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Applications of Massively Parallel Sequencing in Forensic Genetics

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Applications of Massively Parallel Sequencing in Forensic Genetics Applications of massively parallel sequencing in forensic genetics Eirik Natås Hanssen 25th January 2018 Thesis submitted for the degree of Philosophiae Doctor Institute of clinical medicine, University of Oslo Department of forensic sciences, Oslo university hospital © Eirik Natås Hanssen, 2018 Series of dissertations submitted to the Faculty of Medicine, University of Oslo ISBN 978-82-8377-256-2 All rights reserved. No part of this publication may be reproduced or transmitted, in any form or by any means, without permission. Cover: Hanne Baadsgaard Utigard. Print production: Reprosentralen, University of Oslo. Acknowledgements This thesis is a result of work done during the period from 2014 to 2018. Although the main focus has been forensic genetics, the project has been highly dependent on the knowledge of experts from other fields of science such as medical and microbial genetics, informatics and statistics. These experts represent different Norwegian research institutions, and their expertise and hospitality have been exceptional. I’m grateful to many. First, I would like to thank my primary supervisor Peter Gill at University of Oslo (UiO)/Oslo university hospital (OUS) for sharing his great expertise, for thoughtful guidance and his patience during numerous rounds of proofreading. Thanks to my prior boss at the OUS forensic department Bente Mevåg for the opportunity to leave my casework duties part-time and for her approval of internal funding. I would also thank my current boss Solveig Jacobsen for continuing the support. I’m also thankful for my colleges’ extra efforts in my absence, especially during periods of high workload. Thanks to my co-supervisor Thore Egeland who has made essential contribu- tions to this project. With his large contact network, he has been my main door opener. I am especially thankful for the opportunity to be associated with his biostatistics group at Norwegian university of life science (NMBU) and for his contribution on paper I. Thanks to my co-supervisor Per Hoff-Olsen at UiO/OUS for sharing his ex- pertise in both forensic genetics and pathology, and for all proofreading. Thanks to Robert Lyle at UiO/OUS for introducing me to massively parallel sequencing, bioinformatics and for sharing his great expertise in genetics. Robert made essential efforts during the experimental phase, the writing process and dur- ing proofreading of paper I. Thanks to Knut Rudi for giving me access to his lab at NMBU and Ekaterina Avershina for teaching me microbiome sequencing. Especially thanks for the con- tributions to the experimental design, labwork guidance, writing and proofreading of article II and for being so positive and helpful. Paper II and III would not have been possible without the contribution from i my co-supervisor Lars Snipen. Thanks to Lars for welcoming me to NMBU and for sharing his great range of knowledge in biology, microbiology, informatics and statistics. Thanks for being inspirational. Thanks to the bioinformatics group members at NMBU for you warm inclu- sion. Especially thanks to Kristian Hovde Liland for his contributions on paper III. Last but not least I would like to thank my parents, my wife Kjersti and the kids Adrian and Julie for their love and support. I am really looking forward to our next vacation, kids! Oslo January 2018, Eirik Natås Hanssen ii List of papers [1] Hanssen, E. N., Lyle, R., Egeland, T. and Gill, P. “Degradation in forensic trace DNA samples explored by massively parallel sequencing”. In: Forensic Sci Int Genet 27 (Mar. 2017), pp. 160–166. DOI: 10.1016/j.fsigen. 2017.01.002. [2] Hanssen, E. N., Avershina, E., Rudi, K., Gill, P. and Snipen, L. “Body fluid prediction from microbial patterns for forensic application”. In: Forensic Sci Int Genet (June 2017). DOI: 10.1016/j.fsigen.2017.05.009. [3] Hanssen, E. N., Liland, K., Gill, P. and Snipen, L. “Optimizing body fluid recognition from microbial taxonomic profiles”. In: Manuscript submitted to BMC Bioinformatics (5th Nov. 2017). iii iv Summary In forensic genetics, the main purpose has been to support the identification of biological trace samples through DNA analysis. This has been done by using poly- merase chain reaction to target and amplify certain short tandem repeat markers and then separate the different amplified fragments by length using capillary elec- trophoresis. The method has been the gold standard for decades and has been used for generating practically all DNA-profiles stored in national databases around the world. Because of the high level of standardization necessary, the old technology will probably still be used for many years to come. However, massively parallel sequencing platforms have become a promising alternative to the capillary elec- trophoresis, by having the potential to both improve the current forensic routine analysis and to provide information beyond identification. During the work with this thesis, we have investigated