The contribution of alternative splicing to genetic risk for psychiatric disorders
Bibliographic record
Abstract
A genetic contribution to psychiatric disorders has clearly been established and genome-wide association studies now provide the location of risk genes and genetic variants associated with risk. However, the mechanism by which these genes and variants contribute to psychiatric disorders is mostly undetermined. This is in part because non-synonymous protein coding changes cannot explain the majority of variants associated with complex genetic traits. Based on this, it is predicted that these variants are causing gene expression changes, including changes to alternative splicing. Genetic changes influencing alternative splicing have been identified as risk factors in Mendelian disorders; however, currently there is a paucity of research on the role of alternative splicing in complex traits. This stems partly from the difficulty of predicting the role of genetic variation in splicing. Alterations to canonical splice site sequences, nucleotides adjacent to splice junctions, and exonic and intronic splicing regulatory sequences can influence splice site choice. Recent studies have identified global changes in alternatively spliced transcripts in brain tissues, some of which correlate with altered levels of splicing trans factors. Disease-associated variants have also been found to affect cis-acting splicing regulatory sequences and alter the ratio of alternatively spliced transcripts. These findings are reviewed here, as well as the current datasets and resources available to study alternative splicing in psychiatric disorders. Identifying and understanding risk variants that cause alternative splicing is critical to understanding the mechanisms of risk as well as to pave the way for new therapeutic options.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".