Phenotypic Impact of Genetic Risk Pathways for Alzheimer’s Disease by Daniel Felsky A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy Institute of Medical Science University of Toronto © Copyright by Daniel Felsky 2016 Phenotypic Impact of a Genetic Risk Pathway for Alzheimer’s Disease Daniel Felsky Doctor of Philosophy Institute of Medical Science University of Toronto 2016 Abstract The contribution of genetic variation to risk for late-onset Alzheimer’s disease is well-accepted; however, the roles of specific mutations within established risk genes are not clear. Comprehensive datasets with informative in vivo and postmortem biomarkers now offer the opportunity to understand when, where, and how mutations within these genes individually exert their effects on the brain. Moreover, it is known that many of these genes interact at the pathway level, and therefore genetic effects should also be considered in context using gene-gene interaction approaches. I hypothesized that common functional variants modifying established Alzheimer’s risk pathways would demonstrate a) independent effects and b) synergistic effects on human brain structure and other Alzheimer’s biomarkers. First, the Apolipoprotein E (APOE) gene ε4 allele was found to be associated with white matter integrity in an age-dependent manner. Second, mutations within the sortilin-like receptor (SORL1) gene were associated with differences in white matter integrity, SORL1 gene mRNA expression, and amyloid ii neuropathology that suggested an early genetic risk mechanism beginning as early as childhood. Third, a translocator protein (TSPO) gene variant known to alter TSPO binding characteristics was found to have no direct effects on inflammatory and cerebrovascular brain changes in over 2 300 elderly subjects. Finally, based on evidence from recent human stem cell experiments, RNA sequencing was used to identify a novel interaction of gene variants across the SORL1 gene with the brain derived neurotrophic factor (BDNF) Val66Met polymorphism regulating isoform- specific SORL1 expression related to amyloid pathology and brain structural alterations. Altogether, these experiments demonstrate that some genetic modifiers of AD risk pathways are linked either directly via biochemical function or indirectly via the convergence of pathways they influence. These studies have begun to parse the immense heterogeneity of the Alzheimer’s disease diagnosis as well as uncover distinct genetically-defined molecular subtypes of at-risk individuals who should be targeted in future therapeutic trials. Novel interventions designed to engage specific neural circuits or molecular pathways would be of most benefit to the molecular subtypes in which they are most greatly altered. iii Acknowledgments I am endlessly grateful for the people and opportunities that have shaped my experience over the last several years. I am among a very lucky few to have been supervised by such exceptional mentors, Aristotle Voineskos and James Kennedy, without whom none of this would have happened. To the members of my Program Advisory Committee, Jo Knight, Jason Lerch, and Bruce Pollock, I cannot thank you enough for your support and stamina. You have all set examples that I strive to follow. For the efforts and intelligence of my colleagues, by which I have been inspired over the years, I am also grateful: to Jon, my moral Sherpa, for his guidance, support, example of character, and editing assistance; to Tina B, for being a model of resilience, achievement, and friendship; to Tris and Colin, for their scientific passion and fervent debates; to Julie, for her optimism, dependable virtue, and flailing gestures; to Arash and Tina R, for their unparalleled intellect, enthusiasm, and humility; to Anne, for her uplifting drive and kindness; to Joe, for his command of comprehension, thirst for literature, and taste in music; to Nikhil, for his genuine scientific intrigue and companionship; to David, for his acerbic wit and commitment to the task; to Mallar, for his forthright and collaborative manner; to Vince and Nick, for their delightfully calm demeanors and remarkable insight; to Clement and Arun, for their patience and unwavering support of all in the Kennedy lab; and to Sajid, for the Turkish pizza and for starting it all. It seems that academic training generates much suffering, to a degree greater than equally time- consuming pursuits. Some explain this as the discouraging endlessness of the quest for knowledge, but I think that well before that quest begins we struggle most with our self- perceptions of competence; the so-called imposter phenomenon. Like many graduate students, I often question my own abilities and in many ways this motivates me toward self-awareness and methodological rigor. I have realized over the years that understanding my shortcomings only makes me more like those I admire and feel so far behind. However, while being aware of your weaknesses is useful for suppressing egotism and its degenerative effects on good science, the greater challenge may be trusting your strengths, such that you may shift the weight of your demands to those areas in which you are most able to bear it. I believe that anyone is ultimately capable of learning anything, and that much suffering could be avoided if more people, particularly those committed to studentship, believed it too. iv I dedicate this thesis to my family, for demonstrating the purpose and meaning of life… and for relinquishing painful sums of money during my dependent years. I will do my best to put your investment to good use. My hope is to avoid a career of brickmaking, and that my effort will not contribute to chaos in the brickyard, but rather to the construction of useful edifices (Forscher, 1963). v Contributions Sources of data varied by study and have been cited in the methods section of each chapter. The author (Daniel Felsky) performed all experiments as outlined in each chapter, with the following exceptions: Chapter 3: Magnetic Resonance Imaging (MRI) data was collected by scan technicians at Toronto General Hospital (TGH) as part of a larger study recruiting out of the Centre for Addiction and Mental Health (CAMH). Whole brain tractography and fibre clustering for some subjects was performed by Dr. Aristotle Voineskos. Chapter 4: MRI data were collected independently at TGH and Zucker Hillside Hospital (ZHH, Glen Oaks, NY, USA) by local scan technicians. Preprocessing of MRI data from ZHH was performed by Dr. Philip Sczezko and Dr. Toshikazu Ikuta. Neuropathological assessments in the Religious Orders Study (ROS) and Memory and Aging Project (MAP) were overseen by Dr. Julie A. Schneider. Preprocessing and initial summary statistical analyses of postmortem neuropathology data were performed by Dr. Lei Yu. Chapter 5: Alzheimer’s Disease Neuroimaging Initiative (ADNI) data were acquired at multiple sites across the United States and Canada, and are stored and maintained in the Laboratory of Neuro Imaging (LONI) Image Data Archive (IDA) at University of Southern California (Los Angeles, CA). Clinical, imaging, and genetic ROS/MAP data are stored and maintained at the Rush Alzheimer’s Disease Center (RADC) at the Rush University Medical Center (Chicago, IL, USA). Neuropathological assessments were overseen by Dr. Julie A. Schneider. Microglia counts were obtained by trained research assistants and post-doctoral fellows at Rush University Medical Center. ROS/MAP genomic imputation was overseen by Dr. Philip L. De Jager. ROS/MAP MRI data were gathered as part of an imaging sub-study run by Drs. Zoe Arvanitakis and Konstantinos Arfaniakis. Dr. Debra Fleischman contributed blood-based biomarker data. Chapter 6: RNA sequencing data were preprocessed and compiled by Jishu Xu. Sources of neuroimaging data for ADNI and ROS/MAP datasets were the same as from Chapter 5. vi Table of Contents Acknowledgments ..................................................................................................................... iv Contributions ............................................................................................................................. vi Table of Contents ..................................................................................................................... vii List of Tables ........................................................................................................................... xii List of Figures ......................................................................................................................... xiii List of Abbreviations ................................................................................................................ xv Chapter 1 .............................................................................................................................. 1 1 Literature Review .................................................................................................................. 1 1.1 Alzheimer’s Disease ....................................................................................................... 1 1.1.1 Discovery and Early History ................................................................................ 1 1.1.2 Diagnosis ............................................................................................................ 3 1.1.3 Heterogenety and Subtypes ................................................................................. 5 1.1.4 Prevalence and Economic