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E-raamat: Forensic DNA Profiling: A Practical Guide to Assigning Likelihood Ratios [Taylor & Francis e-raamat]

  • Formaat: 242 pages, 87 Tables, color; 38 Line drawings, color
  • Ilmumisaeg: 10-Dec-2019
  • Kirjastus: CRC Press
  • ISBN-13: 9780429001017
  • Taylor & Francis e-raamat
  • Hind: 147,72 €*
  • * hind, mis tagab piiramatu üheaegsete kasutajate arvuga ligipääsu piiramatuks ajaks
  • Tavahind: 211,02 €
  • Säästad 30%
  • Formaat: 242 pages, 87 Tables, color; 38 Line drawings, color
  • Ilmumisaeg: 10-Dec-2019
  • Kirjastus: CRC Press
  • ISBN-13: 9780429001017

DNA testing and its forensic analysis are recognized as the “gold standard” in forensic identification science methods. However, there is a great need for a hands-on step-by-step guide to teach the forensic DNA community how to interpret DNA mixtures, how to assign a likelihood ratio, and how to use the subsequent likelihood ratio when reporting interpretation conclusions.

Forensic DNA Profiling: A Practical Guide to Assigning Likelihood Ratios will provide a roadmap for labs all over the world and the next generation of analysts who need this foundational understanding. The techniques used in forensic DNA analysis are based upon the accepted principles of molecular biology. The interpretation of a good-quality DNA profile generated from a crime scene stain from a single-source donor provides an unambiguous result when using the most modern forensic DNA methods. Unfortunately, many crime scene profiles are not single source. They are described as mixed since they contain DNA from two or more individuals.

Interpretation of DNA mixtures represents one of the greatest challenges to the forensic DNA analyst. As such, the book introduces terms used to describe DNA profiles and profile interpretation. Chapters explain DNA extraction methods, the polymerase chain reaction (PCR), capillary electrophoresis (CE), likelihood ratios (LRs) and their interpretation, and population genetic models—including Mendelian inheritance and Hardy-Weinberg equilibrium. It is important that analysts understand how LRs are generated in a probabilistic framework, ideally with an appreciation of both semicontinuous and fully continuous probabilistic approaches.


KEY FEATURES:
• The first book to focus entirely on DNA mixtures and the complexities involved with interpreting the results
• Takes a hands-on approach offering theory with worked examples and exercises to be easily understood and implementable by laboratory personnel
• New methods, heretofore unpublished previously, provide a means to innovate deconvoluting a mixed DNA profile, assign an LR, and appropriately report the weight of evidence
• Includes a chapter on assigning LRs for close relatives (i.e., “It’s not me, it was my brother”), and discusses strategies for the validation of probabilistic genotyping software

Forensic DNA Profiling fills the void for labs unfamiliar with LRs, and moving to probabilistic solutions, and for labs already familiar with LRs, but wishing to understand how they are calculated in more detail. The book will be a welcome read for lab professionals and technicians, students, and legal professionals seeking to understand and apply the techniques covered.

