Estimating the Contributions of Rare and Common Genetic Variations and Clinical Measures to a Model Trait: Adiponectin
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Bibliographic record
Abstract
Common genetic variation frequently accounts for only a modest amount of interindividual variation in quantitative traits and complex disease susceptibility. Circulating adiponectin, an adipocytokine implicated in metabolic disease, is a model for assessing the contribution of genetic and clinical factors to quantitative trait variation. The adiponectin locus, ADIPOQ, is the primary source of genetically mediated variation in plasma adiponectin levels. This study sought to define the genetic architecture of ADIPOQ in the comprehensively phenotyped Hispanic (n = 1,151) and African American (n = 574) participants from the Insulin Resistance Atherosclerosis Family Study (IRASFS). Through resequencing and bioinformatic analysis, rare/low frequency (<5% MAF) and common variants (>5% MAF) in ADIPOQ were identified. Genetic variants and clinical variables were assessed for association with adiponectin levels and contribution to adiponectin variance in the Hispanic and African American cohorts. Clinical traits accounted for the greatest proportion of variance (POV) at 31% (P = 1.16 × 10-(47)) and 47% (P = 5.82 × 10-(20)), respectively. Rare/low frequency variants contributed more than common variants to variance in Hispanics: POV = 18% (P = 6.40 × 10-(15)) and POV = 5% (P = 0.19), respectively. In African Americans, rare/low frequency and common variants both contributed approximately equally to variance: POV = 6% (P = 5.44 × 10-(12)) and POV = 9% (P = 1.44 × 10-(10)), respectively. Importantly, single low frequency alleles in each ethnic group were as important as, or more important than, common variants in explaining variation in adiponectin. Cumulatively, these clinical and ethnicity-specific genetic contributors explained half or more of the variance in Hispanic and African Americans and provide new insight into the sources of variation for this important adipocytokine.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it