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(More customer reviews)Applied Statistical Genetics with R For Population-based Association Studies is by Andrea S. Foulkes
of the University of Massachusetts and is meant for an audience with some understanding of both genetics and statistics, though the level of understanding in both areas need not be extensive. The statistical knowledge required would be covered in one or two undergraduate courses and an introductory genetics course would be helpful. Lacking this background, the first three chapters provide a well written review of the required knowledge and also provide extensive references for further reading. Indeed, the entire book provides plenty of references for further study of all of the topics covered.
For genetic studies, this book covers several basic topics, including linkage disequilibrium, Hardy-Weinberg equilibrium, and haplotypic phase as well as methods for identifying associations between single genetic polymorphisms and a trait. Subjects that are not covered include family studies, population genetics or gene expression analysis.
One of the great strengths of this book is the presentation of topics that while relevant to genetics studies are also relevant to the general statistical reader. There are very good chapters and sections on missing data, multiple comparisons, cross-validation, the EM algorithm, classification and regression trees [CART] and random forests as well as several Bayesian techniques. While some statistical notation and formulas are used throughout, each topic is presented in clear fashion that is understandable to the less mathematically inclined. Algorithms are laid out in a step-by-step fashion that makes topics such as the EM algorithm and Gibbs sampling understandable. Indeed, this is one of the few statistical texts, beyond the most basic introductory texts, that can be read cover-to-cover. If not for the extensive use of examples with a genetics focus, I would recommend this as a general text on advanced statistics.
This book makes extensive use of the freely available R programming language and publicly available data sets, with many worked-out examples throughout the text. A web site provides download-able data sets and code. While there is an appendix that introduces the R language, some working familiarity with R beyond this text will be necessary for most readers.
In all, I found this to be a very readable introduction to the use of statistics in genetics. It would make a very good text for an introductory course on statistical genetics. I also recommend this book to the general statistics reader because of its very readable presentation of some complex statistical topics.
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Statistical genetics has become a core course in many graduate programs in public health and medicine. This book presents fundamental concepts and principles in this emerging field at a level that is accessible to students and researchers with a first course in biostatistics. Extensive examples are provided using publicly available data and the open source, statistical computing environment, R.
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