Relationship inference with Familias and R : statistical methods in forensic genetics / Thore Egeland, Daniel Kling, Petter Mostad
Idioma: Inglês.Edição: 1st ed.Publicação: Amsterdam [etc.] : Academic Press ; Elsevier, 2016Descrição física: ix, 243 p.; 24 cmISBN: 9780128024027Assunto: 238061 | 321508 | 349883 | 232358Resumo: Preface. Chapter 1: Introduction. 1.1 Using this book. 1.2 Warm-up examples. 1.3 Statistics and the law. Chapter 2: Basics. 2.1 Forensic markers. 2.2 Probabilities of genotypes. 2.3 Likelihoods and LRs. 2.4 Mutation. 2.5 Theta correction. 2.6 Silent allele. 2.7 Dropout. 2.8 Exclusion probabilities. 2.9 Beyond standard markers and data. 2.10 Simulation. 2.11 Several, possibly complex pedigrees. 2.12 Case studies. 2.13 Exercises. Chapter 3: Searching for relationships. 3.1 Introduction. 3.2 Disaster victim identification. 3.3 Blind search. 3.4 Familial searching. 3.5 Exercises. Chapter 4: Dependent markers. 4.1 Linkage. 4.2 Linkage disequilibrium. 4.3 Haplotype frequency estimation. 4.4 Programs for linked markers. 4.5 Exercises. Chapter 5: Relationship inference with R. 5.1 Using R. 5.2 Exercises. Chapter 6: Models for pedigree inference. 6.1 Population-level models. 6.2 Pedigree-level models. 6.3 Observational-level models. 6.4 Computations. 6.5 Exercises. Chapter 7: Parameter estimation and uncertainty. 7.1 Allele frequencies. 7.2 The Theta-correction parameter. 7.3 The Lambda model for haplotype frequencies. 7.4 Mutations and mutation models. 7.5 Other parameters. 7.6 Handling “uncertainty” in LRs. 7.7 Exercise. Chapter 8: Making decisions. 8.1 Some basic decision theory. 8.2 LR as a random variable. 8.3 Exercises. Glossary for non-biologists. Bibliography.| Item type | Current location | Call number | Copy number | Status | Date due | Barcode |
|---|---|---|---|---|---|---|
| Monografias | Biblioteca da Polícia Judiciária | PJ35847 | Available | PJ135847 |
Preface. Chapter 1: Introduction. 1.1 Using this book. 1.2 Warm-up examples. 1.3 Statistics and the law. Chapter 2: Basics. 2.1 Forensic markers. 2.2 Probabilities of genotypes. 2.3 Likelihoods and LRs. 2.4 Mutation. 2.5 Theta correction. 2.6 Silent allele. 2.7 Dropout. 2.8 Exclusion probabilities. 2.9 Beyond standard markers and data. 2.10 Simulation. 2.11 Several, possibly complex pedigrees. 2.12 Case studies. 2.13 Exercises. Chapter 3: Searching for relationships. 3.1 Introduction. 3.2 Disaster victim identification. 3.3 Blind search. 3.4 Familial searching. 3.5 Exercises. Chapter 4: Dependent markers. 4.1 Linkage. 4.2 Linkage disequilibrium. 4.3 Haplotype frequency estimation. 4.4 Programs for linked markers. 4.5 Exercises. Chapter 5: Relationship inference with R. 5.1 Using R. 5.2 Exercises. Chapter 6: Models for pedigree inference. 6.1 Population-level models. 6.2 Pedigree-level models. 6.3 Observational-level models. 6.4 Computations. 6.5 Exercises. Chapter 7: Parameter estimation and uncertainty. 7.1 Allele frequencies. 7.2 The Theta-correction parameter. 7.3 The Lambda model for haplotype frequencies. 7.4 Mutations and mutation models. 7.5 Other parameters. 7.6 Handling “uncertainty” in LRs. 7.7 Exercise. Chapter 8: Making decisions. 8.1 Some basic decision theory. 8.2 LR as a random variable. 8.3 Exercises. Glossary for non-biologists. Bibliography.
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