Course manual 2021/2022

Course content

Forensic Statistics is relevant for all forensic disciplines and the Bayesian paradigm connects them. The area where this is most pronounced and most developed is DNA evidence. For this reason, the focus of the course will be on DNA and biological trace evidence.

This Course has two main topics:

The biology and genetics of DNA-evidence, as well as the current methods for the analysis and interpretation of DNA traces:
The forensic statistical methods used for the evaluation and combination of (biological) evidence, both at source and activity level.

The Bayesian paradigm for computation of the value of evidence is applied to DNA evidence of increasing complexity, ranging from single source profiles to more complicated DNA evidence such as DNA mixtures and low template DNA profiles. The issues covered will include: DNA database searches, Kinship analysis and Disaster Victim identification (DVI), Bayesian Networks for Activity level evaluation and the combination of multiple evidence, and Familial Searching.

Study materials

Literature

Butler, J. M. (2005) Forensic DNA typing biology, technology, and genetics of STR markers . 2nd ed. London: Elsevier Academic Press.
Statistics and the Evaluation of Evidence for Forensic Scientists, C. Aitken and F. Taroni, Wiley, 2020.
Butler, J. (2011) Advanced topics in forensic DNA typing: methodology. Academic Press.

Syllabus

Lecture notes and power point presentations
Reader Forensic Statistics and DNA-evidence

Software

Hugin
LRmix / Euroformix

Other

the textbooks are available online via the UvA library

Objectives

Apply knowledge of forensic biology, biological trace examination and DNA-analysis to problems from crime scene, identification and individualization of biological evidence.
Apply the Bayesian paradigm for Forensic Statistics to DNA-evidence, i.e. to compute match probabilities for standard DNA profiles and, for example, mixtures, relatedness issues and database search.
Apply relevant statistical procedures to test the validity of Likelihood Ratio procedures
Create Bayesian networks to address e.g. activity-level questions or a combination of evidence.
Apply a relevant statistical analysis in problems of Kinship, relatedness in pedigrees, DVI and Familial searching.
Evaluate a comprehensive request for examination of biological trace evidence and to formulate hypotheses and alternative hypotheses at activity level.
Evaluate the evidential strength of complex DNA profiles using (semi) continuous statistical tools.

Teaching methods

Lecture
Self-study
Presentation/symposium
Computer lab session/practical training

Lectures, tutorials consisting of exercises, mock crime scenes, report writing on the basis of mock and real laboratory data (DNA profiles) and group presentations of studied literature.

Learning activities

Activity	Hours
Hoorcollege	40
Presentatie	4
Tentamen	3
Self study	121
Total	168	(6 EC x 28 uur)

Attendance

This programme does not have requirements concerning attendance (OER part B).

Assessment

Item and weight	Details
Final grade
50% Tentamen	Must be ≥ 5.5
10% Group Presentations DNA typing	Must be ≥ 5.5
10% Bayesian Framework assignment	Must be ≥ 5.5
15% Bayesian network assignment	Must be ≥ 5.5
15% DVI Assignment	Must be ≥ 5.5

All components will be graded on a scale from 1 to 10, with a maximum of one decimal after the point. These grades are used to calculate the final grade. In order to pass the course, all components and the final grade have to be sufficient, i.e. at least a five and a half. When a student has not fulfilled this requirement, the examiner will register the mark ‘did not fulfill all requirements’ (NAV) whether or not the averaged grade is sufficient.

The components will be weighted as follows:

Hand in exercises Island Problem (10%).
Group assignment on Bayesian networks (15%).
Group assignment mixture analysis (0%; not graded).
Group presentations on biological topics (DNA typing)(10%).
Individual DVI assignment, to hand in at the final exam (15%)

Final exam (50%)

Assignments 1 and 2 together cover statistical topics (for 25%). Assignments 4 and 5 cover topics in forensic biology (for 25%). Assignment 3 is not handed in; correct answer is given in tutorial session.

The exam of this course will be a written examination (open book exam) based on the content covered during the lectures. The exam consists of two parts, a part about the biology in the course and a part about the statistical aspects of DNA analysis. Both count for half of the score for the exam.

The final grade will be announced at the latest on 26st of April (= 15 working days after the final course activity). Between the 26st of April to May 23rd (= 35 working days after the final course activity) a post-exam discussion or inspection moment will be planned. This will be announced on Canvas and/or via email.

