Multi-Trait Analysis of GWAS and Biological Insights to Cognition: A response to Hill (2017)
Bibliographic record
Abstract
Hill (2017) presented a critique of our recently published paper in Cell Reports entitled “Large-Scale Cognitive GWAS Meta-Analysis Reveals Tissue-Specific Neural Expression and Potential Nootropic Drug Targets” (Lam et al. 2017). Specifically, Hill offered several inter-related comments suggesting potential problems with our use of a new analytic method called Multi-Trait Analysis of GWAS (MTAG; Turley et al. 2017). In this brief paper, we respond to each of these concerns. Using empirical data, we conclude that our MTAG results do not suffer from “inflation of the false discovery rate”, as suggested by Hill (2017), and are not “more relevant to the genetic contributions to education than they are to the genetic contributions to intelligence.”
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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 | 0.158 | 0.374 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.025 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.021 | 0.066 |
| 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".