|
Applied Statistical Genetics with R For Population-based Association Studies
Andrea S Foulkes
Springer
2009
Softcover 252 pp ISBN 9780387895536
£35.00
|
|
|
|
|
|
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.
Written for: Students, researchers
Contents
Preface
List of Tables
List of Figures
Acronyms
- Genetic Association Studies
- Elementary Statistical Principles
- Genetic Data Concepts and Tests
- Multiple Comparison Procedures
- Methods for Unobservable Phase
- Classi_cation and Regression Trees
- Additional Topics in High-Dimensional Data Analysis
A.1 Getting started
A.2 Types of data objects
A.3 Importing data
A.4 Managing data .
A.5 Installing packages
A.6 Additional help
References
Glossary of Terms
Glossary of Select R Packages
Subject Index
Index of R Functions and Packages
To find similar publications, click on a keyword below:
Recent additions
: Springer
: biology, general
: genetics
: populations
: statistics
: textbook
| 
