The Genetic Landscape of Neurodegenerative and Cerebrovascular Disease Phenotypes.
Bibliographic record
Abstract
Neurodegenerative diseases are progressive, incurable conditions characterized by neuronal degeneration and protein aggregation, resulting in cognitive decline and/or motor dysfunction. Over half a million Canadians are affected with these diseases, and the number of cases is expected to rise as the aging population grows and average lifespans continue to increase. There are currently no curative treatments, and only few therapeutics are available to target disease symptoms or slow disease progression. Further, diagnosis can be challenging, relying on clinical features that are often highly heterogeneous between patients. Gaining a greater understanding of the full spectrum of genetic factors contributing to these diseases may offer the opportunity to more accurately assess risk of disease development, improve diagnosis, and identify potential therapeutic targets. By leveraging the unique study design of the Ontario Neurodegenerative Disease Research Initiative (ONDRI) — a multi-platform study characterizing neurodegenerative diseases and cerebrovascular disease (CVD) — I have made significant progress in the elucidation of overlapping genetic determinants across neurodegenerative diagnoses. Using a targeted next-generation sequencing (NGS) approach, I comprehensively genetically characterized the ONDRI cohort (n = 519), including participants diagnosed with: 1) Alzheimer’s disease (AD); 2) amyotrophic lateral sclerosis (ALS); 3) frontotemporal dementia (FTD); 4) mild cognitive impairment (MCI); 5) Parkinson’s disease (PD); and 6) CVD. I identified associations between common genotypes or haplotypes of high phenotypic effect and neurodegenerative disease presentation and features. I also assessed novel gene-disease relationships and the potential genetic overlap between neurodegenerative and cerebrovascular diagnoses through the assessment of rare genetic variation captured by the targeted NGS panel and gold-standard Sanger sequencing methods. Finally, I identified a novel association between Notch receptor 3 (NOTCH3) rare variants and CVD burden in participants with PD. The work presented throughout this Dissertation highlights the complexity of neurodegenerative disease genetic risk factors by demonstrating a large amount of overlap between specific diagnoses. The findings contribute to the longstanding effort to fully understand the genetic architecture of neurodegenerative diseases and improve therapeutic development, diagnostic tools, and progression prediction.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".