Estimating the Contributions of Rare and Common Genetic Variations and Clinical Measures to a Model Trait: Adiponectin
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".