Association study of 182 candidate genes in anorexia nervosa
Bibliographic record
Abstract
We performed association studies with 5,151 SNPs that were judged as likely candidate genetic variations conferring susceptibility to anorexia nervosa (AN) based on location under reported linkage peaks, previous results in the literature (182 candidate genes), brain expression, biological plausibility, and estrogen responsivity. We employed a case-control design that tested each SNP individually as well as haplotypes derived from these SNPs in 1,085 case individuals with AN diagnoses and 677 control individuals. We also performed separate association analyses using three increasingly restrictive case definitions for AN: all individuals with any subtype of AN (All AN: n = 1,085); individuals with AN with no binge eating behavior (AN with No Binge Eating: n = 687); and individuals with the restricting subtype of AN (Restricting AN: n = 421). After accounting for multiple comparisons, there were no statistically significant associations for any individual SNP or haplotype block with any definition of illness. These results underscore the importance of large samples to yield appropriate power to detect genotypic differences in individuals with AN and also motivate complementary approaches involving Genome-Wide Association (GWA) studies, Copy Number Variation (CNV) analyses, sequencing-based rare variant discovery assays, and pathway-based analysis in order to make up for deficiencies in traditional candidate gene approaches to AN.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".