Commentary: What is the case for candidate gene approaches in the era of high‐throughput genomics? A response to Border and Keller (2017)
Bibliographic record
Abstract
Border and Keller argue that candidate gene approaches are outdated and out-of-touch with the current understanding of the genetic architecture of complex behavioral traits and should be abandoned in favor of unbiased, genome-wide approaches. Border and Keller further suggest that a candidate gene should not be selected for in-depth investigation unless identified by a well-powered genome-wide association study (GWAS). An alternative perspective is offered that candidate approaches can be sensible for developmental and deep-phenotyping studies aimed at elucidating particular biological pathways responsible for the emergence of psychological phenotypes, and that candidates should not necessarily be expected to be confirmed by, or solely selected based on, GWAS. Both candidate and whole genome strategies have limitations, and each approach is useful and valid in the quest to identify the elusive genetic architecture of complex behavioral phenotypes.
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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 | 0.011 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.065 | 0.091 |
| Insufficient payload (model declined to judge) | 0.008 | 0.010 |
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".