The oligogenic structure of amyotrophic lateral sclerosis has genetic testing, counselling, and therapeutic implications
Bibliographic record
Abstract
Abstract Recently, large-scale case-control analyses have been prioritized in the study of ALS. Yet the same effort has not been put forward to investigate additive moderate phenotypic effects of genetic variants in genes driving ALS risk, despite case-level evidence suggesting a potential oligogenic risk model. Considering its direct clinical and therapeutic implications, a large-scale robust investigation of oligogenicity in ALS is greatly needed. Here, we leveraged the Project MinE ALS Sequencing Consortium genome sequencing datasets of individuals with ALS (n = 6711) and controls (n = 2391) to identify signals of association between oligogenicity in known ALS genes (n=26) and disease risk, as well as clinical outcomes. Applying regression models to a discovery and replication cohort, we observed that the risk imparted from carrying rare variants in multiple known ALS genes was significant and was 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, such as C9orf72 . However, in contrast to risk, the relationships between oligogenicity and ALS clinical outcomes, such as age of onset and survival, might not follow the same pattern as we did not observe any associations. Our findings represent the first large-scale, case-control assessment of oligogenic associations in ALS to date and confirm that oligogenic events involving known ALS risk genes are indeed relevant for the risk of disease in approximately 6% of ALS but not necessarily for disease onset and survival. This must be considered in genetic counselling and testing by ensuring the use of comprehensive gene panels even when a potential pathogenic variant has already been identified. Moreover, in the age of stratified medication and gene therapy, it supports the need of 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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| 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".