A Candidate-Gene Approach Identifies Novel Associations Between Common Variants in/Near Syndromic Obesity Genes and BMI in Pediatric and Adult European Populations
Bibliographic record
Abstract
We hypothesized that monogenic syndromic obesity genes are also involved in the polygenic variation of BMI. Single-marker, tag single nucleotide polymorphism (tagSNP) and gene-based analysis were performed on common variants near 54 syndromic obesity genes. We used publicly available data from meta-analyses of European BMI genome-wide association studies conducted by the Genetic Investigation of ANthropometric Traits (GIANT) Consortium and the UK Biobank (UKB) (N = 681,275 adults). A total of 33 loci were identified, of which 19 of 33 (57.6%) were located at SNPs previously identified by the GIANT Consortium and UKB meta-analysis, 11 of 33 (33.3%) were located at novel SNPs, and 3 of 33 (9.1%) were novel genes identified with gene-based analysis. Both single-marker and tagSNP analyses mapped the previously identified 19 SNPs by the GIANT Consortium and UKB meta-analysis. Gene-based analysis confirmed 15 of 19 (78.9%) of the novel SNPs’ associated genes. Of the 11 novel loci, 8 were identified with single-marker analysis and the remaining 3 were identified with tagSNP analysis. Gene-based analysis confirmed 4 of 11 (36.3%) of these loci. Meta-analysis with the Early Growth Genetics (EGG) Consortium (N = 35,668 children) was conducted post hoc for top SNPs, confirming 17 of 33 (51.5%) loci, of which 5 were novel. This study supports evidence for a continuum between rare monogenic syndromic and common polygenic forms of obesity.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".