Applied Statistical Genetics with R: For Population-based Association StudiesPaperback Use R!
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- Publisher: Springer-Verlag New York Inc.
- Format: Paperback | 252 pages
- Dimensions: 156mm x 240mm x 18mm | 399g
- Publication date: 6 May 2009
- Publication City/Country: New York, NY
- ISBN 10: 0387895531
- ISBN 13: 9780387895536
- Illustrations note: 1, black & white illustrations
- Sales rank: 591,319
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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From the reviews: "This book aims at filling a real gap in the literature. ... After three introductory chapters on basic statistical and genetic concepts and association studies, the book deals with the problems of multiple comparison, unknown phase, and model building and predictions in high dimension: topic choices that I find relevant and stimulating... This textbook, then, serves as a starting point for further reading, and this is a great way of introducing statistical genetics problems to a general audience. From this point of view, and in many ways, the book feels like the transcription of lecture notes of an introductory class. This is certainly the way in which many great texts were developed. (Chiara Sabatti, Journal of Statistical Software, September 2009, Volume 31) "This book provides a gentle introduction to genome-wide association studies (GWAS) within both a theoretical and methodological perspective. It will especially be a useful resource to those interested in the ever growing interdisciplinary approach to 'genetic epidemiology'... This new book in the Springer Use R! series certainly fills the lacking R resources on this rapidly evolving field in statistical genetics..." (Christopher Lalanne, Journal of Statistical Software, September 2009, Volume 31) "Applied Statistical Genetics With R is written at a level accessible to non-experts in statistical genetics. The author does not assume the reader is familiar with statistical techniques and hence introduces techniques s required. Examples are accompanied by R scripts typical of the Use R! book series which encourages hands-on experimentation by the readers. Re-using existing packages is characteristic of open-source software development, such as R, and encourages transparency and reproducibility whole minimizing redundancy. The author invokes functionalities from readily available R packages and provides supplemental R scripts as required. ...Overall, the book provides a nice... introduction to the area of statistical genetics concepts using R." (American Statistician, August 2010, Vol. 64, No. 3) "This book is addressed to a wide readership. Researcher with medical background will learn about the statistical fundamentals in this field, whereas statisticians will see how established methods can be used in this modern research area. ... a book written about this topic often has to satisfy particular needs and interests. This is well done in this book. ... The book is a useful help for researchers and students who are interested in an applied approach to statistical genetics in population-based association studies." (Daniel Fischer, International Statistical Review, Vol. 78 (1), 2010)
Back cover copy
The vast array of molecular level information now available presents exciting opportunities to characterize the genetic underpinnings of complex diseases while discovering novel biological pathways to disease progression. In this introductory graduate level text, Dr. Foulkes elucidates core concepts that undergird the wide range of analytic techniques and software tools for the analysis of data derived from population-based genetic investigations. Applied Statistical Genetics with R offers a clear and cogent presentation of several fundamental statistical approaches that researchers from multiple disciplines, including medicine, public health, epidemiology, statistics and computer science, will find useful in exploring this emerging field. Couched in the language of biostatistics, this text can be easily adopted for public health and medical school curricula. The text covers key genetic data concepts and statistical principles to provide the reader with a strong foundation in methods for candidate gene and genome-wide association studies. These include methods for unobservable haplotypic phase, multiple testing adjustments, and high-dimensional data analysis. Emphasis is on analysis of data arising from studies of unrelated individuals and the potential interplay among genetic factors and more traditional, epidemiological risk factors for disease. While theoretically rigorous, the analytic techniques are presented at a level that will appeal to researchers and students with limited knowledge of statistical genetics. The text assumes the reader has completed a first course in biostatistics, uses publicly available data sets for illustration, and provides extensive examples using the open source, publicly available statistical software environment R. Dr. Foulkes is an Associate Professor of Biostatistics at the University of Massachusetts, Amherst, where she has been recognized for teaching excellence. Her active research program includes the development of methods for characterizing the relationships among high-dimensional molecular and cellular level data and measures of disease progression. She has authored numerous technical manuscripts in this field and currently serves as the principal investigator of an individual research award from the National Institute of Allergy and Infectious Diseases, a division of the National Institutes of Health.
Table of contents
Genetic association studies.- Elementary statistical principles.- Genetic data concepts and tests.- Multiple comparison procedures.- Methods for unobservable phase.- Classification and regression trees.- Additional topics in high-dimensional data analysis.