Informative phenotypes for genetic studies of psychiatric disorders
Bibliographic record
Abstract
Despite its initial promise, there has been both progress and some set backs in genetic studies of the major psychiatric disorders of childhood and adulthood. Finding true susceptibility genes may be delayed because the most genetically informative phenotypes are not being used on a regular basis in linkage analysis and association studies. It is highly likely that using alternative phenotypes instead of DSM diagnostic categories will lead more rapid success in the search for these susceptibility genes. The objective of this paper is to describe the different types of informative phenotypes that can be employed in psychiatric genetic studies, to clarify their uses, to identify several methodologic issues the design and conduct of linkage and association studies that use alternative phenotypes and finally to suggest possible solutions to those difficulties. This is a conceptual review with a focus on methodological issues that may arise in psychiatric genetics and examples are taken from the literature on autism, schizophrenia, bipolar disorder, and alcoholism.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".