The impact of rare variants on polygenic risk and transcriptomic dysregulation in amyotrophic lateral sclerosis
Bibliographic record
Abstract
Amyotrophic lateral sclerosis (ALS) is a rapidly progressive neurodegenerative disease, currently without treatment options or effective long-term clinical interventions.Beginning with symptoms of muscle weakness and spasticity, the disease can progress to near total loss of innervation to muscles and ultimately to paralysis and loss of life.ALS is a rare disease, in that approximately 3 in 100,000 individuals will be diagnosed per year.However, the lifetime risk to for an individual to develop ALS is 1/300, suggesting a substantial impact at the individual, healthcare, and societal levels.Historically, the study of ALS genetics has focused on monogenic, inherited variants.However, because age of symptom onset in ALS is variable, and because some variants do not result in disease in all carriers, it has been hypothesized that additional factors are required.Oligogenic and polygenic inheritance are schema of separate, concurrently inherited genetic variants to cause disease; one variant might not be sufficient, but the combined effects of two or more variants in an individual could result in a phenotype.While these inheritance patterns have been suggested for ALS, evidence for their necessity is not conclusive. Many of the genetic variants observed in genes related to ALS impact RNA regulation pathways.Gene expression can be impacted directly through allele-specific transcription, or indirectly through downstream effects of variant-containing RNA.One way that RNA is regulated in a cell is through the addition of chemical modifications to the nucleotides following transcription.The most common post-transcriptional modification is N6-methyladenosine (m 6 A), which can affect RNA turnover, splicing, localization, and binding of regulatory proteins to target RNA.The proportion of RNA containing an m 6 A modification has substantial downstream effects on the Contribution to Original KnowledgeThis thesis contains the following novel contributions to scientific knowledge: Chapter 2 is a survey of rare variants in the Québec ALS and control populations, stratified by C9orf72 expansion carrier status.Several novel variants are described that may be unique to the French-Canadian or French cohorts.The main finding of this short manuscript is that variants secondary to the C9orf72 expansion are predicted to be less severe than variants in unaffected controls or in ALS cases without the expansion.This study provides evidence that oligogenic inheritance in ALS is rare, and likely due to variant frequency rather than necessity to explain genetic risk.Chapter 3 is a survey of rare variants across a larger and non-ascertained French-Canadian ALS case-control cohort, with the addition of common variant genotyping to assess polygenic contribution to disease risk.Because these samples were not part of the GWAS from which the summary statistics were created, it was possible to generate polygenic risk scores without statistical bias and overfitting.We found that polygenic risk was highest in ALS cases without a variant in ALS-associated genes, which both better defined the utility of polygenic risk scores in ALS as well as confirmed that rare variants are likely sufficient for disease risk.Chapter 4 is the first examination of the m 6 A epitranscriptome in the context of C9orf72 hexanucleotide expansion.Our use of post-mortem case and control cerebellums, as well as patient and control-derived stem cell differentiated motor neurons and astrocytes was able to show that
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.003 | 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".