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Gene discovery in individuals from families indicative of Mendelian forms of late onset Alzheimer disease Greenwood, Talitha 2014

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GENE DISCOVERY IN INDIVIDUALS FROM FAMILIES INDICATIVE OF MENDELIAN FORMS OF LATE ONSET ALZHEIMER DISEASE by Talitha Greenwood  B.Sc.H., University of Toronto, 2003  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Medical Genetics)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)  November 2014  © Talitha Greenwood, 2014 ii  Abstract  Globally, approximately 35.6 million people live with dementia, with a yearly incident increase of approximately 7.7 million.  Factoring in the aging population and increasing life expectancies, current projections predict that by 2050, global prevalence of dementia will reach 155 million.  Alzheimer disease (AD) is the most common cause of dementia accounting for 60-80% of cases.  AD is a complex and genetically heterogeneous condition.  Most cases are the result of multifactorial inheritance with advancing age being the greatest contributor to risk; however, approximately 5% of AD occurs in the context of a dominant family history.  Because there is such a strong association between young onset age (before age 60-65) and dominantly inherited AD, it is unclear how often late-onset Alzheimer disease (LOAD) is due to single gene inheritance.  We hypothesize that LOAD in multi-incident families is, at times, caused by single gene mutation, in either the 3 genes known to cause early-onset AD (PSEN1, PSEN2 and APP) or genes not previously associated with AD.  Family history data from attendees of a referral-based memory disorder clinic were entered into a new comprehensive database which allowed selection of thirteen families suggestive of dominantly inherited LOAD.  A targeted gene panel containing the coding region of 177 genes implicated in dementia and other neurodegenerative conditions was used to screen for pathogenic mutations in our candidate families.  We identified 97 missense variants and 1 nonsense variant, including mutations in PSEN1 (p.I437V), PSEN2 (p.S130L), DNAJC13 (p.N855S), DCTN1 (p.T147A) and LMNA (p.N459S).  Our findings justify offering diagnostic genetic testing to individuals symptomatic for LOAD with family histories suggestive of autosomal dominant inheritance.  Currently, such testing is only offered to individuals with early-onset disease.  This research also provides a useful framework for ongoing iii  gene discovery in LOAD and other dementias utilizing the family history database and population-based DNA samples available at the University of British Columbia Hospital Clinic for Alzheimer Disease and Related Disorders (UBCH-CARD). iv  Preface This dissertation is an original work and intellectual product of Talitha M. Greenwood.  The raw data and DNA samples for this research were collected by the geneticist and genetic counsellors at the University of British Columbia Hospital Clinic for Alzheimer Disease and Related Disorders starting in 1984.  A basic participant database pre-existed this research that was maintained by research assistants, Dr. D. Dickstein, M. Tierney and M. de Lemos, under the supervision of Dr. A.D. Sadovnick.  I designed and completed entry for the new comprehensive family history database with technical assistance and advice from database manager K. Atkins.  I selected the cases for study.  Next generation sequencing techniques were selected by me in consultation with Drs. M.J. Farrer and C. Vilariño-Güell.  The targeted gene panel for dementia and other neurodegenerative disorders was created by Dr. M.J. Farrer prior to this research.  Targeted sequencing, including bioinformatics analysis, was conducted by Dr. M.J. Farrer and the Centre for Applied Neurogenetics.  Sanger sequencing validation of novel variants and mutations of interest was conducted by Dr. C. Vilariño-Güell.  Genotyping of additional participants for DNAJC13 (p.N855S) was done by me with help from Dr. C. Vilariño-Güell.  Drs. I.R. Mackenzie  and V. Hirsch-Reinshagen reexamined the neuropathology slides for available cases, thus providing the autopsy phenotype.  Analysis and interpretation of findings was conducted by me with generous contribution of expertise by my supervisor Dr. A.D. Sadovnick, and advisory committee members Drs. C. Vilariño-Güell and G.Y.R. Hsiung.  This research was conducted with the approval of the University of British Columbia office of research ethics, clinical research ethics board under certificate H07-03022. v  Table of Contents  Abstract .......................................................................................................................................... ii Preface ........................................................................................................................................... iv Table of Contents ...........................................................................................................................v List of Tables ................................................................................................................................ ix List of Figures .................................................................................................................................x List of Symbols ............................................................................................................................ xii List of Abbreviations ................................................................................................................. xiii Glossary ...................................................................................................................................... xvi Acknowledgements .................................................................................................................. xviii Dedication .....................................................................................................................................xx Chapter 1: Introduction ................................................................................................................1 1.1 Alzheimer disease ........................................................................................................... 3 1.1.1 Diagnostic criteria ....................................................................................................... 3 1.1.1.1 National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer’s Disease and Related Disorders Association Criteria ......................... 4 1.1.1.2 Diagnostic and Statistical Manual of Mental Disorders, Version III, Revised ... 6 1.1.1.3 Cognitive Assessment Tools and Dementia Rating Scales............................... 10 1.1.1.4 Neuropathology criteria .................................................................................... 11 1.1.2 Pathophysiology ........................................................................................................ 14 1.1.2.1 AP ..................................................................................................................... 14 1.1.2.2 NFT ................................................................................................................... 14 vi  1.1.3 Genetics of AD ......................................................................................................... 15 1.1.3.1 Pathogenic mutations ........................................................................................ 16 1.1.3.2 Susceptibility genes .......................................................................................... 17 1.1.3.3 Genome-wide association studies ..................................................................... 18 1.1.3.4 Genetic counselling and testing for Alzheimer disease .................................... 20 1.2 Hypotheses .................................................................................................................... 23 1.3 Objectives ..................................................................................................................... 23 Chapter 2: Materials and Methods ............................................................................................24 2.1 The Family History Database ....................................................................................... 24 2.1.1 Proband data form ..................................................................................................... 29 2.1.2 Pedigree data form .................................................................................................... 36 2.1.3 Contents of the database ........................................................................................... 43 2.1.4 Family selection and demographics .......................................................................... 44 2.2 Next generation sequencing .......................................................................................... 45 2.2.1 Targeted panel genes................................................................................................. 45 2.2.2 Additional screening for mutations of interest.......................................................... 50 2.2.3 Haplotype analysis .................................................................................................... 50 Chapter 3: Results........................................................................................................................51 3.1 Sequencing Results ....................................................................................................... 57 3.1.1 Family 1 .................................................................................................................... 64 3.1.1.1 PSEN1 c.1311 A>G (p.I437V) ......................................................................... 64 3.1.1.2 Family details .................................................................................................... 64 3.1.1.3 Clinical and pathological phenotype ................................................................. 65 vii  3.1.1.4 Summary ........................................................................................................... 66 3.1.2 Family 2 .................................................................................................................... 69 3.1.2.1 PSEN2 c.388 C>T (p.S130L)............................................................................ 69 3.1.2.2 Family details .................................................................................................... 69 3.1.2.3 Clinical and pathological phenotype ................................................................. 70 3.1.2.4 Summary ........................................................................................................... 70 3.1.3 Family 12 .................................................................................................................. 73 3.1.3.1 DNAJC13c.2563 A>G (p.N855S)..................................................................... 73 3.1.3.2 APOE c.136 T>C (p.L46P) ............................................................................... 74 3.1.3.3 Family details .................................................................................................... 74 3.1.3.4 Clinical and pathological phenotype ................................................................. 74 3.1.3.4.1 Carrier for DNAJC13c.2563 A>G (p.N855S) ............................................. 75 3.1.3.4.2 Carriers for APOE c.136 T>C (p.L46P) ...................................................... 75 3.1.3.5 Screening for additional AD carriers of DNAJC13 p.N855S ........................... 75 3.1.3.6 Summary ........................................................................................................... 76 3.1.4 Family 13 .................................................................................................................. 77 3.1.4.1 LMNA c.1375 A>G (p.N459S) ......................................................................... 77 3.1.4.2 DCTN1 c.441 T>C (p.T147A) .......................................................................... 79 3.1.4.3 Family details .................................................................................................... 79 3.1.4.4 Clinical and pathological phenotype ................................................................. 79 3.1.4.5 Summary ........................................................................................................... 80 3.1.5 Additional mutations of unknown significance ........................................................ 81 3.1.5.1 Family 4 ............................................................................................................ 81 viii  3.1.5.2 Families 3 and 5-11 ........................................................................................... 84 Chapter 4: Discussion and Recommendations ..........................................................................86 4.1 Strengths and limitations............................................................................................... 86 4.2 Clinical recommendations: ........................................................................................... 88 4.2.1 Recommendation 1: Extend diagnostic genetic testing to LOAD cases ................... 88 4.2.2 Recommendation 2: Repeated testing in LOAD families with identified mutations 89 4.3 Future research directions ............................................................................................. 89 4.4 Conclusion .................................................................................................................... 