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

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

Relationship Inference in Familias and R discusses the use of Familias and R software to understand genetic kinship of two or more DNA samples. This software is commonly used for forensic cases to establish paternity, identify victims or analyze genetic evidence at crime scenes when kinship is involved. The book explores utilizing Familias software and R packages for difficult situations including inbred families, mutations and missing data from degraded DNA. The book additionally addresses identification following mass disasters, familial searching, non-autosomal marker analysis and relationship inference using linked markers. The second part of the book focuses on more statistical issues such as estimation and uncertainty of model parameters. Although written for use with human DNA, the principles can be applied to non-human genetics for animal pedigrees and/or analysis of plants for agriculture purposes. The book contains necessary tools to evaluate any type of forensic case where kinship is an issue. - This volume focuses on the core material and omits most general background material on probability, statistics and forensic genetics - Each chapter includes exercises with available solutions - The web page familias.name contains supporting material

A compilation of 82 annotated bibliographies on Molecular Markers in Plant Genome Analysis. Sponsored by CRIS/ICAR.

Currently there are major challenges in data mining applications in the geosciences. This is due primarily to the fact that there is a wealth of available mining data amid an absence of the knowledge and expertise necessary to analyze and accurately interpret the same data. Most geoscientists have no practical knowledge or experience using data mining techniques. For the few that do, they typically lack expertise in using data mining software and in selecting the most appropriate algorithms for a given application. This leads to a paradoxical scenario of "rich data but poor knowledge". The true solution is to apply data mining techniques in geosciences databases and to modify these techniques for practical applications. Authored by a global thought leader in data mining, Data Mining and Knowledge Discovery for Geoscientists addresses these challenges by summarizing the latest developments in geosciences data mining and arming scientists with the ability to apply key concepts to effectively analyze and interpret vast amounts of critical information. - Focuses on 22 of data mining's most practical algorithms and popular application samples - Features 36 case studies and end-of-chapter exercises unique to the geosciences to underscore key data mining applications - Presents a practical and integrated system of data mining and knowledge discovery for geoscientists - Rigorous yet broadly accessible to geoscientists, engineers, researchers and programmers in data mining - Introduces widely used algorithms, their basic principles and conditions of applications, diverse case studies, and suggests algorithms that may be suitable for specific applications

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