these new possibilities and made contributions in two important and challenging fields of forensic genetics. DNA degradation is a key obstacle for a successful analysis. During degrad- ation, the DNA molecules are cleaved into shorter fragments, and the more the DNA is affected the less efficient the polymerase chain reaction will be. In the worst cases, the short tandem repeat markers will not be sufficiently amplified to be detected. In living cells, DNA associated with proteins or DNA present in higher ordered structure is shielded against degradation. We performed whole genome se- quencing on 4 degraded samples to investigate if this also applies to biological trace material. The sequencing coverage data were adjusted and filtered for GC- effect and low mappability regions respectively, and then used as an expression for the relative amount of DNA present at any genomic region. High abundant regions would be interpreted as regions resistant to degradation and vice versa. However, we found the coverage data to be evenly distributed at the genomic level, the chromosomal level and the sequence level and concluded that for biological trace material, DNA degrades at an even rate throughout the genome. The lack of certain robust DNA regions put a stop to our intention to target such regions in or- der to develop a superior performing method for analysing degraded trace samples. However, the fact that the degradation rate seems even throughout the genome is v still highly relevant information when developing new MPS based methods. Information on type of body fluid might be valuable in some cases. Testing for body fluids has traditionally been done by detecting enzyme activity or im- munoaffinity. However, these tests can be inaccurate and some have high false positive rates. Alternatively, new gene expression based methods have been de- veloped. These show higher accuracy by measuring body fluid specific mRNAs and miRNAs but have yet not found a wide-spread use. As accurate body fluid prediction is still challenging, we have developed another genetic-based method, primarily meant as a supplement to the gene expression methods. Our method takes advantage of the knowledge generated by health-related studies where it has been shown that bacteria-rich body fluids have a reasonable steady bacterial com- position across individuals. These studies have also developed standard laboratory protocols and data handling workflows. In the laboratory, the bacteria in every sample is detected by sequencing different regions in the 16S mRNA gene. The subsequent data handling workflow starts with the building of taxonomic profiles which each represent the bacterial composition of a sample. Then, the dimension of the data is typically reduced by principal component analysis and used as input for a mathematical model such as linear discriminant analysis. For our initial experimental setup, we used saliva on skin as a study model and sampled 6 different samples from 6 individuals. We used the mentioned stand- ard procedures and tailored the design to measure method performance and the effect from what we regarded as critical factors. Variance analysis of the results confirmed the strong association between bacterial composition and body fluid, but also a weaker effect from person was observed. Other factors such as PCR technique (conventional and digital droplet PCR), sampling technique (tape and synthetic swab) or technical replicates (parallel 1 and 2), had no significant effect. A cross-validation using the experimental data gave an accuracy of 94%, but there was a clear bias when comparing the experimental data to data from the Human microbiome project. However, by changing from the standard to a customized data handling workflow, we were able to remove this bias. The new data handling workflow comprised of a combination of partial least square regression and linear discriminant analysis. In addition, the taxonomic profiles were build using dir- ect binning to taxa instead of the standard binning to taxonomic operational units. When using data from the Human microbiome project for training the linear dis- criminant regression model and data from the American gut project for testing, we achieved an accuracy of 96%. Microbial data for feces, saliva, nasal and vaginal body fluids were included in these data sets. Although our method for body fluid prediction is still not ready for casework, we have shown that it has the potential to provide high accuracy and that it seems robust enough to be implemented without excessive intra-laboratory validation ef- vi forts. Further work is still needed to find the optimal calculation settings for highest possible accuracy and to develop an interpretation tool for mixtures of body fluids. In addition, a larger inter-laboratory validation study needs to be done. vii viii Contents Acknowledgements i List of papers iii Summary v Abbreviations xi 1 Introduction 1 1.1 Background . .1 1.1.1 The DNA molecule . .1 1.1.2 Human identification . .3 1.2 Limitations of human identification . .4 1.3 Beyond human identification . .5 1.4 DNA sequencing .
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