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Next-Generation Sequencing in the Diagnosis of Dementia And Next‐generation sequencing in the diagnosis of Dementia and Huntington’s disease Phenocopy Syndromes A thesis submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy from University College London Carolin Anna Maria Koriath Institute of Neurology Department of Molecular Neurodegeneration University College London 2020 1 2 Declaration I, Carolin Anna Maria Koriath, confirm that the work presented in this thesis is my own. Where information has been derived from other sources, I confirm that this has been indicated in the thesis. However, contemporary, large‐scale projects require a complex set of skills and extensive work best provided by a team. Like many studies nowadays, the investigation and analysis of the MRC Dementia Gene Panel study were based on a collaborative effort and contributions from different members of the Human Genetics Programme at the MRC Prion Institute at UCL. I have personally performed a significant amount of the laboratory work and will highlight these contributions in the methods section; I have also performed all stages of data processing. Namely, I have analysed all next‐generation sequencing data and developed criteria for the correct classification of identified variants. For time and cost advantages, samples for the Huntington’s disease phenocopy study were whole‐genome sequenced and aligned at Edinburgh Genomics, but I performed the data analysis. I have put this data into context with the clinical data for further analysis and the statistical workup, and, in close dialogue with my supervisors Prof Simon Mead and Prof Sarah Tabrizi, have drawn conclusions from all available data which are set out in this PhD thesis. 3 4 Abstract Dementia is a major cause of disability worldwide, especially in the elderly. While Mendelian causes of dementia only account for a small proportion of cases, their role in elucidating the pathophysiology has been paramount. Genetically defined cohorts also offer opportunities for trials of disease‐modifying treatments, even before the onset of symptoms. Previously, only a small number of genes could be selected for genetic testing because of cost‐restrictions, but the advent of next‐generation sequencing has enabled its more widespread use. This thesis explored the use of next‐generation sequencing in patients living with dementia and HD phenocopy (HDPC) syndromes, who include patients with mixed presentations of dementia and motor symptoms. Using a validated 17 gene Dementia Gene panel supplemented by C9orf72 expansion testing and Apolipoprotein (ApoE) genotyping in over 3000 patients and controls, I determined the success rate of genetic panel testing in dementia; I developed an algorithm for the selection of patients for genetic testing based on the clinical presentation and common predictors of genetic causes of dementia. A detailed analysis of the ApoE data in the frontotemporal dementia cohort revealed strong effects of ApoE4 on age at onset in the subset with proven or suspected tau neuropathology, as well as opposite effects of amyloid‐beta pathology. In order to improve the definition and diagnostic rate of HDPC syndromes, patients who were referred for HD testing from two clinics were compared based on their clinical presentation; patients could not be distinguished based on clinical presentation alone, even if analysed as patterns. Given the low success rate of dementia gene panel testing in the HDPC cohort, 50 patients were selected for whole‐genome sequencing based on their HD‐likeness and their likelihood of harbouring a Mendelian variant. The results revealed a number of variants of interest but require replication. 5 6 Impact Statement Due to the rising prevalence of dementia, interest in related research has been growing. Genetic causes of dementia have explained underlying processes and paved the way for trials of disease‐modifying treatments; however genetic testing is still underutilized in clinic, due to longstanding perceptions of high costs long turn‐around times, and lack of treatment options. Based on over 3000 patient and control samples, the results of my research could improve clinical genetic testing and counselling. I established likely discovery rates for genetic testing in both rare and common dementias, and developed an algorithm for the selection of patients based on predictors of genetic disease. This has implications for diagnostic options of patients living with dementia and their counselling regarding the success rate and the implications for family members. The discovery rates would warrant more wide‐spread testing, supporting future treatment trials. I also found many mutations in dementia genes not previously linked to the syndrome, many that had not been described previously, and that some patients carried more than one deleterious mutation. This means that picking genes based on clinical presentation risks missing mutations, that the known genes still harbour undescribed causal mutations, and that concurrent mutations may be missed if few genes are sequenced, with consequences