Table of specification

		Exit qualifications
Learning outcome	Components (see above)	1	2	3	4	5	6	7	8	9	10
1	4, 6		x			x
2	1, 3, 6	x			x
3	6	x			x			x
4	2	x			x			x
5	5, 6	x			x			x
6	2, 6					x		x
7	6	x						x

Table 1: Table of specification: the relation between the learning outcomes of the course, the assessment components of the course and the exit qualifications of the Master’s Forensic Science (see the course catalogues for the exit qualifications).

Assignments

The students will be (randomly) divided in groups of five students. Assignments 2 and 4 will be done in groups and graded as such. Assignments 1 and 5 will be individual and will be so graded. Assignment 3 will be done in a group and will not be graded.

1 Hand in exercise Island Problem

During the course the so called Island problem will be reviewed. In the lecture notes there are some exercises concerning this type of problems. The students will be given one such exercise a solution of which they have to hand in individually. The solutions will be graded on a scale of one to ten.

2 Group assignment Bayesian Networks

The theory of Bayesian networks will be done during a lecture. For this assignment the students are working in groups of about five. There will be an introduction to the program HUGIN, a program to analyse Bayesian networks. The groups will be given a certain practical situation where a network can be built. The groups have to make their own network in HUGIN and motivate their choices. The solutions will be graded on a scale of one to ten.

3 Group assignment mixture analysis

The theory of analysis of mixtures will be done during a lecture. The assignment consists of performing an analysis of a DNA sample using computer software for the calculation of the evidential strength of DNA mixtures.

4 Group presentation

The students will be divided in groups of about five. Each group is assigigned to a topic on DNA typing techniques. The group selects a relevant scientific publication or case review on this topic and prepares a presentation on their findings. The presentations are graded with the assessment form in the appendix.

5 Individual DVI assignment

This is an individual assignment where the students are given DNA profiles of unidentified victims in a mass grave and a list of DNA profiles of possible relatives. The assignment consists of a computation based on these profiles to identify the victims.

The calculations and report will be graded on a scale of one to ten.

Fraud and plagiarism

The 'Regulations governing fraud and plagiarism for UvA students' applies to this course. This will be monitored carefully. Upon suspicion of fraud or plagiarism the Examinations Board of the programme will be informed. For the 'Regulations governing fraud and plagiarism for UvA students' see: www.student.uva.nl

Course structure

Weeknummer	Onderwerpen	Studiestof
1	The biology and genetics of DNA-evidence / (Bayesian) statistics
2	DNA-typing and Single source DNA evaluation
3	From single source to mixed DNA evaluation, Bayesian Networks
4	Bayesian Networks and complex DNA-mixtures
5	Kinship, DVI, Continuous Models for complex mixtures
6	Activity level evaluation and validation methods for LR procedures
7	Mixture exercises / Q & A
8	Exam

Timetable

The schedule for this course is published on DataNose.

Last year's course evaluation

In order to provide students some insight how we use the feedback of student evaluations to enhance the quality of education, we decided to include the table below in all course guides.

Forensic Statistics and DNA evidence (6EC)	N=17
Strengths Lectures were clear, structured and detailed, recording of the lectures helps preparing the exam. The lecturers were willing to help if needed. Without biology background it was good to follow Assignments were very helpful.	Notes for improvement DVI lecture was badly rated Bayesian network was too difficult Workload was too high considering there was cybercrime as well. Also, the different computer programs were hard to download (especially on a macbook)
Response lecturer: Course was well evaluated particularly on activating teaching, academic challenge and developing statistical skills. The body fluid assignment is indeed a bit off topic. It is a very interesting topic, but it is more focused on how to do it, rather than on the interpretation. It will be replaced by an assignment on DNA typing techniques. Lower rating of DVI guest lecture probably had to do with the online situation. Hopefully next year the lecture can be given again on campus. The Bayesian network assignment is a high level assignment. Some students asked for feedback and the teacher gave it. Some groups did not ask for feedback at all. If needed a feedback session could be introduced. Students were instructed as preparation to download computer programs and to work with the test files. The announcements were done on time. This preparation will be asked again next year. That said, troubleshooting is one of the things that works better on campus, because teachers can quickly help students on their way in case they encounter issues.

Contact information

Coordinator

dr. E. Musta

Owner	Master Forensic Science
Coordinator	dr. E. Musta
Part of	Master Forensic Science, year 1

Forensic Statistics and DNA-evidence