Comprehensive identification of pleiotropic loci for body fat distribution using the NHGRI‐EBI Catalog of published genome‐wide association studies
Bibliographic record
Abstract
Summary We conducted a hypothesis‐free cross‐trait analysis for waist‐to‐hip ratio adjusted for body mass index (WHRadjBMI) loci derived through genome‐wide association studies (GWAS). Summary statistics from published GWAS were used to capture all WHRadjBMI single‐nucleotide polymorphisms (SNPs), and their proxy SNPs were identified. These SNPs were used to extract cross‐trait associations between WHRadjBMI SNPs and other traits through the NHGRI‐EBI GWAS Catalog. Pathway analysis was conducted for pleiotropic WHRadjBMI SNPs. We found 160 WHRadjBMI SNPs and 3675 proxy SNPs. Cross‐trait analysis identified 239 associations, of which 100 were for obesity traits. The remaining 139 associations were filtered down to 101 unique linkage disequilibrium block associations, which were grouped into 13 categories: lipids, red blood cell traits, white blood cell counts, inflammatory markers and autoimmune diseases, type 2 diabetes‐related traits, adiponectin, cancers, blood pressure, height, neuropsychiatric disorders, electrocardiography changes, urea measurement, and others. The highest number of cross‐trait associations were found for triglycerides (n = 10), high‐density lipoprotein cholesterol (n = 9), and reticulocyte counts (n = 8). Pathway analysis for WHRadjBMI pleiotropic SNPs found immune function pathways as the top canonical pathways. Results from our original methodology indicate a novel genetic association between WHRadjBMI and reticulocyte counts and highlight the pleiotropy between abdominal obesity, immune pathways, and other traits.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".