92 Bibliography .................................................................................................................................93  ix  List of Tables  Table 1.1.  DSM diagnostic criteria across versions ....................................................................... 8 Table 1.2.  Summary of genes linked to AD through GWAS ...................................................... 19 Table 2.1.  Diagnostic categories at the UBCH-CARD................................................................ 27 Table 2.2.  Custom gene panel for neurodegenerative disease ..................................................... 48 Table 3.1.  Summary of candidate families .................................................................................. 52 Table 3.2. Results from targeted panel for variants with minor allele frequency <1% ................ 58 Table 3.3.  Sanger sequencing validation for variants of interest ................................................. 63 Table 3.4.  Protein conservation and damage scores for mutations of interest in families, 1, 2, 12 and 13 ............................................................................................................................................ 67 Table 3.5.  Pre- and post-validation variant details per family. .................................................... 85       x  List of Figures  Figure 1.1.  Dementia by etiology .................................................................................................. 2 Figure 1.2.  Cartoon depiction of CERAD semi-quantitative AP distribution and corresponding scores............................................................................................................................................. 13 Figure 2.1.  Benefit of code table vs. text-based entry in a record field. ...................................... 28 Figure 2.2.  Page 1 of the proband data form................................................................................ 30 Figure 2.3.  Page 2 of the proband data form................................................................................ 31 Figure 2.4.  Page 3 of the proband data form................................................................................ 32 Figure 2.5.  Page 4 of the proband data form................................................................................ 33 Figure 2.6.  Page 5 of the proband data form................................................................................ 34 Figure 2.7.  Page 6 of the proband data form................................................................................ 35 Figure 2.8.  Page 1 of the pedigree data form ............................................................................... 37 Figure 2.9.  Page 2 of the pedigree data form ............................................................................... 38 Figure 2.10.  Page 3 of the pedigree data form ............................................................................. 39 Figure 2.11.  Page 4 of the pedigree data form ............................................................................. 40 Figure 2.12.  Page 5 of the pedigree data form ............................................................................. 41 Figure 2.13.  Page 6 of the pedigree data form ............................................................................. 42 Figure 3.1 A-M.  Pedigrees for patients meeting criteria for familial LOAD. ............................. 53 Figure 3.2.  Amino acid conservation across species for PSEN1 ................................................. 67 Figure 3.3.  Neuropathology images for family 1 ......................................................................... 68 Figure 3.4.  Amino acid conservation across species for PSEN2 ................................................. 71 Figure 3.5.  Neuropathology images for Family 2 ........................................................................ 72 xi  Figure 3.6.  Amino acid conservation across species for DNAJC13 ............................................ 73 Figure 3.7.  Amino acid conservation across mammalian species for LMNA .............................. 78 Figure 3.8.  Amino acid conservation across vertebrates for DCTN1 .......................................... 79 Figure 3.9.  Neuropathology images for F4-II-1........................................................................... 83 Figure 4.1.  Decision tree for future gene discovery at the UBCH-CARD .................................. 91  xii  List of Symbols  + Indicates mixed dementia α Alpha β Beta ε Epsilon γ Gamma q  Signifies a query between two or more differential diagnoses  xiii  List of Abbreviations  3MS Modified mini-mental state test AA  Alzheimer’s association  AD Alzheimer disease ALS Amyotrophic lateral sclerosis AP Extracellular amyloid plaque APOE Apolipoprotein E APP Amyloid Precursor Protein Aβ/Aβ42 Amyloid beta/amyloid beta peptide 42 amino acid splice variant BACE1 Beta-secretase 1 bp Base pair CADASIL Cerebral autosomal-dominant arteriopathy with subcortical infarcts and leukoencephalopathy  CERAD Consortium to establish a registry for Alzheimer's disease CJD Creutzfeldt–Jakob disease CME Clathrin mediated endocytosis CMT/ CMT2 Charcot Marie Tooth disease / Charcot Marie Tooth disease type 2 COG Conserved oligomeric Golgi DGT Diagnostic genetic testing DSM/-III-R/ -IV/-IV-TR/-5  Diagnostic and statistical manual of mental disorders versions three-revised/four/four-text revision/five EOAD Early-onset Alzheimer disease xiv  ESP Exome sequencing project ET Essential Tremor FAD Familial Alzheimer disease FTD Frontotemporal dementia FUS Fused in sarcoma protein GDS Global deterioration scale GERP Genomic evolutionary rate profiling  GWAS Genome wide association study H&E Hematoxylin and eosin stain KHC Kinesin heavy chain LBD Lewy body dementia LOAD Late-onset Alzheimer disease MAF Minor allele frequency MCI Mild cognitive impairment  MMSE Mini mental state examination MoCA Montreal cognitive assessment MS Multiple sclerosis NCBI National Center for Biotechnology Information NCI Not cognitively impaired NFT Neurofibrillary tangle NHLBI National Heart, Lung, and Blood Institute NIA National Institute on Aging NINCDS- National Institute of Neurological and Communicative Disorders and Stroke xv  ADRDA and the Alzheimer’s Disease and Related Disorders Association  OMC Other medical conditions ONS Other neurological syndromes OR Odds ratio PD Parkinson disease PDD Parkinson disease with dementia PGT Predictive genetic testing PHF Paired helical filament PolyPhen2 Polymorphism phenotyping PS Perry syndrome SD Standard Deviation SIFT Sorting Intolerant From Tolerant SNP Single nucleotide polymorphism TDP43 Transactive response DNA binding protein 43 UBCH-CARD University of British Columbia Hospital Clinic for Alzheimer Disease and Related Disorders   UTR Untranslated region VaD Vascular dementia xvi  Glossary  This glossary contains specialized terminology central to this thesis.  Definitions are the authors unless otherwise indicated by the inclusion of a reference number(s).  GERP:  A computer algorithm used to quantify evolutionary conservation for particular genetic sequences/protein motifs.  It can be used to describe conservation in single amino acids or multi-codon segments.  It does this by comparing multiple species alignments to the human genome.  It compares the number of nucleotide substitutions expected under neutral conditions (no effect on organism fitness), to the actual number of observed substitutions.  GERP scores range from 6.18 to ≤-12.36.  Higher numbers indicate fewer changes than expected under neutral evolutionary conditions.  PolyPhen2:  A computer algorithm used to predict the effects of amino acid changes on protein function.  Changes in protein function are described as being ‘probably damaging’, ‘possibly damaging’, ‘benign’, or ‘unknown’.  The program uses annotated information regarding sequence, multiple species alignments and secondary protein structure to call the effect of a particular amino acid change.  Sequence information considers functional, bonding, and transmembrane sites.  Alignment information considers the presence of similar substitutions in homologous proteins.  Structure considerations include changes to side chains and changes in surface structure.  xvii  Proband:  The original subject of interest in a study.  In reference to this study, the proband is the first family member to attend the UBCH-CARD.  SIFT:  A computer algorithm used to predict the effects of amino acid changes on protein function.  It uses multi sequence alignments to call whether a substitution is likely to be deleterious based on conservation across orthologs and paralogs.  Variant nomenclature:  Nomenclature for reporting variants is standardized for clarity.  For this definition we will use the example DNAJC13 c.2563A>G (p.N855S).  The notation begins with the gene symbol (DNAJC13) followed by c. which indicates a coding DNA sequence.  The position of the nucleotide change (2563) is indicated numerically followed by the type of nucleotide change (A>G).  p. precedes the description of the codon change in the protein, which is reported in the format, symbol of canonical amino acid, numeric location of codon change, symbol for variant amino acid.  The example provided therefore, describes an adenine to guanine transition in the coding region of DNAJC13 at the 2563rd nucleotide in the gene sequence.  This causes a change in the protein product at codon 855 from asparagine to serine.  xviii  Acknowledgements  Thank you to my supervisor Dr. Dessa Sadovnick and my committee members Drs. Carles Vilariño-Güell and Robin Hsiung for their patience and support.  I am also grateful to the Pacific Alzheimer Research Foundation in partnership with the Alzheimer Society of Canada, and the Centre for Applied Neurogenetics for their financial support of this project.  Thank you to the patients and their families without whom this research would be impossible.  I have been blessed to belong to a wonderful lab here at UBC.  Words cannot express what their support, mentorship and assistance has meant to me over the years, but words will have to do.  Sura Alwan, Kevin Atkins, Rachel Butler, Maria Criscuoli, Michelle Eisner, Madonna de Lemos, Emily Dwosh, Colleen Guimond, Josh Lee, Caroline Lindholm, Ruth Thomas, Irene Yee, thank you for everything.  I offer my enduring gratitude to the faculty, staff and other students in the Department of Medical Genetics, with special thanks to Grace Tharmarajah, Jon Heppner, Kusala Pussegoda and Cheryl Bishop for the administrative, academic, and moral support.  As a candidate for Master of Science in the Department of Medical Genetics, I would be extremely remiss if I failed to acknowledge my family.  Thank you to my mother, Mary, for the xix  gift of nature inherited in your clever genes.  Thank you more for the gift of nurture that encouraged my curiosity and creativity into academic pursuits.  Thank you also to my dad, Michael, and my dear siblings, Sheila, Matt, Cindy-Anne and Wendy for the unconditional love, the faith you had in me to accomplish this goal, and for your generous gifts of dinners and airmiles which  relieved my financial burden and allowed me to concentrate on my studies.  My nieces Cheylsea, Kaitlynn, Tayler and Mackenzie also deserve special commendation.  Your love brings me joy, but more importantly, during the lowest moments working on this degree, knowing you look up to me helped me persevere and carry on to the next step.  I hope I have been an inspiration to you.  I am grateful to all my friends but special mention must be given to Basia Gajda, Charissa Fung, Vicky Ngai, Terence Fung, and Trish Lenz.  Finally, saving the most important acknowledgment for last, I must thank my fiancé Mike Brush.  No one has been more involved with my day to day successes and failures through this process than he.  His is my rock, my love, and the person who, by example, challenges me to be the best version of myself.  That encouragement has been instrumental in the completion of this thesis – thank you dear heart.  xx  Dedication       For Mike, my grand passion, and partner in all things.1  Chapter 1: Introduction  Dementia is an umbrella term used to describe a progressive neurodegenerative condition not ascribable to normal aging.  Symptoms affect multiple cognitive domains including memory, thinking, emotional regulation, and personality1.  Contributors to dementia can include advancing age, infectious agents, cancer, traumatic brain injury, and/or genetic factors.  There are multiple etiologies for dementia, with mixed etiologies also being common.  Alzheimer disease (AD) is the most common cause of dementia, accounting for 60-80% of cases of progressive cognitive decline2.  Vascular dementia (VaD) is the second most common cause of dementia, followed by Lewy body dementia (LBD) and Frontotemporal dementia (FTD).  Additional rare medical and neurological condition also account for a small fraction of cases2–7 (Fig. 1.1).  Globally, it is estimated that 35.6 million people live with dementia.  The annual incidence is approximately 7.7 million.  Factoring in the aging population and increasing life expectancies, current projections predict that by 2050, global prevalence of dementia will more than triple, to 155 million2.   2  Figure 1.1.  Dementia by etiology2–6    40% 15% 8% 3% 5% 8% 11% 10% AD AD+LBD LBD other FTD VaD Primary VaD + SecondaryAD Primary AD + Secondary VaD 3  1.1 Alzheimer disease The hallmark clinical symptoms of AD include insidious onset of progressive short- and long-term memory loss, geographic disorientation, word-finding difficulties, poverty of speech and diminished abilities with task execution8.  The primary risk factor is age, with lifetime risk for developing AD at 7% for the general population9 and prevalence approximately doubling every 5 years after age 65. Nonetheless, environmental exposures  and genetic factors also play a significant role10.  