ: Wiley. Ting, N. (ed.) (2007), Dose Finding in Drug Development, New York: Springer

Verlag.

Likelihood, Bayesian and MCMC Methods in Quantitative Genetics.

Daniel Sorensen and Daniel GiANOLA. New York: Springer, 2002.

ISBN 0-387-95440-6. xvii + 740 pp. $ 99.00 (H). Digital: $19.80.

This book's title gives even a window shopper enough information to know

what lies between inside its covers. This is a book that instructs the reader on the

basic theory and applications to analyzing quantitative genetic data with likeli

hood, Bayesian, and Markov chain Monte Carlo (MCMC) statistical methods.

The book is pitched at the "numerate biologist" who has a working knowledge of differential and integral calculus and linear algebra and a basic background in statistical inference at the level, of say, Hogg and Craig (1995). The book

may serve as a reference or a textbook, although the latter use would have

to be supplemented with exercises and perhaps also software instruction, be cause the book lacks both. Part I reviews probability and distribution theory;

Part II covers inference with likelihood and Bayesian methods; Part III intro

duces MCMC and its use in implementing statistical models; and Part IV cov

ers a handful of applications in quantitative genetics, focusing on models for

(additive) genetic effects, genetic variance components, threshold models for

categorical (e.g., disease) traits, longitudinal studies, quantitative trait loci, and

segregation analysis. It is conceivable that the book could be used as a text

book for a course on "modern" inferential methods, for which Parts I?III could

stand alone. Indeed, the book is reminiscent ofthat by Elandt-Johnson (1971), a standard text on basic probability and statistical inference. The fact that the

examples come from genetics gives the book its title, but its main objective is to teach the biologist some basic statistical methods. The genetics examples provide a comfort space for the biologist to think and learn about the statistical

issues. As a reference book for established investigators or students addressing

analysis of quantitative genetic data, I cannot think of a better resource. Both

Sorensen and Gianola have extensive experience in thinking about and analyz

ing (real) quantitative genetic data, especially data arising from animal breeding programs. The result is a book whose contents represent statistical techniques and models that are known to be practically useful for analyzing quantitative ge netic data. Although this book is limited in scope, it does not scrimp on detail.

The intended audience of numerate biologists will appreciate the step-by-step derivations and examples. On the other hand, the statistical expert may focus on the substantive applications throughout the book, and especially in Part IV.

As far as the statistical presentation is concerned, Sorensen and Gianola get

things right. Although the book does not follow a theorem-proof format, the

authors do not shy away from careful mathematical treatment. The reader will

have to feel comfortable with the prerequisite mathematical level of the book to fully appreciate much of the discussion; in particular, a solid grounding in

mixed-effects linear models will prove very useful. The authors proudly iden

tify themselves as belonging to a scientific community, as opposed to a com

munity of mathematicians whose favorite data may be x\, X2, , xn. As such, Sorensen and Gianola appreciate and take a strong stand on setting the right model for the real problem at hand. This means positing statistical models con

sistent with the biological systems that they are supposed to describe. I could not agree more. However, the seasoned statistician may find little to learn about

quantitative genetics in this book. Granted, this is not the book's purpose, but

such terms as "selection," "random mating," "additive genetic variance," "gene

dropping," "allelic effects," and others may leave the budding statistical geneti cist reaching for the text by Falconer and Mackay (1996) for background on

quantitative genetics. It is also worth noting that most of the genetics exam

ples come from designed studies that do not include humans; for example, the

discussion on models for detecting quantitative trait loci (QTL) is within the

context of a QTL backcross of inbred lines with full marker information. Hu man studies of QTL are observational, and most genetic markers are not typed with full information. This reflects the background and choices that the authors

have made concerning the genetics applications. Despite this bias, the book is

worth owning for anyone interested in applying likelihood or Bayesian mod

els, especially realistic models that may require MCMC for implementation. Sorensen and Gianola have written a book that stands alone, for the moment.

Bayesian models are gaining popularity in many areas of genetics and bioinfor

matics and, more generally, "computational biology." The appropriate use and

implementation of Bayesian models will depend in large part on resources like

this very useful book.

Rudy Guerra

Rice University

REFERENCES

Elandt-Johnson, R. C. (1971), Probability Models and Statistical Methods in

Genetics, New York: Wiley. Falconer, D. S., and Mackay, T. F. C. (1996), Introduction to Quantitative Ge

netics (4th ed.), Essex, U.K.: Longman. Hogg, R. V, and Craig, A. T. (1995), Introduction to Mathematical Analysis

(5th ed.), New York: MacMillan.

Bayesian Core: A Practical Approach to Computational Bayesian Statistics.

Jean-Michel Marin and Christian P. Robert. New York: Springer, 2007. ISBN 978-0-387-38979-0. xiii + 255 pp. $74.95.

As its title suggests, this text intentionally focuses on a few fundamental

Bayesian statistical models and key computational tools. By avoiding a more

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