Influence of Polygenic Risk on Height and BMI in Adults With a 22q11.2 Microdeletion
Bibliographic record
Abstract
Abstract Context Elevated a priori risk may enhance the likelihood that common variant effects, captured collectively in a polygenic risk score (PRS), approach clinical utility. Objective In this study, we investigated the modifying effect of PRSs for adult height and body mass index (BMI) in individuals with elevated baseline risk for short stature (<3rd percentile height) and obesity (BMI ≥30) conferred by a 22q11.2 microdeletion. Methods We tested height-PRS and BMI-PRS for association with their respective phenotypes in 259 adults of European ancestry with a 22q11.2 microdeletion using sequencing data and multivariable linear regression models to account for clinical/demographic variables. Results In multivariable linear regression models, height-PRS and BMI-PRS explained 25.8% and 5.7% of the variance in their respective traits (P < .001 for both). When applying the height-PRS to stratify risk for short stature, 42.3% of individuals in the lowest PRS quintile had short stature (vs 5.9% in the highest PRS quintile, odds ratio = 11.46, P = 1.74E-05). Using logistic regression models to predict short stature in a receiver operating characteristic curve analysis, a model combining height-PRS and clinical/demographic covariates achieved an area under the curve of 0.78, performing significantly better than a covariate-only model. Conclusion The results demonstrate that adult height and BMI can be influenced by the effects of genome-wide common variants in the presence of a rare variant conferring elevated a priori risk. Height-PRS may help refine growth expectations in individuals with 22q11.2 microdeletion.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".