Rare Variants in Complex Traits: Novel Identification Strategies and the Role of de novo Mutations
Bibliographic record
Abstract
Following the limited success of linkage and association studies aimed at identifying the genetic causes of common neurodevelopmental syndromes like autism and schizophrenia, complex traits such as these have recently been considered under the 'common disease-rare variant' hypothesis. Prior to this hypothesis, the study of candidate genes has enabled the discovery of rare variants in complex disorders, and in turn some of these variants have highlighted the genetic contribution of de novo variants. De novo variants belong to a subcategory of spontaneous rare variants that are largely associated with sporadic diseases, which include some complex psychiatric disorders where the affected individuals do not transmit the genetic defects they carry because of their reduced reproductive fitness. Interestingly, recent studies have demonstrated the rate of germline de novo mutations to be higher in individuals with complex psychiatric disorders by comparison to what is seen in unaffected control individuals; moreover, de novo mutations carried by affected individuals have generally been more deleterious than those observed in control individuals. Advanced sequencing technologies have recently enabled the undertaking of massive parallel sequencing projects that can cover the entire coding sequences (exome) or genome of several individuals at once. Such advances have thus fostered the emergence of novel genetic hypotheses and ideas to investigate disease-causative genetic variations. The genetic underpinnings of a number of sporadic complex diseases is now becoming partly explained and more major breakthroughs for complex traits genomics should be expected in the near future.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".