Foreword ix
Preface xi
Acknowledgments xiii
Authors xv
1 An Introduction and Review of DNA Profile Interpretation
1(16)
1.1 A Very Basic Review of a DNA Profile
1(3)
1.2 Thresholds
4(3)
1.3 Mixture Interpretation
7(1)
1.4 The Clayton Rules
7(4)
1.5 CPI
11(1)
1.6 RMP
12(1)
1.7 A Three-Allele Example
13(2)
1.8 Higher-Order and Complex Mixtures
15(1)
1.9 Conclusion and the Case for Probabilistic Genotyping
15(2)
2 An Introduction to Statistics and Proposition Setting
17(20)
2.1 Probability
17(4)
2.2 Derivation of Bayes' Theorem
21(2)
2.3 Odds Form of Bayes' Theorem
23(1)
2.4 Principles of Evidence Interpretation
23(1)
2.5 Setting Propositions
24(6)
2.6 The Likelihood Ratio
30(1)
2.7 Representing the Weight of Evidence and the Verbal Scale
30(2)
2.8 The Prosecutor and Defense Attorney's Fallacies
32(2)
2.9 Conclusion
34(1)
2.10 Practice Examples for the Reader
35(2)
3 Assigning the LR: Single-Source Examples and Population Genetic Models
37(18)
3.1 Population Parameters and Sampling Estimates
37(1)
3.2 Heterozygote Single-Source LR
37(2)
3.3 Homozygote Single-Source LR
39(1)
3.4 Theory - Population Genetic Models
40(1)
3.5 Product Rule
40(1)
3.6 NRCII 4.1
41(1)
3.7 NRC II 4.2 (Balding and Nichols Formulae)
42(2)
3.8 Theory - Theta
44(1)
3.9 Application of the Population Genetic Model to Single-Source Examples
45(1)
3.10 Theory - Data below the Analytical Threshold (Dropout)
45(4)
3.11 Drop-In
49(1)
3.12 Full-Profile Example
50(2)
3.13 Conclusion
52(1)
3.14 Practice Examples for the Reader
52(3)
4 Application of the Binary LR for Mixtures
55(30)
4.1 Two-Person Mixture with Conditioning
55(3)
4.2 Application of NRC II Recommendation 4.2 to Mixtures
58(4)
4.3 Two-Person Mixture without Conditioning
62(2)
4.4 Two-Person Resolvable Mixture
64(2)
4.5 Two-Person Partially Resolvable Mixture
66(2)
4.6 Two-Person Unresolvable Mixture
68(4)
4.7 Two-Person Unresolvable Mixture (Alleles below ST)
72(3)
4.8 Three-Person Mixture Example
75(3)
4.9 Conclusion
78(1)
4.10 Practice Examples for the Reader
79(6)
5 LRs Considering Relatives as Alternate Contributors
85(22)
5.1 Theory (Identity by Descent Coefficients)
85(2)
5.2 Single-Source LR Examples: Heterozygote
87(8)
5.3 Single-Source Examples: Homozygote
95(1)
5.4 Mixed DNA Profile Example
96(3)
5.5 Incorporating Subpopulation Correction
99(5)
5.6 Conclusion
104(1)
5.7 Practice Examples for the Reader
104(3)
6 Probabilistic Genotyping: Semicontinuous Models
107(34)
6.1 Probabilistic Methods of Interpretation
107(1)
6.2 Underlying Concepts
108(1)
6.3 Nomenclature
109(1)
6.4 Semicontinuous Methods: Single-Source Examples
110(12)
6.5 Semicontinuous Methods: Mixture Example
122(4)
6.6 Application of the Balding and Nichols Formulae
126(10)
6.7 Conclusion
136(2)
6.8 Practice Examples for the Reader
138(3)
7 Probabilistic Genotyping: Continuous Models
141(28)
7.1 Theory
141(5)
7.2 Worked Examples
146(18)
7.3 Conclusion
164(2)
7.4 Practice Examples for the Reader
166(3)
8 Considerations on Validation of Probabilistic Genotyping Software
169(16)
8.1 SWGDAM and ISFG Recommendations
170(1)
8.2 Specificity and Sensitivity Experiments
170(5)
8.3 Precision
175(2)
8.4 Effect of Changing the Number of Contributors
177(2)
8.5 Effect of Varying Propositions
179(3)
8.6 Conclusion
182(3)
Appendix 1 Allele Frequencies 185(4)
Appendix 2 Model Answers 189(40)
References 229(8)
Index 237
Jo-Anne Bright, PhD, has an MSc and PhD in Forensic Science from the University of Auckland. She is a Senior Science Leader at the Institute of Environmental Science and Research Limited, in Auckland, New Zealand where she has worked since 1999. She has 20 years of experience in forensic casework, quality management, and research. She has over 70 publications in the area of forensic DNA analysis and interpretation. Dr. Bright is a co-developer of the DNA profile interpretation software STRmix and has undertaken many presentations and workshops on DNA profile interpretation in Australasia, Asia, the United States, and Europe.

Michael D. Coble, PhD, is an Associate Professor and the Associate Director of the Center for Human Identification at the University of North Texas Health Science Center in Fort Worth, Texas. Dr. Coble received his masters degree in forensic science and his PhD in genetics from The George Washington University. He is a Fellow of the American Academy of Forensic Sciences and a member of the International Society for Forensic Genetics. He serves as a member of the OSAC Biological Data Interpretation and Reporting Committee and is an invited guest at the Scientific Working Group on DNA Analysis Methods (SWGDAM). He is a co-editor of the Forensic Biology subject area of WIRE's Forensic Science journal and is a member of the editorial board of Forensic Science International: Genetics.