| Description |
1 online resource (xxiii, 252 pages) : illustrations |
| Series |
Use R! |
|
Use R!
|
| Contents |
Genetic Association Studies. Overview of population-based investigations ; Data components and terminology ; Data examples. -- Elementary Statistical Principles. Background ; Measures and tests of association ; Analytic challenges. -- Genetic Data Concepts and Tests. Linkage disequilibrium (LD) ; Hardy-Weinberg equilibrium (HWE) ; Quality control and preprocessing. -- Multiple Comparison Procedures. Measures of error ; Single-step and step-down adjustments ; Resampling-based methods ; Alternative paradigms. -- Methods for Unobservable Phase. Haplotype estimation ; Estimating and testing for haplotype-trait association. -- Classication and Regression Trees. Building a tree ; Optimal trees. -- Additional Topics in High-Dimensional Data Analysis. Random forests ; Logic regression ; Multivariate adaptive regression splines ; Bayesian variable selection ; Further readings |
| Summary |
"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."--Publisher's website |
| Analysis |
geneeskunde |
|
medicine |
|
biostatistiek |
|
biostatistics |
|
levenswetenschappen |
|
life sciences |
|
toegepaste statistiek |
|
applied statistics |
|
Biology (General) |
|
Biologie (algemeen) |
| Bibliography |
Includes bibliographical references (pages 227-235) and indexes |
| Notes |
Print version record |
| In |
Springer eBooks |
| Subject |
R (Computer program language)
|
|
Genetics -- Statistical methods
|
|
Epidemiologic Methods
|
|
Electronic Data Processing
|
|
Genetics, Population -- methods
|
|
Models, Statistical
|
|
data processing.
|
|
computer science.
|
|
SCIENCE -- Life Sciences -- Genetics & Genomics.
|
|
Genetics, Population -- methods.
|
|
Epidemiologic methods.
|
|
Automatic Data Processing.
|
|
Models, Statistical.
|
|
Genetics -- Statistical methods.
|
|
R (Computer program language)
|
|
Sciences de la vie.
|
|
Biomédecine.
|
|
Genetics -- Statistical methods
|
|
R (Computer program language)
|
| Form |
Electronic book
|
| ISBN |
9780387895543 |
|
038789554X |
|
9780387895536 |
|
0387895531 |
|