1.1.1 Diagnostic criteria Clinical diagnostic criteria exist for the differential diagnosis of dementia but a definitive diagnosis of AD requires pathological confirmation at brain-limited autopsy including microscopic tissue examination.  Concordance between clinical and neuropathological diagnoses of AD can be as high as 80% sensitivity and 87% specificity.11  The most common clinical diagnostic criteria used are those of the National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer’s Disease and Related Disorders Association (NINCDS-ADRDA)1 and the Diagnostic and Statistical Manual of Mental Disorders Third Edition Revised (DSM-III-R)12. These criteria assume that symptoms cannot be accounted for by a more likely cause and are fulfilled in part by the results of neuropsychiatric testing using validated cognitive assessment tools13–16.  4  1.1.1.1 National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer’s Disease and Related Disorders Association Criteria The NINCDS-ADRDA criteria were initially proposed in 1984 to serve as a clinical diagnostic tool.  The original criteria assumed a tight association between clinical symptoms and brain pathology and categorized AD into three diagnostic categories: possible; probable; and definite1.  The original criteria were a very effective tool, creating standardization in clinical and research settings.  Supported in part by the universal use of the 1984 criteria, the subsequent expanded understanding of the clinical features and pathological underpinnings of AD made it clear that the NINCDS-ADRDA criteria should be revisited.  Among the primary catalysts for revision was the realization that clinical symptoms and brain pathology do not concur as closely in AD as was thought to be the case in 1984.  AD is now recognized to have a broad clinical spectrum which is reflected in the new recommendations17.  In 2011, revised criteria were proposed as a result of the outcomes from three working groups convened by the National Institute on Aging (NIA) and the Alzheimer’s Association (AA) to examine the asymptomatic pre-clinical phase, the pre-dementia symptomatic phase, and the dementia phase. Major changes to the 1984 criteria include:  (i) codification of mild cognitive impairment (MCI) as a pre-dementia phase on the clinical spectrum for AD;  (ii) stricter criteria for differential diagnosis due to other dementing etiologies;  (iii) de-emphasis on memory decline as the hallmark symptom of AD;  5  (iv) addition of diagnostic genetic testing for cases indicative of autosomal dominant disease; (v) removal of age limits for the diagnosis of AD18.  Under the new NINCDS-ADRDA criteria clinical diagnosis of AD requires that criteria for all-cause dementia are met18.  These include cognitive or behavioural symptoms that: (i) interfere with the function of daily life; (ii) signify a decline from previous function; (iii) are not elsewise explained by temporary causes of delirium or a psychiatric event; (iv) demonstrate detectable cognitive impairment as identified through history-taking and/or application of a cognitive assessment tool; (v) impair at least two of the following: a. acquisition of new information; b. reasoning ability, judgment and complex task performance; c. visuospatial abilities; d. language function; e. typical demeanor, behaviour and personality.  Once the above criteria for all-type dementia are met, additional criteria are applied to classify AD as probable or possible18.  Cases are classified as probable AD if they demonstrate: 1. insidious onset; 6  2. progression of cognitive decline; 3. have prominent symptoms that are either a. amnestic or b. non-amnestic (including impairments in language, visuospatial reasoning and executive function).  Increased levels of certainty for classification of probable AD include documented decline observed through long-term patient follow-up and carrier status for a causative mutation in the patient or biological family member.  AD is classified as possible when symptoms adhere with those described above, but either indicate sudden onset or show insufficient evidence for progressive decline.  1.1.1.2 Diagnostic and Statistical Manual of Mental Disorders, Version III, Revised The Diagnostic and Statistical Manual of Mental Disorders (DSM) is now in its fifth version; nevertheless, version III-revised (III-R)12 is still used in clinical reports of AD for diagnostic continuity. DSM version IV19 and IV-text-revision (IV-TR)20 provide identical criteria to each other, and very similar criteria to version III-R, but the DSM-521 includes major changes to criteria (Table 1.1).  As with NINCDS-ADRDA, diagnostic criteria for dementia must first be met.  Dementia, according to the DSM-III-R, must significantly interfere with activities of daily life, and include the following in the absence of delirium, without evidence for non-organic mental disorders: 7  1. impairment in short- and long-term memory; 2. at least one of: a. impairment in abstract thinking; b. impaired judgment; c. other disturbances in higher cortical function, such as aphasia, apraxia, agnosia, and visuospatial disturbances; d. personality changes. Additional criteria specific to AD include: 3. insidious onset, and 4. progressive declining course.   8  Table 1.1.  DSM diagnostic criteria across versions12,19–21 DSM-III-R  criteria  for Primary Degenerative Dementia of the Alzheimer type DSM-IV and IV-TR criteria for Dementia of the Alzheimer Type DSM-5 criteria for Neurocognitive Disorder  A. Demonstrable evidence of impairment in short- and long-term memory.  Impairment in short-term memory (inability to learn new information) may be indicated by the inability to remember three objects after five minutes. Long-term memory impairment, inability to remember past personal information (e.g., what happened yesterday, birthplace, occupation) or facts of common knowledge (e.g., past Prime Ministers, well-known dates).  B. At least one of the following: i. impairment in abstract thinking, as  indicated by inability to find similarities and differences between related words, difficulty in defining words and concepts, and other similar tasks ii. impaired judgment, as indicated by inability to make reasonable plans to deal with interpersonal, family, and job-related problems and issues iii. other disturbances of higher cortical function, such as aphasia (disorder of language), apraxia (inability to carry out motor activities despite intact comprehension and motor function), agnosia (failure to recognize or identify objects despite intact sensory function), and "constructional difficulty" (e.g., inability to copy three-dimensional figures, assemble blocks, or arrange sticks in specific designs) A. The development of multiple cognitive deficits manifested by both  i. memory impairment (impaired ability to learn new information or to recall previously learned information)  ii. one (or more) of the following cognitive disturbances::  (a) aphasia (language disturbance)  (b) apraxia (impaired ability to carry out motor activities despite intact motor function)  (c) agnosia (failure to recognize or identify objects despite intact sensory function)  (d) disturbance in executive functioning (i.e., planning, organizing, sequencing, abstracting)  B. The cognitive deficits in Criteria A1 and A2 each cause significant impairment in social or occupational functioning and represent a significant decline from a previous level of functioning.   C. The course is characterized by gradual onset and continuing cognitive decline.    1. Evidence of significant cognitive decline from a previous level of performance in one or more cognitive domains — such as complex attention, executive function, learning, memory, language, perceptual-motor or social cognition.  This evidence should consist of:  Concern of the individual, a knowledgeable informant (such as a friend or family member), or the clinician that there’s been a significant decline in cognitive function; and  A substantial impairment in cognitive performance, preferably documented by standardized neuropsychological testing, or if neuropsychological testing isn’t available, another type of qualified assessment.  2. The cognitive deficits interfere with independence in everyday activities (e.g., at a minimum, requiring assistance with complex instrumental activities of daily living, such as paying bills or managing medications).  3. The cognitive deficits don’t occur exclusively in context of a delirium, and are not better explained by another mental disorder.  4. The cognitive deficits are not primarily attributable to another mental disorder (e.g., major depressive disorder, schizophrenia).  9  Table 1.1.  DSM diagnostic criteria across versions12,19–21 DSM-III-R  criteria  for Primary Degenerative Dementia of the Alzheimer type DSM-IV and IV-TR criteria for Dementia of the Alzheimer Type DSM-5 criteria for Neurocognitive Disorder  C. The disturbance in A and B significantly interferes with work or usual social activities or relationships with others.  D. Not occurring exclusively during the course of delirium. Either (1) or (2): (1) there is evidence from the history, physical examination, or laboratory tests of a specific organic factor (or factors) judged to be etiologically related to the disturbance (2) in the absence of such evidence, an etiologic organic factor can be presumed if the disturbance cannot be accounted for by any nonorganic mental disorder, e.g., major depression accounting for cognitive impairment  E. Insidious onset with a generally progressive deteriorating course  F. Exclusion of all other specific causes of dementia by history, physical examination and laboratory tests D. The cognitive deficits in Criteria A1 and A2 are not due to any of the following:  (1) other central nervous system conditions that cause progressive deficits in memory and cognition (e.g., cerebrovascular disease, Parkinson's disease, Huntington's disease, subdural hematoma, normal-pressure hydrocephalus, brain tumor)  (2) systemic conditions that are known to cause dementia (e.g., hypothyroidism, vitamin B or folic acid deficiency, niacin deficiency, hypercalcemia, neurosyphilis, HIV infection) substance-induced conditions   E. The deficits do not occur exclusively during the course of a delirium.   F. The disturbance is not better accounted for by another Axis I disorder (e.g., Major Depressive Episode, Schizophrenia).   The specific symptoms of Alzheimer’s disease are: 1. There is insidious onset and gradual progression of impairment in two or more cognitive domains 2. The following criteria are also met.  Evidence of a causative Alzheimer disease genetic mutation from family history or genetic testing.  Clear evidence of decline in memory and learning, and at least one other cognitive domain (based on detailed history or serial neuropsychological testing).   Steadily progressive, gradual decline in cognition without extended plateaus.   No evidence of mixed etiology.   10  1.1.1.3 Cognitive Assessment Tools and Dementia Rating Scales Several assessment tools quantify cognitive performance and enhance accuracy of clinical diagnoses. Commonly used tools include: the Mini Mental State Examination (MMSE)13, the Modified Mini-Mental State Test (3MS) 14, the Montreal Cognitive Assessment (MoCA)15, and the Global Deterioration Scale (GDS) 16.  The MMSE is a 30 point questionnaire that tests arithmetic memory and orientation13.  Test results are not sufficient to diagnose dementia in the absence of clinical context, but in general, scoring is rated as follows:  >27 – indicate normal function;   19 to 24 – indicate mild impairment;   10 to 18 – indicate moderate impairment;  < 9 – indicate severe impairment.  The 3MS is scored out of 100 and adds several points to the MMSE designed to refine the scoring, and better distinguish functional domains to aid in differential diagnosis14.  The convention for scoring dementia for the 3MS is:  >79 – indicates normal function;  49 to 78 – indicates impairment;  <48 – indicates severe impairment.  The MoCA is particularly well-suited for detection of mild cognitive impairment.  It is scored out of 30 with cut-off scores generally set at: 11   >27 – for normal cognition;  18 to 26 – for mild impairment;  10 to 17 – for moderate impairment;  <10 – for severe impairment15.  The GDS categorizes dementia progression into one of seven stages which include:  no cognitive decline;  very mild cognitive decline;  mild cognitive decline;  moderate cognitive decline;  moderately severe cognitive decline;  severe cognitive decline16.  1.1.1.4 Neuropathology criteria The microscopic features that define AD are intracellular neurofibrillary tangles (NFTs) and extracellular amyloid plaques (APs)22.  The distribution and severity of NFTs is scored according to Braak stages23.  Braak stages are grouped into clinically relevant categories that in general correspond to:  asymptomatic pre-clinical disease (stages I/II);  pre-dementia symptomatic AD (stages III/IV);  dementia phase AD (stages V/VI)24. 12  In stages I and II, NFTs are limited to the entorhinal cortex of the medial temporal lobes.  Stages III and IV are described as the limbic stages and involve expansion of NFTs into the hippocampus.  The final neocortical phase corresponds to Braak stage V/VI and shows extensive NFT distribution through the hippocampus with expansion into the isocortex.  Distribution of APs is quantified by criteria set out by the Consortium to Establish a Registry for Alzheimer's Disease (CERAD).  CERAD uses semi-quantitative assessments to describe AP distribution (example in Figure 1.2).  Therefore, AP staging is termed: (A) sparse; (B) moderate; (C) frequent.  