Genome‐wide Association Study and Follow‐up Analysis of Adiposity Traits in Hispanic Americans: The IRAS Family Study
Bibliographic record
Abstract
We investigated candidate genomic regions associated with computed tomography (CT)-derived measures of adiposity in Hispanics from the Insulin Resistance Atherosclerosis Study Family Study (IRASFS). In 1,190 Hispanic individuals from 92 families 3 from the San Luis Valley, Colorado and San Antonio, Texas, we measured CT-derived visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), and visceral:subcutaneous ratio (VSR). A genome-wide association study (GWAS) was completed using the Illumina HumanHap 300 BeadChip (approximately 317K single-nucleotide polymorphisms (SNPs)) in 229 individuals from the San Antonio site (stage 1). In total, 297 SNPs with evidence for association with VAT, SAT, or VSR, adjusting for age and sex (P<0.001), were genotyped in the remaining 961 Hispanic samples. The entire Hispanic cohort (n=1,190) was then tested for association, adjusting for age, sex, site of recruitment, and admixture estimates (stage 2). In stage 3, additional SNPs were genotyped in four genic regions showing evidence of association in stage 2. Several SNPs were associated in the GWAS (P<1x10(-5)) and were confirmed to be significantly associated in the entire Hispanic cohort (P<0.01), including: rs7543757 for VAT, rs4754373 and rs11212913 for SAT, and rs4541696 and rs4134351 for VSR. Numerous SNPs were associated with multiple adiposity phenotypes. Targeted analysis of four genes whose SNPs were significant in stage 2 suggests candidate genes for influencing the distribution (RGS6) and amount of adiposity (NGEF). Several candidate loci, including RGS6 and NGEF, are associated with CT-derived adipose fat measures in Hispanic Americans in a three-stage genetic association study.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".