Partitioning the Pleiotropy Between Coronary Artery Disease and Body Mass Index Reveals the Importance of Low Frequency Variants and Central Nervous System–Specific Functional Elements
Bibliographic record
Abstract
Background: The objective of this study is to investigate the extent and nature of pleiotropy between coronary artery disease (CAD) and body mass index (BMI). Methods: We examined the contribution of genome-wide single-nucleotide polymorphisms (minor allele frequency ≥0.01) to co-occurrence of CAD and BMI in a sample of genetically unrelated 8041 subjects (genetic resemblance ≤0.025) of European ancestry using mixed-linear-models. We further partitioned the estimated pleiotropy according to biological features to gain insight into the nature of pleiotropy between CAD and BMI. Results: We found significant ( P <0.0001) positive genetic correlation between CAD and BMI ( r g =0.60). The estimated pleiotropy explained 68% of phenotypic correlation, and it was not proportionally distributed across the chromosomes; notably, chromosome 10 contributed more; whereas, chromosomes 11 and 14 contributed less to pleiotropy than expected given their chromosomal length. We noted that a large proportion (63%; P =0.002) of the pleiotropy is attributed to single-nucleotide polymorphisms with low allele frequency (minor allele frequency <0.05). Of note, pleiotropy was enriched among central nervous system genes and genes of metabolic pathways. Further analyses revealed that these effects are more pronounced in the proopiomelanocortin pathway and genes involved in carbohydrate metabolism. After genome-wide association study meta-analysis, only single-nucleotide polymorphisms downstream of the MC4R gene were found concordantly associated with ( P <5×10 –8 ) BMI and CAD with lead single-nucleotide polymorphism being rs663129 (combined P =2.7×10 –65 ). Finally, partitioning the pleiotropy according to functional elements pointed to the importance of superenhancers and notably brain-specific superenhancers. Conclusions: Genome-wide pleiotropy substantially contributes to co-occurrence of CAD and obesity, and it is highly enriched among low frequency variants and central nervous system–specific functional elements.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".