Based on Braak and CERAD staging, the consensus statement for neuropathologic findings in the presence of clinical symptoms, outlines 3 levels of certainty for establishing the diagnosis of AD26.  The likelihood that dementia is due to AD are grouped as follows: (i) high: when NFT distribution is in Braak stage V or VI and APs are frequent per CERAD score; (ii) intermediate: when NFT distribution is in Braak stage III or IV with a CERAD moderate score for APs; (iii) low: when NFT distribution is in Braak stage I or II and AP distribution is CERAD infrequent25.   13  Figure 1.2.  Cartoon depiction of CERAD semi-quantitative AP distribution and corresponding scores.  The image represents density of plaques per square millimeter at approximately 100x microscopic field.          Sparse  Moderate  Frequent   14  1.1.2 Pathophysiology There are 2 pathological hallmarks of AD – APs and NFTs.  1.1.2.1 Amyloid Plaques AD is characterized pathologically in part by APs which are extracellular deposits of amyloid beta (Aβ)26.  Aβ is derived by proteolytic cleavage of the Amyloid Precursor Protein (APP).  If localized to the plasma membrane, APP is cleaved by α-secretase into a soluble non-toxic peptide.  However, if cleavage takes place under more acidic conditions, APP undergoes sequential cleavage by β- and γ- secretase26 producing the insoluble and toxic 42 amino acid peptide (Aβ42) associated with disease.  Aβ42 forms several different secondary structures, including dimers, oligomers and fibrils.  There is ongoing debate regarding which of these is the pathogenic agent27.  Under normal physiological conditions, APP and β-secretase (BACE1) are trafficked along non-overlapping pathways28–30.  Co-localization is required for the creation of Aβ42.  There is much debate regarding where β-cleavage takes place, including the early endosome26,28, the Trans-Golgi network31, and the recycling endosome29. There is general agreement, however, that the APP substrate for Aβ42 is derived via clathrin-mediated endocytosis (CME)28–30.  1.1.2.2 Neurofibrillary Tangles NFTs form when the microtubule-associated protein tau becomes hyper-phosphorylated and forms into a paired helical filament (PHF).  Under normal physiological conditions, tau plays a role in microtubule stabilization and axonal transport.  Tau is found predominantly in the axon and, to a lesser extent, in the dendrite of neurons.  The mechanism by which tau becomes hyper-15  phosphorylated is not well understood but it is clear that phosphorylation negatively regulates binding to microtubules.  Hyper-phosphorylated tau accumulates in the dendrite, where it aggregates into PHFs27.  There is ongoing debate about the nature of toxicity between AP and NFT.  According to the amyloid cascade hypothesis27, the formation of AP is the instigating event that drives disease progression, including NFT formation.  Support for this theory has been found in mouse models for AD.  Transgenic mice, which over-express Aβ42 also develop NFT, but there is no corresponding development of AP in transgenic mice which express transgenic tau27.  While dysfunction in tau may occur downstream of amyloid-toxicity, there is also evidence that Aβ42 and hyper-phosphorylated tau play a synergistic role in disease.  Immunization against Aβ42 effectively clears APs; but, disease progression continues.  This suggests that, once formed, NFTs drive neurodegeneration independent of Aβ4232.  Furthermore, transgenic mouse models lacking the gene for tau do not develop amyloid toxicity, even in the presence of Aβ4227.  These findings indicate that while Aβ42 and the formation of APs instigate disease progression, hyper-phosphorylation of tau and the formation of NFTs perpetuate and amplify neuronal death.  1.1.3 Genetics of AD AD is a complex and genetically heterogeneous condition.  Most cases are the result of multifactorial inheritance with advancing age being the greatest contributor to risk.  Still, having an affected family member also increases risk, 9,33 indicating a hereditary component and a role for susceptibility genes even in the absence of single gene inheritance.  Recurrence risks in 16  families are highly dependent on the number of affected individuals and the degree of relation between them.  For example, having a first degree relative with AD increases lifetime risk to 24% compared to 8% for the general populations9,33.  In contrast to sporadic disease, approximately 5% of AD occurs in the context of an autosomal dominant family history9.  Single pathogenic mutations have already been identified in some of these families.  It is likely that single pathogenic mutations are also responsible for disease in the remaining dominant families, but the causative mutations have yet to be identified.  Recessive or sex-linked forms of AD have not been observed. Genetic testing, either to confirm a diagnosis or to predict future disease with certainty, is not an option for the vast majority of individuals.   1.1.3.1 Pathogenic mutations To date, mutations in only three genes are known to cause autosomal dominant AD: Amyloid Beta Precursor Protein (APP); Presenilin1 (PSEN1 ); and Presenilin 2 (PSEN2) 34.  Typically, a mutation in one these genes results in first symptoms beginning before age 60 or 65 which is the canonical onset age cut off for early-onset Alzheimer disease (EOAD). However, later ages of onset, particularly among carriers of PSEN2 mutations, have been observed35 .  As there is such a strong association between young onset age and gene mutations, it is unclear how often these genes may be implicated in families with late-onset Alzheimer disease (LOAD).  Mutations in PSEN1 are the most commonly described cause of familial EOAD; with 185 pathogenic mutations reported to date, and an additional 8 yet to be validated22,36.  PSEN2 has at least 20 mutations described to date, although confirmation of pathogenicity is still required in 17  some cases22,36.  Of the 40 mutations described for APP to date, 6 are not pathogenic, and one still requires confirmation of pathogenicity22,36.  PSEN1 and PSEN2 code transmembrane proteins PS-1 and PS-2, and make up half of the tetrameric γ-secretase complex, responsible for the processing of APP.  γ-secretase constitutes the final cleavage step of one of two differential cleavage pathways that APP follows.  Mutations that lead to conformational changes in PS-1 and PS-2 allow preferential cleavage of APP into the toxic Aβ42 peptide37.  Mutations in APP, lead to changes in the APP protein which cause disease  either by increasing output of all cleavage products or by allowing preferential cleavage into Aβ4238–40.  1.1.3.2 Susceptibility genes Twin studies indicate that among non-familial cases of AD, 61% of the contributors to disease are genetic factors41.  The most prominent genetic risk factor for AD is Apolipoprotein E (APOE)42,43.  APOE is a lipoprotein, with many functions, including a role in clearing Aβ from the brain.  APOE is classified into 3 main allelic types, contingent on the amino acid residue present at codons 112 and 15844,45.  Allele frequencies vary between populations, but in general they can be estimated at 0.073 for ε2, 0.783 for ε3, and 0.143 for ε446.  Allele ε2 contains a cysteine residue at both locations and is the least common variant.  There is evidence that carriers of allele ε2 experience a significant protective effect against AD47.  Allele ε3 is characterized by the presence of cysteine112 and arginine158 and is considered the wild type form of APOE. Allele ε4 is defined by the presence of arginine at codons 112 and 158.  Being an APOEε4 carrier is the most significant genetic risk factor for AD.   18  The frequency of the  APOEε4 allele is enriched in people with AD48.  Risk to develop AD increases in a dose-dependent manner wherein one copy of ε4 increases the age specific risk by an odds ratio (OR) of 1.7 to 7.9 and two copies increase risk by 9.3 to 24.748–51.  The presence of allele ε4 also leads to younger age of onset by 6 to 7 years per copy42.  There is conflicting evidence for the interaction between sex and ε4 carrier status to the risk of developing AD.  One study found that male carriers of one or two ε4 alleles had a relative risk of 2.9 (CI 1.4-6.2) for developing AD compared to 1.4 (CI 1.0-1.9) for females carriers of one or two ε4 alleles52.  In contrast, Breitner et al found that female carriers for ε4 had an increased OR of 1.44 (0.77-2.68) for heterozygotes and 1.72 (0.57-5.20) for homozygotes.49,50  Phenotypically ε4 carrier status in women is predictive of lower cognitive test scores53, and increased AP and NFT burden on autopsy when compared to male counterparts54.  Initial research indicated that the risk for AD associated with APOEε4 was limited to Caucasians55.  Subsequent studies, however, indicate that there is also an increased risk of AD to ε4 carriers in African American (OR = 3.6 for 2 copies)56 and Hispanic (OR = 2.1 for 1 copy and 2.9 for 2 copies) 56 populations, albeit not to the same extent as in Caucasian populations.  1.1.3.3 Genome-wide association studies Extensive international efforts have been made to identify new genetic risk variants for AD through genome-wide association studies (GWAS).  To date, more than 20 loci have been associated with AD57.  These loci are involved variously with: immune and inflammatory response58; APP trafficking, processing and recycling; Aβ disposal; CME; tau pathology; 19  vulnerability to other neurodegenerative conditions; cytoskeletal regulation, DNA repair, cell cycle progression, and apoptosis; neuronal development; cell migration; cytoskeletal structure and axonal transport; and epigenetic regulation59–61 (Table 1.2).    20  1.1.3.4 Genetic counselling and testing for Alzheimer disease Genetic testing for AD is available in certain limited circumstances.  Testing is only offered for the genes that cause autosomal dominant inheritance of AD, as consensus within the genetic counselling and larger medical community is that genetic testing for AD susceptibility factors is unwarranted given that such susceptibility factors are neither necessary nor sufficient for disease outcome.  At the University of British Columbia Hospital Clinic for Alzheimer Disease and Related Disorders (UBCH-CARD) all patients have an opportunity to meet with an American and/or Canadian Board certified genetic counsellor.  Family history data are collected and validated using standard protocol62.  A  family history assessment is performed, which helps categorize AD cases as being either autosomal dominant, multi-incident (more than 2 affected family members, who are third-degree relatives or closer63), or sporadic in nature, in addition to distinguishing between early- and late-onset cases.  In the context of complementary family history, EOAD is often very suggestive of autosomal dominant inheritance.  The distinction between autosomal dominant and multi-incident AD in a family with late ages of onset is less clear.  This is due to the fact that in late-onset disease certain members of the family are uninformative regarding disease transmission due to death from unrelated causes before the expected age of onset for AD.  Diagnostic genetic testing (DGT) of the three genes known to cause familial EOAD (PSEN1, PSEN2 and APP) is offered to individuals symptomatic for EOAD if the family history suggests autosomal dominant inheritance.  Predictive genetic testing is offered only to individuals with a 21  family member who is a known carrier of a causative mutation in PSEN1, PSEN2 or APP.  Genetic testing is not available to individuals with family histories indicative of heritable LOAD as there are currently no causative genes associated with LOAD.  At the UBCH-CARD, uptake of DGT for familial EOAD in appropriate cases is high.  By contrast, there is very little uptake of predictive genetic testing (PGT)64.  Clinic records show that 55 individuals have received DGT, whereas only one individual has received PGT (n.b. there is no clinic record available of how many people have been offered either DGT or PGT, only of those who accepted).  Pre- and post-test genetic counselling is required for individuals who are interested in predictive testing for PSEN1, PSEN2 or APP63,65.  The potential risks and benefits of genetic testing are reviewed in detail with the decision whether or not to pursue testing being made at the individual’s discretion and in consideration of emotional, psychosocial, legal, and financial implications63,66.  The majority of patients who attend the UBCH-CARD provide informed consent for banking of a biological sample.  In 2013, 121 of 138 (87%) patients who were offered blood banking for research and future clinical testing accepted.  In so doing, patients who opt not to have clinical genetic testing, or do not meet criteria to have the test offered at present, preserve the opportunity to do so in future, if their circumstances, inclination, or the clinical utility of the test should change.  They also consent to the use of their sample for genetic research.  These practices are in accordance with recommendations published in 2011 by the American College of Medical Genetics and the National Society of Genetic Counselors63 and 22  recommendations from the 4th Canadian Consensus Conference on the Diagnosis and Treatment of Dementia67.   23  1.2 Hypotheses Hypothesis 1: A proportion of cases of multi-incident LOAD are due to heritable deterministic factors, as yet unidentified.  Hypothesis 2: Genetic variants traditionally identified as causing familial cases of EOAD also play a deterministic role in a proportion of cases of multi-incident LOAD.  1.3 Objectives To test these hypotheses, the specific aims of this study were to operationalize and streamline the discovery of new genetic variants potentially causative for LOAD in patients attending the UBCH-CARD by: 1. creating a comprehensive family history database for case ascertainment; 2. selecting families that meet criteria for multi-incident LOAD; and 3. performing next generation sequencing studies in the most genetically informative families.   