for predictive testing of relatives. I could show that ApoE4, a risk factor for Alzheimer’s disease, was linked to earlier disease onset in patients with frontotemporal dementia and tau neuropathology, which may make in relevant in other types of dementia. Harnessing the statistical power of this dataset, I was able establish a frequency threshold in the population for mutations expected to cause early‐onset dementia (EOD) in all patients who carry it (100% penetrance). The findings imply that not all variants published as deleterious will cause disease, and that mutations found in patients need to be compared to population databases to prove pathogenicity. This has direct impact on the assessment of novel variants and which ones are reported 7 back to patients. This work has been published to disseminate it to clinician’s the world over. Focusing on a subset of patients with poor diagnostic rates using focused genetic testing, I attempted to improve the definition and diagnostic rate of Huntington’s disease phenocopy (HDPC) syndromes which include mixed presentations of dementia and motor symptoms. They are an excellent example of unclear neurodegenerative disease; clarifying their definition would improve the prediction of whose Huntington’s disease (HD) test will be positive and, consequently, counselling of patients. However, HD and HDPC patients could not be distinguished based on clinical presentation alone, even if analysed as patterns. In a further attempt to better define these HDPC syndromes, I analysed whole‐genome sequencing (WGS) of 50 HDPC patients to find a genetic link between these patients. However, with the exception of one known HDPC syndrome, none of the suspicious variants discovered in the dataset could be sufficiently verified as causal without replication. This requires further work, but could ultimately help elucidate the affected pathways both in patients with HD and HDPC syndromes. 8 Acknowledgements I am very grateful to patients and volunteers who have donated blood samples and their time to research and have made this study possible. I would like to thank my PhD supervisors Professor Simon Mead, Professor Sarah Tabrizi, Dr Jonathan Rohrer and Dr Edward Wild for their continued support and guidance. I would also like to thank Gary Adamson, Ronald Druyeh, William Taylor, Athanasios Dimitriadis, Penny Norsworthy, Tracy Campbell, and Lee Darwent for their tireless work in the laboratory, as well as Dr Janna Kenny and Dr Jon Beck for the work they previously dedicated to the gene‐panel sequencing project. I would like to thank Dr Holger Hummerich, Fernando Guntoro, and Liam Quinn for bio‐informatic support, Irina Neves Simoes and Dr TzeHow Mok for their help gathering clinical information, and Helen Speedy for help accessing the Genomics England project. I would like to thank the Leonard Wolfson Foundation for funding my PhD, the CHDI Foundation, and particularly Dr Thomas Vogt, for their funding of the HD phenocopy whole‐genome sequencing project, the Biomedical Research Unit at UCL, the Medical Research Council UK and the MRC Institute for Prion diseases at UCL for their support of this project. Finally, I would like to thank my parents Dres. Joachim and Elisabeth Cyran for their support over the years, my husband Alexander Koriath for his love and for his interest in my PhD project and thesis, as well as my sons Carl Oscar and Conrad Otto for the welcome distraction they provided. 9 10 Table of Contents Declaration 3 Abstract 5 Impact Statement 7 Acknowledgements 9 Table of Contents 11 Table of Figures 19 Table of Tables 20 Abbreviations 22 I. Introduction 24 1 Dementia(s) through the ages 24 A. Traditional hurdles and new challenges 24 B. Clinical heterogeneity in autosomal dominant dementia 26 a. Genetic factors in dementia syndromes 26 b. Alzheimer’s disease 28 c. Frontotemporal dementia 30 d. Prion disease 31 e. Huntington’s disease and Huntington’s disease phenocopy syndromes 32 2 Advances in genetic sequencing methods 33 A. Single gene testing 34 B. Multiple gene testing 35 C. Testing for nucleotide repeat expansions 37 3 Challenges and questions in genetic dementia 38 D. Variant identification and classification 38 a. Classification criteria and variants of uncertain significance 38 b. Secondary findings 39 11 E. Ethical considerations 39 F. Project Hypotheses 40 II. Materials and Methods 45 1 Cohort stratification scores 45 A. The Goldman Score 45 B. The HD Phenocopy Score 46 2 Sample DNA‐extraction and control 46 A. Blood 46 B. Saliva 47 C. DNA quality control on the Agilent 4200 TapeStation System ® 48 3 Gene‐panel sequencing of dementia syndromes 48 A. Selection of samples for gene‐panel sequencing 48 a. Patient samples for gene‐panel sequencing 48 b. Control samples for gene‐panel sequencing 51 c. Clinical predictors 51 B. Next‐generation gene panel sequencing 51 a. The IonTorrent® PGM platform 51 i. The MRC dementia gene panel 52 ii. Primer Design for IonTorrent ® sequencing 53 iii. Library Preparation, Template Preparation and Sequencing 53 iv. Analysis using NextGENe® and GeneticistAssistant® 54 b.
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