
- 184 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
eBook - ePub
Pedigree Analysis in R
About this book
Pedigree Analysis in R gives an introduction to the theory of relatedness and covers a range of applications in forensic and medical genetics. The book's material was developed through teaching courses on genetic relatedness, pedigree analysis and R, and offers insights from a decade of research activities in forensic and medical genetics. The R code in the book uses the ped suite, a unified collection of packages for pedigree analysis, developed by the author. All code examples are given in full, allowing accurate reproduction of figures and results. At the end of each chapter, a selection of exercises encourages the reader to explore further and perform their own analyses.
- Introduction to the theory of genetic relatedness, richly illustrated with classic and novel examples
- In-depth case studies including kinship testing, pedigree reconstruction, linkage analysis and clinical segregation analysis
- Easy-to-follow R code with explanations
- Based on the ped suite packages for pedigree analysis in R
- Suitable for R users at all levels, including complete beginners
- Exercises after each chapter
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Please note we cannot support devices running on iOS 13 and Android 7 or earlier. Learn more about using the app.
Yes, you can access Pedigree Analysis in R by Magnus Dehli Vigeland in PDF and/or ePUB format, as well as other popular books in Law & Forensic Science. We have over one million books available in our catalogue for you to explore.
Information
Table of contents
- Title of Book
- Cover image
- Title page
- Table of Contents
- Copyright
- Preface
- Biography
- Chapter 1 Prerequisites
- Chapter 2 Pedigrees and Marker Data
- Chapter 3 Coefficients of Relatedness
- Chapter 4 Realised Relatedness
- Chapter 5 Probabilities on Pedigrees
- Chapter 6 Kinship Testing
- Chapter 7 Inference of Pairwise Relatedness
- Chapter 8 Pedigree Reconstruction
- Chapter 9 Linkage Analysis
- Chapter 10 Segregation Analysis for Variant Interpretation