24  Chapter 2: Materials and Methods  Family history ascertainment and genetic counselling have been part of routine patient visits to the UBCH-CARD since its inception in 198468.  The research made possible through this data source include discovery of pathogenic genes identified in familial FTD69, and discovery of new pathogenic mutations within PSEN170.  The UBCH-CARD also served as one of the sites for the Multi-Institutional Research in Alzheimer's Genetic Epidemiology (MIRAGE) research group71.    DNA banking has also been a longstanding practice at the UBCH-CARD.  Initially, due to limited resources and the knowledge constraints of the time, collection was limited to multi-incident or possible autosomal dominant families as well as atypical cases and early-onset cases.  Since 2008, every patient attending UBCH-CARD has been invited to contribute DNA for research purposes.  Affected biological family members of clinic attendees with documented diagnoses are also invited, with appropriate consent, to provide a DNA sample for research purposes.  2.1 The Family History Database The family histories collected through the UBCH-CARD contain clinical and demographic information on the proband and his/her biological relatives, both affected and unaffected by dementia, spanning a minimum of three generations. Information is also collected on family members outside the three generations who are reported to have dementia.  Before this study, family history data was stored in paper charts, with only basic information about the proband entered into a database.  Any research use of the family history data required extensive and time-25  consuming chart review.  For example, in the original database specific diagnoses were only tracked for the proband.  The presence of additional family members affected by dementia was indicated, but not what type of dementia.  There was no information on family members unaffected by dementia, requiring chart review for assessment of hereditary disease transmission patterns.  Furthermore, there was no digital record of DNA sample availability, again necessitating manual chart review for all genetic studies.  Therefore, the first part of this study involved design and implementation of a new comprehensive database.  The new database codifies all the information contained within the genetic chart.  Most of the decisions on what is contained within the genetic charts pre-date this research.  They were made based largely on maximizing clinical utility for attendees of the UBCH-CARD.  Minor changes were made to the data collection protocol of the genetic counsellor, which track DNA contributions to the UBCH-CARD blood bank, and specify exactly what patients and their family members consent to for the use of their biological samples.  Clinical designations, such as the diagnostic categories used at the UBCH-CARD (Table 2.1) also predate this research.  The new database is designed on a code table system (see example in Fig. 2.1) which allows any detail recorded to be queried across all individuals in the database.  Exceptions to this are text-based fields for the following: (i) proband name; (ii) proband self-described job title; (iii) municipality of birth; (iv) cause of death; (v) and neuropathology findings.  Text descriptions are also available to elaborate on a limited set of coded fields including: (i) relationship to proband; (ii) diagnosis; (iii) initial symptom; (iv) comorbidities; (v) ethnicity; (vi) consanguinity; (vii) genetic test results; (viii) and consent specifications for research use of DNA.  The new database 26  has been designed to have utility outside the scope of the current research and will also be useful to query various topics across all diagnostic categories used at the UBCH-CARD (Table 2.1) including: (i) sex ratio, current or changing over time (limited to 30 year history of data); (ii) incidence and prevalence trends by whole study population and/or by ethnic group; (iii) demographic contributors to dementia; (iv) incidence and prevalence change over time; (v) comorbid risk factors/protective elements; (vi) dementia progression; (vii) and ongoing genetic investigations.  The database consists of two main components, one specific to the UBCH-CARD probands, and one specific for family history information. The two sections have many fields in common, but some data is specific to the proband.   27  Table 2.1.  Diagnostic categories at the UBCH-CARD      28  Figure 2.1.  Benefit of code table vs. text-based entry in a record field.  29  2.1.1 Proband data form Information collected exclusively for the proband include: (i) date the initial family history was taken; (ii) level of education; (iii) occupation and job title; (iv) birth order position in the sibship; (v) consanguinity of parents or grandparents; and (vi) ethnicity (Figs. 2.2-2.7).  A unique ID is generated for each proband being entered.    30  Figure 2.2.  Page 1 of the proband data form  31  Figure 2.3.  Page 2 of the proband data form    32  Figure 2.4.  Page 3 of the proband data form    33  Figure 2.5.  Page 4 of the proband data form  34  Figure 2.6.  Page 5 of the proband data form    35  Figure 2.7.  Page 6 of the proband data form  36  2.1.2 Pedigree data form The pedigree form is used to enter every biological family member of the proband.  At times both adopted- and married-in family members are also included.  Each individual within the pedigree is given an ego number that is a unique numerical identifier (Figs. 2.8-2.13).  The proband ID combines with the ego number to indicate members of the same family. .37  Figure 2.8.  Page 1 of the pedigree data form    38  Figure 2.9.  Page 2 of the pedigree data form    39  Figure 2.10.  Page 3 of the pedigree data form  40  Figure 2.11.  Page 4 of the pedigree data form    41  Figure 2.12.  Page 5 of the pedigree data form    42  Figure 2.13.  Page 6 of the pedigree data form   43  2.1.3 Contents of the database For each individual within the pedigree we encode the following: (i) parent egos, (ii) year of most recent information update; (iii) name; (iv) sex; (v) date/year of birth; (vi) relationship to proband; (vii) twin status; (viii) adoption status; (ix) place of birth; (x) date/year of death, (xi) age of death, (xii) cause of death; (xiii) number and gender of children; (xiv) medical history with age of onset for each reported illness; (xv) dementia status with year and age of onset; (xvi) initial symptom; (xvii) DNA banking status; (xviii) DNA location; (xix) status for brain-limited autopsy requests, consent and completion; and (xx) DNA use consent specifications.  Completeness for this information is dependent on the knowledge and cooperation of the family historian and the thoroughness of the genetic counsellor that collects the information.  The database is specifically designed so that the answer to a query is very clear – e.g. “yes”, “no”, “inapplicable”, ‘not asked”.  This is critical for the usefulness of the database as each research question may need different categories.  Cognitive assessment scores are available for all probands (administered as standard first visit protocol to the UBCH-CARD) and scores are acquired whenever possible for off-site family members of clinic patients who reportedly have dementia.  Clinical diagnoses were made according to the DSM3-R dementia criteria and the NINCDS-ADRDA criteria1.  Neuropathology diagnostic criteria are scored via BRAAK stage23 and CERAD neuritic plaque score72.  When dementia is reported in a family member, additional documentation is sought to confirm the family historian’s report.  The following can be encoded as medically documented: (i) 44  request for information status; (ii) diagnosis; (iii) year and age of onset; (iii) assessment scores for the GDS, MMSE, 3MS, and MoCA; (iv) genetic test outcomes; and (v) neuropathology results.  These fields are distinct from reported-only fields.  This allows us to distinguish between family reports of dementia, and medically documented cases of dementia for individuals who are not patients at the UBCH-CARD.  Completion of these fields is limited to the content of requested medical documentation.  Medical documentation indicating clinical or autopsy confirmed diagnoses of AD are sought for all family members reported to be affected. While reported dementia alone is insufficient for inclusion as being affected for the purposes of our research, the accuracy of family history reports from the UBCH-CARD has proven to be high62. Therefore, we have reason to be confident in the diagnoses of family members even in the absence of objective documentation.  2.1.4 Family selection and demographics As of September 17, 2014, data for 4,870 families consisting of 58,194 individuals is stored in the family history database.  The functionality of the new database has allowed many queries, including the one critical for this study; namely, is the family suggestive of familial LOAD, inclusion criteria for which are: (i) 3 affected biological relatives; (ii) at least one affected family member with onset of dementia symptoms ≥65; and (iii) diagnoses of AD (clinical or neuropathological) or mixed dementia with an AD component.  45  To identify whether genetic mutations responsible for AD exist in these families, we sequenced a panel of genes known to be involved in neurological diseases, including AD, VaD, FTD, and LBD among others (vide infra).  2.2 Next generation sequencing Selection of next generation sequencing techniques was determined based on available DNA samples from the UBCH-CARD.  There is a large amount of clinical and familial data available through the UBCH-CARD; however, there is a relative dearth of corresponding DNA samples.  Based on this  we decided to limit our sequencing to the coding region of a specific set of candidate genes (vide infra).  This allowed us to test the hypothesis that EOAD genes may also play a role in LOAD.  Each of the additional candidate genes were selected based on their role in the development of other neurodegenerative disease, thus providing the opportunity to identify new phenotypic presentations of genes previously implicated in neurodegenerative disease.  2.2.1 Targeted panel genes The targeted gene panel contains genes previously implicated in dementia.  These include the three causative genes for AD, APP, PSEN1 and PSEN2, as well as the most significant susceptibility gene APOE.  Additional genes linked to AD through GWAS and by biological relevance due to pathophysiology include BIN, CLU, CALM, BACE1, and SERPINA3.  Mutations in NOTCH3 cause an autosomal dominant form of VaD.  Mutations in DNAJC13, SNCA, and SNCB can cause heritable forms of LBD, while GBA increases susceptibility.  Genes leading to dominantly inherited forms of FTD include CHMP2B, DCTN1, FUS, GRN, MAPT, VCP, and loci C9orf72.  Additional loci implicated in very rare forms of dementia include PRNP 46  (prion diseases), DNAJC5 (neuronal ceroid lipofuscinosis-4B), FTL (neurodegeneration with brain iron accumulation type 3), PANK2 (neurodegeneration with brain iron accumulation type 1), and loci C19orf12 (neurodegeneration with brain iron accumulation type 4).  The targeted gene panel also contains genes implicated in ALS: ALS2; ANG; C9orf72; DCTN1; FIG4; FUS; OPTN; SETX; TARDBP; VAPB; VCP; and other motor neuron disease: BSCL2; C19orf12; DNAJC5; SEPT9; UCHL1; and VAPB.  Genes that cause and contribute to dystonia were also included on the custom panel.  These include loci significant to PD and other parkinsonian syndromes: ATP13A2; ATP7B; BST1; DCTN1; DNAJC13; FBXO7; FTL; GABP1; GAK; GBA; HK3; HLA-DRB5; LINGO1; LRRK2; MAOA; MAOB; MAPT; PARK2; PARK7; PINK1; PLA2G6; PM20D1; RAB25; RAB7L1; SNCA; SNCB; SNCG; SOD1; SPR; SYNJ1; TH; UCHL1; and VPS35.  Loci implicated in other dystonias include: ACTB; ATP1A3; GCDH; GCH1; MR1; PRKRA; SGCE; SLC2A1; SLC6A3; TAF1; THAP1; TIMM8A; and TOR1A.  Genes involved in other rare movement disorders include: PNKD; PRRT2; SETX; and SLC2A1.  Additionally, several genes involved in Charcot Marie Tooth neuropathy were included on the panel: ARHGEF10; DNM2; DYNC1H1; EGR2; FGD4; FIG4; GARS; GDAP1; GJB1; HSPB1; HSPB8; LITAF; LMNA; MFN2; MPZ; MTMR2; NDRG1; NEFL; PMP22; PRPS1; PRX; RAB7A; SBF2; SH3TC2; and YARS.  Spinal muscular atrophy and other peripheral neuropathies also have genetic contributors on the panel, including: IGHMBP2; SMN1; SMN2; BSCL2, CTDP1; GAN; 47  HSPB1; IKBKAP; MCCC1; MPZ; NGF; NTRK1; PRPS1; SH3TC2; SLC12A6; SOX10; SPG11; SPTLC1; and WNK1.  Susceptibility genes for schizophrenia and psychiatric disorders in general included on the panel are: COMT; DISC1; DTNBP1; FXYD6; NR2E1; and PRODH.  Finally, genes with physiological and biological relevance to neuronal health were also included on the panel.  They include: DNM1; DNM3; DRD1; DRD3; DRD4; DRD5; EHD1; EHD2; EHD3; EIF4G1; FCHO1; FCHO2; GAB2; GBAP1; GPNMB; HGS; HSPA8; NXN; PABPC1; PABPC1L; PXMP2; RAC1; RAC3; RPE65; SETD1A; SGTA; SH3BP4; SH3GL1; SH3GL2; SH3GL3; SLC18A2; SNAP25; SNX1; SNX2; SNX32; SNX5; SNX6; STARD13; STK39; STX10; STX16; STX6; SYNJ2; VAMP1; VAMP2; VAMP3; VAMP4; VAMP7; VAMP8; VPS26A; VPS26B; VPS29; VPS54; VTI1A; WASH; and WASH1 ( Table 2.2).  48  Table 2.2.  Custom gene panel for neurodegenerative disease  49  Genomic DNA from all samples was sheared to 150 base pairs (bp) using the Covaris E220 sonicator and enriched for exonic regions of interest. A TargetSeqTM Custom Enrichment Kit covering the exonic regions of 177 genes linked and/or associated with neurodegenerative conditions was used to capture regions of interest according to manufacturer instructions (Life Technologies, Carlsbad, CA, USA). Quantification and quality control was performed using the Agilent 2100 Bioanalyzer (Agilent Technologies, Palo Alto, CA) and a Qubit Fluorometer (Life Technologies). Captured DNA was amplified by bead emulsion PCR with the EZ Bead system and sequenced using 75x35 bp paired-end reads on a SOLiD 5500xl platform (Life Technologies). Sequence alignment to the human reference genome (NCBI Build 37), and variant calling were performed by LifeScope version 2.5.1 (Life Technologies); followed by variant annotation with ANNOVAR73.  