<i>SGCG</i> rs679482 Associates With Weight Loss Success in Response to an Intensively Supervised Outpatient Program
Bibliographic record
Abstract
Weight loss in response to energy restriction is highly variable, and identification of genetic contributors can provide insights into underlying biology. Leveraging 1000 Genomes imputed genotypes, we carried out genome-wide association study (GWAS) analysis in 551 unrelated obese subjects of European ancestry who participated in an intensively supervised weight loss program with replication of promising signals in an independent sample of 1,331 obese subjects who completed the program at a later date. By single nucleotide polymorphism–based and sib-pair analysis, we show that that weight loss is a heritable trait, with estimated heritability (h2 = 0.49) within the range reported for obesity. We find rs679482, intronic to SGCG (sarcoglycan γ), highly expressed in skeletal muscle, to concordantly associate with weight loss in discovery and replication samples reaching GWAS significance in the combined meta-analysis (β = −0.35, P = 1.7 × 10−12). Located in a region of open chromatin, rs679482 is predicted to bind DMRT2, and allele-specific transcription factor binding analysis indicates preferential binding of DMRT2 to rs679482-A. Concordantly, rs679482-A impairs native repressor activity and increases basal and DMRT2-mediated enhancer activity. These findings confirm that weight loss is a heritable trait and provide evidence by which a novel variant in SGCG, rs679482, leads to impaired diet response.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".