Book Review

Vijay Shankar



Cedric Gondro, Julius van der Werf and Ben Hayes (Eds.):
Genome-Wide Association Studies and Genomic Prediction.

Series: Methods in Molecular Biology (Book 1019)
Hardcover: 515 pages
Publisher: Humana Press; 2013 edition (June 25, 2013)
Language: English
ISBN-10: 1627034463
ISBN-13: 978-1627034463
Price: 125,52 EUR


Image: Soberve/iStockphoto

Genome-Wide Association Studies and Genomic Prediction

Genomes, big data and new age medicine. What’s the connection? Another new specimen from the popular “Methods in Molecular Biology” series joins the dots. Laymen, however, should stay away.

Genome sequencing and advanced computing technologies play a large role in understanding the complexity of living systems. Genome Wide Association Studies and Genomic Prediction (Methods in Molecular Biology, vol. 1019) is a practical guide for experts to obtain, qualify, and statistically analyse data on genomes and to support geno­type-phenotype information. In a growing field, this is the first hands-on book for experts in a relatively new discipline.

Biologists and computer experts are working out how to better use expertise from each field. One recent attempt is to statistically scan the genomes based on Single Nucleotide Poly­morphism (SNP) markers, for differences in their frequencies and numbers, and to associate them with complex traits or diseases. This shift from identifying a ‘candidate gene’ to ‘candidate genomic regions’ attempts to pave the way towards finely tuned agriculture, personalised medicine and a better understanding of basic biological complexity. The number of such Genome-Wide Association Studies (GWAS) have grown rapidly since the first study almost a decade ago. Thus this discipline is indeed thirsty for good textbooks – both for beginners and experts. How suitable is this book for an enthusiast in the field?

The discipline longs for good books

The 26-chapter tome was edited by three Australian scientists: Cedric Gondo and Julius van der Werf from the University of New England (New South Wales) and Ben Hayes from the department of Primary Industries, Victoria, Australia.

Experts, beginners and enthusiastic readers will all look for a good introduction to the field, whether to brush up on basic knowledge or to hear differing viewpoints on the subject. GWAS is a complicated topic that requires an initial investment of time. When it comes to speci­fic terms like linkage disequilibrium, odds ratios and so on, it would be nice to have an introductory chapter and a thorough glossary at the end of the book. Your reviewer was disappointed to find the introduction limited to a preface and searched in vain for a glossary.

Another issue is chapter organisation. After scanning through the book for some time, one can only guess at what the random chapters tell you about statistical program packages, databases and how to use them to design, carry out and make use of GWAS.

The significance of data quality control

Otherwise, the book is too good to ignore once you start reading and pick up information along the way. This is in fact a reference book for your genomics lab that you cannot whizz through in one go. There is good information about using the open-source statistical program Suite R in the first chapter. Other chapters cover, along with R coding scripts, how one can use modules of R for various GWAS purposes.

Another good thing about the book is the clear picture that it gives of GWAS design and how to enhance its power (for instance by a clever choice of samples, managing their data before and after the experiments for decent analysis). The information on the significance of data quality control, and methods to analyse genomic data is ample and clear. The practical implication of validating a GWAS is meticulously detailed in a separate chapter.

Richly packed with statistical tools

The field of GWAS is also useful in plant and animal sciences. However, the book covers this application of the subject in only a few chapters. Otherwise, it is richly packed with statistical tools and notes to identify genomic regions for selected phenotypes and to predict their associations (in different sample sizes or with prior knowledge of the phenotypes).

The reader’s hopes are naturally high as the book comes from a popular series and is written by scientists from various walks of life. As expected, the book does not disappoint the reader outright, but would have been better without the above dents. One hopes that the next edition will fill existing gaps. But if you are new to the field, this book will certainly extend you a warm welcome to the tricky world of GWAS.





Letzte Änderungen: 11.10.2013




Information 4


Information 5