Sanger sequencing was used to confirm all novel variants and mutations of interest (Table 3.3). 50  2.2.2 Additional screening for mutations of interest If the panel returned a mutation for a gene previously linked to neurodegeneration, but newly associated with AD, we then performed additional screening of the UBCH-CARD population DNA bank for the same mutation.  Taqman probes were used to genotype DNAJC13 p.N855S on an ABI 7900 sequence detection system and analyzed with SDS 2.4 software.  All positive calls were confirmed by Sanger sequencing.  2.2.3 Haplotype analysis Microsatellite markers spanning the DNAJC13 locus (vide infra for justification) were chosen based on previous findings74 to define the disease haplotype.  PCR reactions were run under standard conditions on the ABI3730xl DNA analyzer with one primer in each pair bearing a fluorescent tag.  The results were analyzed using GeneMapper 4.0 software  51  Chapter 3: Results  Thirteen families were selected for inclusion in this study (Table 3.1).  Together, these families consist of 49 family members with a clinical or neuropathological diagnosis of AD, 44 reportedly affected but only by family informants, and 48 informative unaffected family members.  Unaffected family members are deemed informative if their age/age of death is >60 or if they are older than the low end of the familial onset-age range.  Among individuals with a known age of onset, the average age at onset of disease was 69.10 (SD 7.45) years for documented cases and 71.14 (SD 8.56) years for all reported cases taken together.  Of note, age of onset for all-cases may be skewed toward higher ages as there is a statistically significant difference between age of onset for documented versus undocumented (75.00 (SD 9.28)) cases (p=0.003).  The female to male ratio for affected individuals was 1.00:0.92.  There is no statistically significant difference in sex ratio between documented and undocumented cases (p=0.284) (Fig. 3.1). 52  Table 3.1.  Summary of candidate families.  Informative age is family specific based on familial age of onset.  q indicates differential diagnoses.  + indicates mixed dementia.Family Number affected Number documented Age of onset  (mean (SD)) Age of onset (family range)  Number unaffected (informative age) Ethnicity Diagnoses 1 10 5 62.50 (8.63) 52-79 6 English, Scottish AD 2 9 5 78.00 (7.84) 68-85 4 English AD 3 6 5 73.00 (4.47) 66-76 5 English, French AD, qAD/LBD 4 7 4 71.50 (6.95) 63-83 0 Scottish AD, AD+LBD 5 6 3 62.75 (5.19) 55-65 6 Acadian AD 6 6 3 76.17 (11.20) 68-87 4 English, Scottish AD, qAD/VaD/FTD 7 8 6 67.75 (6.40) 60-73 4 German, Russian AD,PDD 8 4 3 69.25 (2.63) 67-71 5 Swedish AD 9 8 4 74.20 (13.18) 55-85 1 Polish AD, AD+VaD 10 9 4 74.57 (3.51) 69-80 3 German AD  11 6 1 70.29 (8.88) 65-88 1 English, Hungarian AD 12 10 4 72.38 (5.83) 65-85 3 German (Mennonite) AD+LBD, AD+FTD, AD+VaD 13 4 2 72.00 (3.61) 68-85 6 English, French Canadian AD   53  Figure 3.1 A-C.  Pedigrees for patients meeting criteria for familial LOAD.  Individual pedigrees are labeled by random assignment of family number for this study only.  Black-filled symbols indicate individuals with documented diagnoses, and gray-filled symbols indicate reported diagnoses.  The number in brackets below the symbol indicates age-of-onset when available, and the number to the upper right corner of the symbol indicates age or age of death.  A + sign indicates that a DNA sample is available. A.  B.         C.      Family 3 English/French 63 81 68 89  88 85 72 88 91 78 75 II-7 (71) II-6 II-5 (76)  II-2 (76) II-1 II-3 II-4 (76)   III-1 (66)  + III-2 I-2 I-1   90 101 86 90 87 76 80 91 83 Family 2 English ? 85 70 92 70 III-1 (85) III-2 (68) III-3 (73)  + III-4 III-5 (72) + III-6 III-7 (80) III-8 (74) III-9 (80) I-1 I-2 II-1 II-2 II-3               58 80 85 80 72 73 86 80 75 54 71 72 61 69 Family 1 Scottish/English IV-1 (55)  + III-1 (52) III-2 (58)  + III-3 (79) III-4 III-5 (58) III-6 (70) III-7 (69) II-5 (69) II-2 (58) II-1 II-3 II-4 II-6 I-2 I-1     70   ? 54  90 88 69 71 63 ? 97 84 84 76 84 Family 6 English/Scottish IV-1 (58)  + III-1 (85)  III-2 (77) III-3 (68) III-4 II-1 (82) II-2 II-3 I-2 (87) I-1 II-4   Figure 3.1 D-G.  Pedigrees for patients meeting criteria for familial LOAD.  Individual pedigrees are labeled by random assignment of family number for this study only.  Black-filled symbols indicate individuals with documented diagnoses, and gray-filled symbols indicate reported diagnoses.  The number in brackets below the symbol indicates age-of-onset when available, and the number to the upper right corner of the symbol indicates age or age of death.  A + sign indicates that a DNA sample is available. D. E.      F.    G.    53 83 Family 7 Russian/German  88 79 77 95 73 83 91 97 83 65 92 II-2 (65) II-1 II-3 II-4 II-5 (73)  + II-6 II-7 (73) II-8 II-9 I-1 I-2            II-10  II-11 (60)  +  81 75 71 69 65 Family 5 Acadian   ? ? ? 80s 89 80 80 89 68 ? IV-1 (66)  + III-1  III-2 III-3 III-4 (65) III-5 (65) III-6  II-1 II-2 II-3 I-2 I-1 IV-2 IV-3 (55)  + IV-4 IV-5     Family 4 Scottish 75 89 79 78 85 72 80 II-5 (72)  + II-2 II-1 (63) II-3 (75) II-4 (68) I-2 (83) I-1 (68) 55  ? Family 10 German ? 75 45 92 ? 85 79 85 77 78 83 81 75 79 II-2 (80)  II-3 II-4 (77) II-5 I-1 I-2      II-1   III-2 (74)  III-1 (69) III-3 (75) III-4 III-5 (72) III-6 (75) + III-7 III-8         Figure 3.1 H-K.  Pedigrees for patients meeting criteria for familial LOAD.  Individual pedigrees are labeled by random assignment of family number for this study only.  Black-filled symbols indicate individuals with documented diagnoses, and gray-filled symbols indicate reported diagnoses.  The number in brackets below the symbol indicates age-of-onset when available, and the number to the upper right corner of the symbol indicates age or age of death.  A + sign indicates that a DNA sample is available. H. I.        J. K.      Family 9 Polish 75 83 50 75 93 ? 86 91 90 63 II-1  II-2 (81)  + II-3 (85) II-4 (84) II-5 (66) II-6 II-7 III-1 (55)  + I-1 I-2           Family 11 English Hungarian 76 89 87 83 80 82 79 II-2 (65)  II-3 (65) II-4 (75) II-5 II-1 (71)  + I-1 (65) I-2 (88)        Family 8 Swedish 67 32 85 83 78 67 71 65 70 72 III-1 (71)   III-2 III-3 (67) III-4 (67)  +  III-5 III-6 I-1 I-2 II-1 II-2            56  Figure 3.1 L-M.  Pedigrees for patients meeting criteria for familial LOAD.  Individual pedigrees are labeled by random assignment of family number for this study only.  Black-filled symbols indicate individuals with documented diagnoses, and gray-filled symbols indicate reported diagnoses.  The number in brackets below the symbol indicates age-of-onset when available, and the number to the upper right corner of the symbol indicates age or age of death.  A + sign indicates that a DNA sample is available.   L.         M.     English     French Canadian Family 13 68 94 64 78 77 64 66 67 72 74 75 II-2 (68)  +  II-1  II-3 (73)  + II-4 + II-5 +  II-6 + II-7 + II-8  II-9  I-1 I-2 (75)       II-2 II-1 II-3 (85) II-4 (80) II-5 (76) + II-6 II-7 (68) + II-8 II-9 I-1 I-2 (72)        Family 12 German     II-10 (65)  II-11     2  2         85 Family 12 German  81 82 85 86 88 67 87 70 75 79 84 82 II-2 II-1 II-3 (85) II-4 (80) II-5 (79)  + II-6 II-7 (68)  + II-8 II-9 I-1 I-2 (72)           II-10 (65)  II-11     57  3.1 Sequencing Results DNA was available for 23 individuals from 13 families.  In 6 families DNA was only available for a single family member.  Six additional families had 2 samples available for analysis, and 1 family had 6 samples available, although with four members of the family within or younger than the onset range for that family.  Sequencing of candidate genes in 23 affected family members from 13 families identified 97 missense variants, 1 nonsense variant and 26 variants in untranslated regions (UTRs) with a minor allele frequency below 1% from publicly available databases (1000 Genomes Project and the National Heart, Lung, and Blood Institute (NHLBI) Exome Sequencing Project (ESP)) (Table 3.2).  As autosomal dominant forms of AD are very rare, the allele frequency cut off of 1% was deemed appropriate for identifying the most likely pathogenic candidates from among our results.  We limited validation of our 124 mutations to the coding region, excluding the 26 UTR findings.  Eight variants were excluded because they are likely panel artifacts.  Genes present on the panel due to biological relevance and because of previous findings in GWAS were not validated because they are less likely to be involved in pathogenicity.  After validation, 31 variants remained for analysis in our 13 families of interest (Table 3.3).58   59   60   61   62   63   64  3.1.1 Family 1 For family 1 we had two DNA samples from family members diagnosed with AD.  After validation there were 3 missense mutations in this family.  One family member is a carrier for ARHGEF10 c.877 A>G (p.Y293C) and both family members are carriers for PSEN1 c.1311 A>G (p.I437V) and MFN2 c.894 G>A (p.G298R). Due to previous association with AD, the PSEN1 mutation is the most likely candidate as the pathogenic mutation in this family.  3.1.1.1 PSEN1 c.1311 A>G (p.I437V) Literature search reveals only one previous recorded (though un-indexed in SNP databases) report of PSEN1 p.I437V in a multi-incident AD family75. This amino acid change occurs in a highly conserved protein domain (Fig 3.2) with a Genomic Evolutionary Rate Profiling (GERP) score of 3.7576.  A software systems designed to predict the effects of amino acid changes to protein function, Sorting Intolerant From Tolerant (SIFT) predict this polymorphism to be damaging to protein function, but another, PolyPhen2, does not77 (See Table 3.4 for protein conservation and damage prediction scores).  3.1.1.2 Family details Family 1 shows several unusual features for familial AD (FAD) (Fig. 3.1A).  In FAD, affected family members have very similar ages of onset; but here documented ages of onset range from age 55 to age 70 (52-79 among reported cases).  Additionally, of the three obligate carriers in this family, one (F1-II-4) died at 72 with no clinical signs of dementia (no autopsy was done).  This family may represent a rare situation of reduced penetrance with PSEN1 but it is important 65  that the age of death fell within the onset age range among all reported cases in this family (52-79 years).  3.1.1.3 Clinical and pathological phenotype F1-III-2 is a female who first reported experiencing short term memory loss at approximately age 58.  When assessed at age 63 she had insight into her short term memory loss and scored 17/30 and 58/100 on the MMSE and 3MS respectively.  She was diagnosed with early stage dementia, possibly AD in etiology.  At last clinical assessment at age 66 she was experiencing a gradually declining course, including decreases in memory, ability to converse and geographic orientation, with no insight into her impairment.  The patient died at age 71 and neuropathology confirmed a diagnosis of AD with frequent neuritic plaques (CERAD definite) including the presence of some cotton wool pathology and neurofibrillary tangles (Braak score VI out of VI), and chronic cerebellar degeneration with a transitional meningioma in the left parietal lobe (Fig. 3.3).  F1-IV-1 is a 59 year old female with a 4 year history of memory decline, but no associated aphasia, agnosia, or apraxia.  She was diagnosed with MCI at age 56, but converted to dementia of the Alzheimer type one year later at age 57.  At last assessment at age 59, she was functionally, emotional and cognitively stable with respect to her status as age 57.  Obligate carrier F1-III-5 was female and was diagnosed with cognitive impairment at age 58, converted to dementia of the Alzheimer type the same year, and died at age 72.  Neuropathology confirmed a diagnosis of AD with frequent senile plaques, including cotton wool pathology in 66  the neocortex, mild congophilic angiopathy, severe cerebral atrophy and neuronal loss in both the cortex (moderate) and the hippocampus (severe) (Fig. 3.3).  DNA was not available for individual F1-III-6, but results were obtained for neuropathology assessment.  While F1-III-6 meets criteria for autopsy confirmed AD, her phenotype was markedly different from her family members who are known carriers for p.I437V.  Distribution of both APs and NFTs were reduced in comparison, and there were no cotton wool plaques (Fig. 3.3).  While not conclusive, these neuropathology results may indicate that F1-III-6 represents phenocopy in the context of familial disease, or a different spectrum of the same disease.  3.1.1.4 Summary In family 1, we identified a PSEN1 (p.I437V) mutation in four affected members (F1-II-2, F1-III-2, F1-III-5, F1-IV-1) and one unaffected obligate carrier (F1-II-4).  The age at onset for p.I437V mutation carriers ranged from 55 to 61 years.  The unaffected carrier presented no signs of dementia at time of death at age 72.  There was one reported case of dementia in this family with onset after age 72, but this diagnosis was not confirmed with medical documentation.  It is possible that F1-II-4 may have had pre-clinical AD at time of death.  It is also possible that p.I437V has markedly reduced penetrance in comparison to previously described mutation in PSEN1.  Consistent with previous findings for PSEN1 mutations78, cotton wool pathology was seen in F1-III-2 and F1-III-5, but not in F1-III-6.   