Contribution of rare variant associations to neurodegenerative disease presentation
Bibliographic record
Abstract
Genetic factors contribute to neurodegenerative diseases, with high heritability estimates across diagnoses; however, a large portion of the genetic influence remains poorly understood. Many previous studies have attempted to fill the gaps by performing linkage analyses and association studies in individual disease cohorts, but have failed to consider the clinical and pathological overlap observed across neurodegenerative diseases and the potential for genetic overlap between the phenotypes. Here, we leveraged rare variant association analyses (RVAAs) to elucidate the genetic overlap among multiple neurodegenerative diagnoses, including Alzheimer's disease, amyotrophic lateral sclerosis, frontotemporal dementia (FTD), mild cognitive impairment, and Parkinson's disease (PD), as well as cerebrovascular disease, using the data generated with a custom-designed neurodegenerative disease gene panel in the Ontario Neurodegenerative Disease Research Initiative (ONDRI). As expected, only ~3% of ONDRI participants harboured a monogenic variant likely driving their disease presentation. Yet, when genes were binned based on previous disease associations, we observed an enrichment of putative loss of function variants in PD genes across all ONDRI cohorts. Further, individual gene-based RVAA identified significant enrichment of rare, nonsynonymous variants in PARK2 in the FTD cohort, and in NOTCH3 in the PD cohort. The results indicate that there may be greater heterogeneity in the genetic factors contributing to neurodegeneration than previously appreciated. Although the mechanisms by which these genes contribute to disease presentation must be further explored, we hypothesize they may be a result of rare variants of moderate phenotypic effect contributing to overlapping pathology and clinical features observed across neurodegenerative diagnoses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".