Relationship inference with Familias and R : statistical methods in forensic genetics /
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 invol...
Saved in:
| Online Access: |
Full text (MCPHS users only) |
|---|---|
| Main Authors: | , , |
| Format: | Electronic eBook |
| Language: | English |
| Published: |
London :
Academic Press, an imprint of Elsevier,
2016
|
| Subjects: | |
| Local Note: | ProQuest Ebook Central |
Table of Contents:
- Front Cover; Relationship Inference with Familias and R: Statistical Methods in Forensic Genetics; Copyright; Contents; Preface; Acknowledgments; Chapter 1: Introduction; 1.1 Using This Book; 1.2 Warm-Up Examples; 1.3 Statistics and the Law; 1.3.1 Context; 1.3.2 Terminology; 1.3.3 Principles; 1.3.4 Fallacies; Chapter 2: Basics; 2.1 Forensic Markers; 2.2 Probabilities of Genotypes; 2.3 Likelihoods and LRs; 2.3.1 Standard Hypotheses; 2.3.2 The LR; 2.3.3 Identical by Descent and Pairwise Relationships; 2.3.4 Probability of Paternity: W; 2.3.5 Bayes's Theorem in Odds Form; 2.4 Mutation
- 2.4.1 Biological Background2.4.2 Mutation Example; 2.4.3 Mutation for Duos; 2.4.4 Dealing with Mutations in Practice; 2.5 Theta Correction; 2.5.1 Sampling Formula; 2.6 Silent Allele; 2.7 Dropout; 2.8 Exclusion Probabilities; 2.8.1 Random Match Probability; 2.9 Beyond Standard Markers and Data; 2.9.1 X-Chromosomal Markers; 2.9.2 Y-Chromosomal and mtDNA Markers; 2.9.3 DNA Mixtures; 2.10 Simulation; 2.11 Several, Possibly Complex Pedigrees; 2.12 Case Studies; 2.12.1 Paternity Case with Mutation; 2.12.2 Wine Grapes; Prior model for wine grapes; Likelihoods for wine grapes; 2.13 Exercises
- Chapter 3: Searching for relationships3.1 Introduction; 3.2 Disaster Victim Identification; 3.2.1 Identification Process; 3.2.2 Prior Information; 3.2.3 Implementation in Familias; 3.2.4 Extensions; Quick searching; Multiple relatives; 3.3 Blind Search; 3.3.1 Kinship Matching; 3.3.2 Direct Matching; 3.4 Familial Searching; 3.4.1 Implementation; 3.4.2 *Relatives and Mixtures; 3.4.3 Select Subsets; Top k; LR threshold; Profile centered; Conditional; 3.5 Exercises; Chapter 4: Dependent markers; 4.1 Linkage; 4.1.1 Recombination; 4.1.2 Introduction to Calculations
- 4.1.3 Generalization and the Lander-Green Algorithm4.1.4 Extensions; X-chromosomal markers; Mutations; Subpopulation correction; Dropouts and silent alleles; 4.2 Linkage Disequilibrium; 4.2.1 Introduction to Calculations; 4.2.2 *Generalization; Cluster approach; Exact calculations; 4.3 Haplotype Frequency Estimation; 4.4 Programs for Linked Markers; 4.4.1 FamLink; 4.4.2 FamLinkX; 4.5 Exercises; 4.5.1 Autosomal Markers and FamLink; 4.5.2 X-Chromosomal Markers and FamLinkX; Chapter 5: Relationship inference with R; 5.1 Using R; 5.1.1 R Packages for Relationship Inference
- 5.1.2 The Familias Package5.2 Exercises; Chapter 6: Models for pedigree inference; 6.1 Population-Level Models; 6.1.1 *Frequency Uncertainty; 6.1.2 *Taking Frequency Uncertainty into Account; 6.1.3 *Population Structure and Subpopulations; 6.1.4 *Haplotype Models; 6.1.5 Population Models for Nonautosomal Markers; 6.2 Pedigree-Level Models; 6.2.1 Mutation Models; The ""Equal'' model; The ""Stepwise'' model; *Stationary mutation models; *Model based on frequencies; *Stabilizing existing mutation models; 6.3 Observational-Level Models; 6.4 Computations; 6.4.1 Identical by Descent