67  Figure 3.2.  Amino acid conservation across species for PSEN1  p.I437V Homo sapiens (NP_000012.1) GLCLTLLLLAIFKKALPALPISITFGLVFYFATDYLVQPFM Mus musculus (NP_032969.1) GLCLTLLLLAIFKKALPALPISITFGLVFYFATDYLVQPFM Gallus gallus (NP_989494.1) GLCLTLLLLAIFKKALPALPISITFGLVFYFATDNLVQPFM Danio rerio (NP_571099.1) GLCLTLLLLAIFKKALPALPISITFGLVFYFATDNLVRPFM Xenopus laevis (NP_001084023.1) GLCLTLLLLAIFKKALPALPISITFGLVFYFATDYLVQPFM Drosophila melanogaster (NP_001262111.1) GLCLTLLLLAIWRKALPALPISITFGLIFCFATSAVVKPFM Caenorhabditis elegans (NP_508175.1) GLCFTLVLLAVFKRALPALPISIFSGLIFYFCTRWIITPFV   Table 3.2.  Protein conservation and damage scores for mutations of interest in families, 1, 2, 12 and 13 Family Variant GERP score PolyPhen2 score SIFT score  1 PSEN1  p. I437V 3.75 0.002 Benign 0.035 Damaging  2 PSEN2 p. S130L 5.14 0.948 Possibly damaging 0.012 Damaging 12 DNAJC13 p.N855S 5.41 0.993 Probably damaging 0.111 Tolerated APOE p.L46P -5.71 0.949 Possibly damaging 0.109 Tolerated 13 LMNA p.N459S 3.41 0.008 Benign 0.032 Damaging DCTN p.T147A 3.92 0.002 Benign 0.423 Tolerated  68  Figure 3.3.  Neuropathology images for family 1. Inserts on H&E stain slides highlight cotton wool plaques.  F1-III-2 F1-III-5 F1-III-6 Bielschowsky silver stain        H&E stain        Tau antibody stain    .69  3.1.2 Family 2 For family 2 we initially had 1 DNA sample for an individual diagnosed with AD.  After validation there were 4 mutations in this individual: PSEN2 c.388 C>T (p.S130L); DNAJC13 c.3871 A>G (p.E1291G); MAPT c.319 G>T (p.G107V); and MAPT c.1407 G>A (p.A469T).  Due to previous association with AD, the PSEN2 mutation is the most likely candidate as the pathogenic mutation in this family.  Of note, a second DNA sample for an individual diagnosed with AD in this family became available after validation of the panel findings.  Genotyping for PSEN2 (p.S130L) in this individual was negative.  3.1.2.1 PSEN2 c.388 C>T (p.S130L) Results for family 2 implicate a previously reported missense mutation in exon 6 of PSEN2, rs6375019779–81.  PSEN2 p.S130L is located in a highly conserved protein domain (Fig. 3.4) (GERP = 5.14), and is likely damaging to protein function, according to both SIFT and PolyPhen2 (Table 3.4).  Previous reports for rs63750197 indicate that it may exhibit reduced penetrance, and the phenotype leans toward later age of onset and relatively slow disease progression79–81.   3.1.2.2 Family details Family 2 is noteworthy for late age of onset with a mean of 78.00 years (SD 7.84) and a range 68-85 years, and slow disease progression (mean = 11.83 (SD11.10)) (Fig. 3.1B).  Unlike the evidence from previous reports for reduced penetrance, we find almost an entire sibship (6/9) affected.  Empirically we would expect to see 50% of the sibship affected in the case of autosomal dominant inheritance, although probabilistically it is still possible that six members of 70  this sibship inherited the pathogenic mutation.  It is more likely however, that some of the members of family 2 may have a distinct cause of AD in the context of familial disease.  This is epitomized by F2-III-5, who is not a carrier for the p.S130L mutation despite having a very similar phenotype (onset at age 72 with autopsy confirmed AD and comparable neuropathology to carrier after 11 years of illness) to the known carrier in this family (Fig. 3.5).  3.1.2.3 Clinical and pathological phenotype F2-III-3 was a male with disease onset at age 73 who experienced a 13 year course of cognitive decline and died at age 86.  No records are available outlining his initial symptoms, but by age 82 he was experiencing a profound cognitive deficit with particular problems in memory and task planning.  The most recent UBCH-CARD clinical update at age 83 noted further decline in memory, as well as a tendency toward aggressive behavior when frustrated.  An MMSE was attempted but abandoned at that time, because he was completely disoriented to place, time, and somewhat to person.  Consent was obtained for a brain limited autopsy.  Neuropathology confirmed AD with cerebral atrophy, neuritic plaques and neurofibrillary tangles, congophilic angiopathy, and micro-angiopathic changes in white matter, basal ganglia, pons and cerebellar dentate (Fig. 3.5).  3.1.2.4 Summary Phenotypically, PSEN2 mutations have been observed in patients with later age of onset35,79,82,83, long disease progression and reduced penetrance; which is consistent with an age at onset of 73 years and a disease course of 13 years observed in F2-II-3.  Previous reports of reduced penetrance for p.S130L are less certain.  After accounting for the phenocopy (F2-II-5), 6 of 9 71  members of this sibship are still affected, but we are unable to identify which family members are carriers for S130L, and which may also be exhibiting phenocopy in the context of FAD.  Figure 3.4.  Amino acid conservation across species for PSEN2       p.S130L Homo sapiens (NP_000438.2)  RFYTEKNGQLIYTPFTEDTPSVGQRLLNSVLNTLIMISVIV Mus musculus (NP_035313.2)  RFYTEKNGQLIYTPFTEDTPSVGQRLLNSVLNTLIMISVIV Gallus gallus (NP_989633.1)   RFYTEKNGQLIYTPFSEDTPSVGQRLLNSVLNTIIMISVIV Danio rerio (NP_571589.2)  FYTEKSGQRLIYTPFEEDPNSVGQRLLNSVLNTLVMISVIV Xenopus laevis (NP_001081211.1) SFYTEKDGQLIYTPFSEDTTSVGERLLNSVLNTLIMISVIL Caenorhabditis elegans (NP_508175.1) FYSQNNGRHLLYTPFVRETDSIVEKGLMSLGNALVMLCVVV  72  Figure 3.5.  Neuropathology images for Family 2  F2-III-3 F2-III-5 Bielschowsky silver stain  x20 resolution   Bielschowsky silver stain  x40 resolution   Tau antibody stain x20 resolution   Tau antibody stain x40 resolution   73  3.1.3 Family 12 In family 12, we identified a DNAJC13 c.2563 A>G (p.N855S) mutation in one individual diagnosed with mixed dementia with AD and VaD etiologies.  We also identified APOE c.136 T>C (p.L46P) (aka p.L28P) in the same individual as well as a second family member diagnosed with AD and comorbid history of strokes.  3.1.3.1 DNAJC13c.2563 A>G (p.N855S) Results for family 12 returned a DNAJC13 c.2563 A>G (p.N855S) mutation, previously reported to cause PD and ET74,84.  The codon change occurs in a highly conserved protein motif (GERP = 5.41) (Fig. 3.6) and is predicted to damage protein function by PolyPhen2 but not by SIFT (Table 3.4).  All previous reports for this mutation are identical by decent, and haplotype analysis in family 12 confirmed the mutation is located on the same disease haplotype, meaning it too came from the same common ancestor as the other reported carriers.  Reports of this mutation have been consistently pathogenic74,85.  Figure 3.6.  Amino acid conservation across species for DNAJC13  p.N855S Homo sapiens (NP_056083.3) LLEEDENEESGSIKRSYEFFNELYHRFLLTPKVNMKCLCLQ Mus musculus (NP_001156498.1) LLEEDENEESGSIKRSYEFFNELYHRFLLTPKVNMKCLCLQ Gallus gallus (XP_004939500.1) LLEEDETEESGAIKKSYEFFNELYHRFLLTPKVNMKCLCLQ Xenopus tropicalis (XP_002937780.1) LLEEDENEENAAIKQSYEFFNELYHRFLLTPKVNMKCLCLQ Drosophila melanogaster (NP_610467.1) LILEKDDWPQNLVKDPIELFNALYRRVLCRQRVNDDQMTVF Caenorhabditis elegans (NP_492222.2) LLLIEADENATPIHNPLEFFNNVYHRFLLSTKVDMKCLCLR   74  3.1.3.2 APOE c.136 T>C (p.L46P) In Family 12, we also identified the previously reported “Pittsburgh mutation” (rs769452)86 in APOE p.L46P (aka p.L28P, NM_000041.2).  APOE p.L46P was initially considered to be a risk factor for AD86,87, but further analysis showed it to be in complete linkage disequilibrium88 with APOEε4.  When the presence of the ε4 allele is accounted for, p.L46P makes no additional contribution to the risk of developing AD88,89.  3.1.3.3 Family details In addition to the haplotype analysis, demographic details for this family strongly suggest a common ancestor to the PD and ET kindreds where p.N855S was first reported74.  The affected individuals in family 12 are characterized by phenotypic heterogeneity (Fig. 3.1L).  Among the confirmed diagnoses is one case of mixed dementia AD+VaD, two cases of AD with history of stroke and one case of mixed dementia AD+FTD.  Among the reportedly affected are three additional cases of mixed dementia AD+VaD, and one mixed dementia AD+LBD.  Onset in this family has a mean of 72.38 (SD 5.83) and ranges 65-85 years.  Disease course ranges from 1 to 14 years with a mean of 7.67 (SD 4.41) years.    3.1.3.4 Clinical and pathological phenotype F12-II-5 is a carrier for both DNAJC13 (p.N855S) and APOE (p.L46P).  F12-II-7 is a carrier for APOE (p.L46P).    75  3.1.3.4.1 Carrier for DNAJC13c.2563 A>G (p.N855S) F12-II-5 is a male with a complicated course of dementia, including both AD and VaD etiologies.  The major onset symptom was a large bleed in the left deep white matter and two smaller hemorrhages bleeding into the lateral ventricle at age 79.  There are indications that cognitive changes began 6 months prior to this event.  Assessment scores are unavailable due to the type of impairment involved.  Since age 84, F12-II-5 has been unable to read or write and also suffers from confusion, anomia, aphasia, and dysarthria.  At time of last assessment at age 87, F12-II-5 had declined even further, and was likely approaching end of life.  Clinical history shows no indications of a movement disorder.  3.1.3.4.2 Carriers for APOE c.136 T>C (p.L46P) F12-II-7 is a female diagnosed with AD.  Her symptoms began in her late 60s.  By age 77 her symptoms had advanced to moderate AD, with memory loss and wandering being the primary concerns.  She experienced a marked decline in MMSE score over a three year period (21 at age 78; 7 at age 80).  F12-II-5 (see clinical description above) is also a carrier for APOE (p.L46P).  3.1.3.5 Screening for additional AD carriers of DNAJC13 p.N855S As this is the first report linking AD to DNAJC13 p.N855S we screened an additional 253 Canadian AD patients of European descent from the UBCH-CARD. Average patient age was 70.48 (SD 10.10) years at time of last assessment or death, with an average age of onset of 65.69 (SD 9.87) years and a female to male ratio of 1:0.97.  All patients were examined and observed 76  longitudinally by UBCH-CARD physicians and diagnosed according to DSM3-R dementia criteria and NINCDS-ADRDA criteria1.  Genotyping of DNAJC13 p.N855S in 253 additional AD patients did not identify any mutation carriers.  3.1.3.6 Summary In family 12, we find two variants that are likely contributing to disease.  While the presence of APOE p.L46P does not itself confer risk, it does signify the presence of APOEε4.  The diagnosis of AD in F12-II-7 can reasonably be described as phenocopy due to the presence of at least one copy of APOEε4.  All previous reports of DNAJC13 p.N855S have been associated with neurodegenerative illness, PD and ET specifically74,84.  F12-II-5 has a diagnosis of AD complicated by a major stroke resulting in language disturbance which makes clinical assessment difficult. Clinical diagnoses typically concur with neuropathology findings11; however, in the absence of neuropathology we cannot say definitively whether this represents a new AD phenotype for DNAJC13 p.N855S, or whether it is more similar to previously reported parkinsonian phenotypes.  F12-II-5 is also an inferred carrier for APOEε4, further complicating the interpretation of his symptoms.  It is possible that the diagnosis of AD in F12-II-5 is due primarily to the presence of APOEε4, and that other symptoms are being obscured by a combination of AD and VaD symptomatology.  It is also possible that DNAJC13 p.N855S causes AD pathology, in addition to parkinsonian.  As DNAJC13 is involved in CME,90,91 which is the pathway by which APP is co-localized with BACE126,28,29, there is biological justification for DNAJC13 p.N855S causing amyloid 77  pathology.  Additional cases of AD in carriers of DNAJC13 p.N855S are needed to confirm this hypothesis.  3.1.4 Family 13 For family 13 we initially had 2 DNA samples from family members diagnosed with AD.  After validation of the panel findings, there were 3 mutations in this family.  One family member is a carrier for PRODH c.577 C>T (p.G193D) and both family members are carriers for DCTN1 c.441T>C (p.T147A) and LMNA c.1375A>C (p.N459S).   Four additional DNA samples for individuals unaffected by dementia became available after validation of the panel findings.  Genotyping for DCTN1 (p.T147A) in these individuals revealed two more carriers.  Previously reported mutations in LMNA cause recessive disease, which may indicate that DCTN1 is the more likely pathogenic agent in this case.  However, the protein change caused by LMNA p.N459S is predicted to be more damaging than that of DCTN1 p.T147A, which may mean that LMNA is the more likely candidate.  3.1.4.1 LMNA c.1375 A>G (p.N459S) Mutations in LMNA are known to cause a wide variety of serious conditions; including recessive Charcot-Marie-Tooth type 2B1 (CMT2)92,93, which accounts for its inclusion on the neurological disease panel.  LMNA p.N459S is in a protein domain conserved among mammals (GERP = 3.41) (Fig. 3.7).  It is predicted to be damaging by SIFT but not by PolyPhen2 (Table 3.4).   78  Figure 3.7.  Amino acid conservation across mammalian species for LMNA       p.N459S Homo sapiens (NP_733821.1) RVAVEEVDEEGKFVRLRNKSNEDQSMGNWQIKRQNGDTYRF Mus musculus (NP_001002011.2)  RVAVEEVDEEGKFVRLRNKSNEDQSMGNWQIRRQNGDDPLM Rattus norvegicus (NP_001002016.2)  RVAVEEVDEEGKFVRLRNKSNEDQSMGNWQIRRQNGDDPLM Canis lupus familiaris (NP_001274080.1) RVAVEEVDEEGKFVRLRNKSSEDQSMGNWQIKRQNGDDPLL   79  3.1.4.2 DCTN1 c.441 T>C (p.T147A) Mutations in DCTN1 have previous been described in ALS94, Perry syndrome (PS)95, PD96–98, FTD96, and hereditary distal motor neuropathy type VIIB99. DCTN1 p.T147A has a GERP score of 3.92, indicating the protein domain is conserved (Fig. 3.8), and is located on the edge of conserved protein domain COG1615.  However, it is not predicted to damage protein function by either SIFT or PolyPhen2 (Table 3.4).  Figure 3.8.  