Oligogenic structure of amyotrophic lateral sclerosis has genetic testing, counselling and therapeutic implications
Bibliographic record
Abstract
BACKGROUND: Despite several studies suggesting a potential oligogenic risk model in amyotrophic lateral sclerosis (ALS), case-control statistical evidence implicating oligogenicity with disease risk or clinical outcomes is limited. Considering its direct clinical and therapeutic implications, we aim to perform a large-scale robust investigation of oligogenicity in ALS risk and in the disease clinical course. METHODS: We leveraged Project MinE genome sequencing datasets (6711 cases and 2391 controls) to identify associations between oligogenicity in known ALS genes and disease risk, as well as clinical outcomes. RESULTS: In both the discovery and replication cohorts, we observed that the risk imparted from carrying multiple ALS rare variants was significantly greater than the risk associated with carrying only a single rare variant, both in the presence and absence of variants in the most well-established ALS genes. However, in contrast to risk, the relationships between oligogenicity and ALS clinical outcomes, such as age of onset and survival, did not follow the same pattern. CONCLUSIONS: Our findings represent the first large-scale, case-control assessment of oligogenicity in ALS and show that oligogenic events involving known ALS risk genes are relevant for disease risk in ~6% of ALS but not necessarily for disease onset and survival. This must be considered in genetic counselling and testing by ensuring to use comprehensive gene panels even when a pathogenic variant has already been identified. Moreover, in the age of stratified medication and gene therapy, it supports the need for a complete genetic profile for the correct choice of therapy in all ALS patients.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".