Amino acid conservation across vertebrates for DCTN1  p.T147A Homo sapiens (NP_004073.2) KTSKLRGLKPKKAPTARKTTTRRPKPTRPASTGVAGASSSL Mus musculus (NP_031861.2) KTSKLRGLKPKKAPTARKTTTRRPKPTRPASTGVAGPSSSL Rattus norvegicus (NP_077044.1) KTSKLRGLKPKKAPTARKTTTRRPKPTRPASTGVAGPSSSL Gallus gallus (NP_001026538.1) KGSKLRGAKPKKTTARRPKPTRTP--TSAPSSGTAGPSGS- Drosophila melanogaster (NP_524061.1) SRQSLLGSRTQLTTSLSERTASSSSIGPRKSLAPQN-SKDK  3.1.4.3 Family details In family 13, there are 3 affected brothers, 2 of whom have DNA samples available and a diagnosis of AD.  The mother is also affected (Fig. 3.1M).  The average age of onset in this family is 72.00 years (SD 4.08) and ranges from 68-85 years.  Disease duration ranges from 5 to 19 years with a mean of 11.33 (SD 8.03) years.  No members of family 13 show any signs or symptoms of ALS, PS, PD, FTD, hereditary distal motor neuropathy or CMT.  3.1.4.4 Clinical and pathological phenotype Both F13-II-2 and F13-II-3 are heterozygous carriers for both DNCT1 p.T147A and LMNA p.N459S.  80  F13-II-2 experienced symptom onset at age 68, with short term memory loss, but no associated personality change or depression. Over nine years of clinical follow-up MMSE scores dropped from 30 to 7.  At last assessment at age 76 his condition had advanced to moderate/severe AD and he was experiencing dysphasic speech.  He died at age 78.  No neuropathology was performed.  F13-II-3 is a male with symptom onset at age 73 that included memory decline and word finding difficulty.  He was diagnosed as having AD. Over the past year, he has showed decline on both the MMSE (25 to 20) and the 3MS (85 to 64).  At last assessment at age 76, he was markedly more confused in his speech and more forgetful than at the previous assessment 6 months earlier.   F13-II-4 and F13-II-7 are male carriers of DCTN1 p.T147A, aged 75 and 67 respectively.  Although F13-II-4 is now within the age of onset range previously observed in this family (68-75), as of 2014, neither have any clinical signs or symptoms of cognitive impairment.  3.1.4.5 Summary Both DCTN1 and LMNA are on our custom panel because of the role they play in neurodegenerative disease; however, the disease causing nature of the variants described herein remains to be validated.    LMNA encodes lamin A and lamin C.  The lamins provide structural support to the nucleus.  Lamins exist throughout the animal kingdom, but LMNA is a relatively recent evolutionary divergence specific to vertebrates, and the alternative splicing that forms lamin C is exclusive to 81  mammals100.  Mutations in the LMNA conserved protein domain HemX101,102 cause an autosomal recessive form of CMT (type 2B1)93; whereas, p.N459S is situated almost at the C-terminus, well outside the HemX domain101.  DCTN1 encodes dynactin subunit 1 which forms the large subunit in the dynactin intracellular transport molecule95.  All confirmed pathogenic mutations in DCTN1 are located in the CAP-Gly motif of the p150Glued subunit of dynactin; whereas, p.T147A is located on the edge of conserved protein domain COG1615.  The COG1615 domain is uncharacterized, but other members of the COG (conserved oligomeric Golgi) motif family are responsible for retrograde vesicular transport within the Golgi apparatus103.  This notwithstanding, the relationship between dynactin and APP presents a plausible mechanism for amyloid pathology.  APP binds to the scaffolding protein JIP1, and then intracellular transport molecules DCTN1 and kinesin heavy chain (KHC) compete for the same binding site on JIP1.  DCTN1 is responsible for the retrograde transport of APP which puts it in proximity to BACE1104.  If p.T147A changes protein function in such a way as to up-regulate the retrograde transport pathway, it would increase the production of Aβ42.   3.1.5 Additional mutations of unknown significance Each of the 23 DNA samples from our 13 families of interest returned multiple hits on the custom gene panel; but the majority of these are of unknown significance to date.  3.1.5.1 Family 4 In Family 4 we identified a heterozygote carrier for a recessive mutation for Parkinson disease (PARK2 c.966G>A (p.R322W))105 and Wilson disease (ATP7B c.2307 T>C (p.M769V))106.  Of 82  note, another member of this family for whom DNA was not available, was diagnosed with definite AD, showing cotton wool amyloid plaque pathology and Lewy bodies (McKeith criteria 10/10)107 on neuropathology (Fig 3.9).  Cotton wool plaques are often associated with PSEN1 mutations78 but have also been identified in sporadic cases108.  The age of onset in family 4 ranges from 63 to 83 years, with a mean of 71.50 (SD 5.56).  Disease course ranges from 4 to 16 years with a mean of 8.50 years (SD 4.18). There is strong evidence for a genetic etiology for AD in this family (Fig. 3.1D) with 5 affected siblings, and both parents affected with dementia of unspecified type, but due to limited access to DNA, we are unable to be sure which of several scenarios may be at play here.  These include: (i) our results come from a phenocopy case of AD in the context of a family history; (ii) we have in fact identified the disease causing mutation in this family (either PARK2 or ATP7B) and discovered a new heterozygous phenotype for AD pathology; or (iii) there is a common genetic cause of disease in this family but it was absent from our panel.    83  Figure 3.9.  Neuropathology images for F4-II-1.  Inserts on H&E stain slide highlight cotton wool plaques.  α-synuclein stain  Bielschowsky silver stain   H&E stain  Tau antibody stain    84  3.1.5.2 Families 3 and 5-11 From the remaining 8 families, before validation of the variants we found a total of 156 variants, ranging between 11 and 28 per family with a mean of 19.50 (SD 4.34) per family.  A total of 39 were predicted to damage protein structure by SIFT, ranging from 1 to 12 per family, with a mean of 4.88 (SD 2.63) per family.  PolyPhen2 predicted a total of 10 variants to possibly be damaging to protein function, ranging from 0 to 5 per family with a mean of 1.25 (SD 1.21) per family.  PolyPhen2 also predicted a total of 39 variants to probably be damaging to protein function, with a range of 1 to 8 per family and a mean of 4.88 (SD 1.54) per family (Table 3.5). .85  86  Chapter 4: Discussion and Recommendations  Current projections indicate that the first half of the 21st century will see a substantial global increase in prevalence for dementia in general, and AD specifically as it is the most common cause of dementia2.  While advancing age is the greatest risk factor for AD and other dementias, the fact remains that dementia is not a feature of normal aging.  The role which genetic modifiers play in increasing or mitigating this risk is key to understanding and addressing the pathology behind the genesis and progression of dementia.  4.1 Strengths and limitations Prior to this work the research potential of the UBCH-CARD family history data has been under exploited.  A majority of the work for this thesis involved operationalizing that data into a digital format, thus allowing case selection.  Successful implementation of the family history database constitutes completion of our first objective.  As hoped, the database supported completion of our second objective; namely, selection of thirteen families for inclusion in this research due to a diagnosis of AD in at least 3 individuals, with at least one family member with symptom onset at or after age 65.  Family selection took place in September 2012.  A further proof for the successful implementation of this database is that ongoing expansion of the family history database now reveals an additional 20 families that also meet criteria for inclusion in this study.  With the 23 available DNA samples from our thirteen families we used a targeted panel for the exonic portion of 177 genes associated with dementia and other neurodegenerative conditions to test our hypotheses.   87  Preliminary evidence supporting hypothesis 1 – that heritability of LOAD in multi-incident families is at times due to deterministic factors not previously associated with AD – was found in the results from families 12 and 13.  In family 12 we identified a mutation previously associated with PD and ET in a multi-incident family with LOAD.  Screening for this mutation in 281 additional AD cases did not ascertain more carriers.  A clearer diagnostic picture would help elucidate the finds in this family; to that end, neuropathology arrangements will be made for the affected members of family 12.  DNA samples from the remaining siblings in family 12 will also help trace segregation of disease with DNAJC13 p.N855S.  As the bank of research samples available at the UBCH-CARD increases, it may be advisable to re-screen the clinic population for DNAJC13 p.N855S.  In family 13 we identified two mutations.  DCTN1 has previously been associated with ALS, PS, PD, FTD, and distal motor neuropathy.  Mutations in LMNA cause recessive CMT2.  The clinical picture for this family remains incomplete.  Only time will clarify which members of the sibship will be affected by AD.  In the interim, screening of the clinic population for DCTN1 p.T147A and LMNA p.N459S may also help to clarify the finds for family 13.    The findings from family 1 and 2 provide preliminary support for hypothesis 2; namely, that genetic variants traditionally identified as causing familial cases of EOAD also play a role in families with multi-incident LOAD.  88  In families 1 we identified a mutation in PSEN1 and in family 2 we identified a mutation in PSEN2.  In family 1 we were unable to access DNA from the family members with autopsy confirmed LOAD in order to confirm PSEN1 carrier status.  In family 2, despite having very similar autopsy phenotypes, only one sibling was a carrier for PSEN2.  Again, screening the UBCH-CARD AD population for both these mutations will help clarify the, as yet uncertain, nature of pathogenicity in PSEN1 p.I437V and PSEN2 p.S130L.  The primary limitation of this research is access to DNA samples from our families of interest.  While our results all provide preliminary support for our stated hypotheses, additional investigation is needed in all cases to confirm the veracity of our findings.  That notwithstanding, this work serves as a proof in principle of the gene discovery potential from the combined family history data and DNA samples for which the UBCH-CARD is custodian.  4.2 Clinical recommendations Based on the findings of this research changes to the genetic counselling offered to individuals from multi-incident LOAD families may be warranted.  4.2.1 Recommendation 1: Extend diagnostic genetic testing to LOAD cases Our finds in families 1 and 2 justify offering diagnostic genetic testing to individuals with LOAD under the same circumstances as apply for EOAD cases.  That is: 1. genetic testing should be offered for PSEN1, PSEN2 and APP, 2. to individuals symptomatic for AD with a family history suggestive of an autosomal dominant pattern of inheritance, irrespective of age of onset. 89  4.2.2 Recommendation 2: Repeated testing in LOAD families with identified mutations While we maintain that genetic testing in multi-incident cases of LOAD is justified, evidence from families 2 and 4 indicate that more caution is required when interpreting LOAD family histories.  As AD becomes more common with advancing age, the instances of phenocopy in families carrying known mutations will inevitably increase.  This does not preclude recommendation 1; however, differentiating mutation carriers from phenocopy cases is more difficult in multi-incident LOAD, and will require diagnostic genetic testing.  4.3 Future research directions Use of the targeted gene panel is a cost-effective and time-saving first step for investigating patients with family histories suggestive of autosomal dominant inheritance.  We recommend continued use of this panel for new patients to the UBCH-CARD with multi-incident family histories who consent to research.  While the focus of this research has been on heritable forms of LOAD, this approach is recommended for all diagnosis types, provided the family history is suggestive of Mendelian inheritance.  Individuals with putative hits on the panel should be re-contacted by a research assistant and invited to participate in family-based research.  Additional samples from the family should be sought, for both affected and unaffected individuals.  One of the limitations of our research has been sparse access to DNA, which has precluded our ability to assess whether the mutations segregate with disease thus confirming pathogenicity.  This problem is greatly ameliorated 90  among recent patients to the UBCH-CARD.  Not only is re-contact more feasible within a nearer timeframe to the patients first attending the UBCH-CARD, but recent attendees to the clinic are also more likely to have living family members able to participate in research.  We recommend adoption of a research enrollment framework, according to figure 4.1. 91  Figure 4.1.  Decision tree for future gene discovery at the UBCH-CARD  New patient  (with research consent) Suggestive of Mendelian inheritance Yes Screen genes implicated in neurological diseases using a targeted gene panel Putative hit Yes New association between mutation and disease phenotype No Report findings Yes Able to participate in family research Yes Additional sequencing research No No No Include DNA on  new mutation screening panel (segregated by diagnosis type) 92  4.4 Conclusion This thesis makes important contributions to the understanding of the genetic factors involved in LOAD.  This research provides a useful framework for ongoing gene discovery in LOAD utilizing the family history database and population-based DNA samples available at the UBCH-CARD.  93  Bibliography 1. McKhann, G. et al. Clinical diagnosis of Alzheimer’s disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s Disease. 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